DELIVERABLE D2.2.1 Signal processing techniques: … · Project SELECT - Smart Efficient ... ENP-ED...

Project SELECT - Smart Efficient Location, idEntification and Cooperation Techniques Project- No 257544 Work Package Identification, location and modelling WP – No WP2 Document Deliverable D2.2.1 SaveDate 05/09/2011

D2.2.1_Signal_processing_techniques_interim_report_1.3_CNIT Dissemination Level: Public Page 1/142

THEME ICT

ICT 2009.3.9 Microsystems and Smart Miniaturised Systems

ProgrammeTitle Collaborative project / Small or medium-scale focused research projects

Project Title

Smart Efficient Location, idEntification and Cooperation Techniques Acronym

SELECT Project No

257544

DELIVERABLE D2.2.1

Signal processing techniques: Interim report

WorkPackage2 Leading Partner: CNIT

Document Editor: Davide Dardari (CNIT)

Dissemination Level: PU - Public

Delivery date: 31/8/2011

Version 1.3



Contributors

Partner Contributing authors

DATALOGIC

Fraunhofer

CEA F. Dehmas, V. Heiries, R. D’Errico

CNIT D. Dardari, A. Conti, N. Decarli, S. Bartoletti, V. Casadei, M. Guerra, A. Mariani

CEIT

ARMINES A. Sibille, F. Guidi

NOV

ORIA

Versioning and contribution history

Version Description Contributing authors Date

0.1 TOC D. Dardari 8/3/2011 0.2 Sections

1,2,5,6.1 S. Bartoletti, V. Casadei, A. Conti, R. D’Errico, D. Dardari, N. Decarli, F. Dehmas, M. Guerra, F. Guidi, V. Heiries, A. Mariani

9/5/2011

0.3 Editing D. Dardari – A. Conti – N. Decarli 17/5/2011 0.4 Editing, section

6.2 N. Decarli, V. Heiries 25/6/2011

1.0 Editing D. Dardari 1/7/2011 Internal review M. Bottazzi, G. Micheletti, D. Wisland 1.1 Review A. Conti, D. Dardari 29/8/2011 1.2 Final Review A. Conti, V. Heiries 31/8/2011 1.3 Final Re-format M.Bottazzi 31/8/2011



LIST OF ACRONYMS

Acronym Name

ADC Analog to Digital Converter

AWGN Addictive White Gaussian Noise

BAV Balanced Antipodal Vivaldi

BEP Bit Error Probability

BER Bit Error Rate

BWA Broadband Wireless Access

CAF Cyclic Autocorrelation Function

CIR Channel Impulse Response

CM Channel Model

CP Cyclic Prefix

CPAPR Cross-correlation Peak to Autocorrelation Peak Ratio

CPGPR Cross-correlation Peak to Grass Peak Ratio

CRLB Cramer-Rao Lower Bound

CSCG Circularly Symmetric Complex Gaussian

CW Continuous Wave

DAA Detect And Avoid

DAC Digital to Analog Converter

DFMS Monopole Dual Feed Stripline Antenna

ED Energy Detector

EFIM Equivalent Fisher Information Matrix

EIRP Equivalent Isotropically Radiated Power

EKF Extended Kalman Filter

EME Minimum Eigenvalue ratio detector

ENP-ED Estimated Noise Power Energy Detector

ERP Equivalent Radiated Power

FCC Federal Communications Commission

GPS Global Positioning System

IMF Ideal Matched Filter

IMU Inertial Measurement Unit

ITC Information Theoretic Criterion



LDC Low Duty Cycle

LEO Localization Error Outage

LLRT Log Likelihood Ratio Test

LOS Line Of Sight

LRT Likelihood Ratio Test

MF Matched Filter

MME Maximum-Minimum Eigenvalue ratio detector

MUI Multi-User Interference

NLOS Non Line Of Sight

OFDM Orthogonal Frequency Division Multiplexing

PAM Pulse Amplitude Modulation

PDF Probability Density Function

PF Particle Filter

PN Pseudo-Noise

PPM Pulse Position Modulation

PRF Pulse Repetition Frequency

PRP Pulse Repetition Period

PSD Power Spectral Density

PSK Phase Shift Keying

QAM Quadrature Amplitude Modulation

RCS Radar Cross Section

RF Radio Frequency

RFID Radio Frequency Identification

RII Ranging Information Intensity

RMS Root Mean Square

RMSE Root Mean Squared Error

RMU Range Measurement Unit

ROI Return On Investment

RTLS Real Time Location Systems

RV Random Variable

SAW Surface Acoustic Wave

SBS-MC Serial Backward Search for Multiple Cluster



SCM Supply Chain Management

SCR Signal-to-Clutter Ratio

SIR Signal-to-Interference Ratio

SIS Sequential Importance Sampling

SNR Signal-to-Noise Ratio

SOA State Of Art

SPEB Squared Position Error Bound

SPMF Single-Path Matched Filter

SQNR Signal-to-Quantization-Noise-Ratio

TDM Time Division Multiplexing

TDOA Time Difference Of Arrival

TOA Time-of-Arrival

UHF Ultra-High Frequency

UWB Ultra-Wideband Technology

VNA Vector Network Analyzer

WSN Wireless Sensor Network

WSR Wireless Sensor Radar



EXECUTIVE SUMMARY

The signal processing techniques interim report D2.2.1 presents results of the activity carried out in tasks T2.2, T2.3 and T2.4 related to, respectively, “Passive communication schemes based on signal backscattering”, “High-accuracy active and passive location and tracking algorithms” and “Coexistence and interference mitigation techniques”. It provides the preliminary system architecture design guidelines, analysis, and results.

First, the analysis of potential candidate technologies able to fit the SELECT project requirements is presented.

Different aspects related to signal format definition, tag and reader signal processing schemes are addressed with the purpose to achieve reliable tag-reader communication and tag tracking accuracy. Through the report, the constraints on system design derived from application requirements (work package WP1), propagation effects (task 2.1), spectrum usage regulatory limitations (task 2.4) and technological issues (work packages WP3/WP4) are accounted for.

The last part of the document discusses the main critical issues and next activity directions.

This report provides the inputs for system architecture design (task T2.5), tag design (work package WP3) and reader design (work package WP4).



INTRODUCTION: THE SELECT PROJECT

The SELECT project focuses on studying innovative solutions enabling high-accuracy detection, identification, and location of objects/persons equipped with small ultra-low power tags using a network of intelligent self-configuring radio devices. Network functionalities will be enhanced to include the detection and tracking of moving objects/persons without tags eventually present in the same area.

To achieve this goal, several technologies such as radio frequency identification (RFID), ultra wideband backscattering modulation, time reversal, relaying, and associated advanced algorithms, will be considered and partly or totally integrated in a demonstrator. This will require the design of multi-frequency/multi-technology tags for system-neutral identification along the use lifetime of a tag, based on advanced concepts in low-consumption chip and antenna design.

Innovative techniques will be considered to improve the location accuracy, increase tag energy efficiency and extend system coverage by a mixture of progress in the system architecture, in the detection and tag activation techniques, and in the complexity-performance trade off of chip design.

Special emphasis will be given to the analysis and design of “green” solutions by considering low complexity and low power tags through the exploitation of passive communication (without integrated tag batteries) as well smart cooperation strategies.

Finally, single system components and the overall system performance will be validated through experimental characterization, hardware implementation, as well as simulation.

Identification/detection reliability, tracking accuracy, power consumption will be amongst the major evaluation criteria.

A wireless network integrating detection, identification, and location would lead to relevant improvements in the development of a wide range of advanced applications including logistics (package tracking) and supply chain management (SCM)



Table of contents LIST OF ACRONYMS .......................................................................................................................................... 3

EXECUTIVE SUMMARY ..................................................................................................................................... 6

INTRODUCTION: THE SELECT PROJECT ............................................................................................................. 7

CONTENT........................................................................................................................................................ 12

1. Candidate Backscattering Communication Techniques ......................................................................... 13

1.1 Backscatter Modulation ............................................................................................................... 13

1.1.1 Backscatter modulation basics and SOA solutions ................................................................. 13

1.1.2 Conclusion ............................................................................................................................. 15

1.2 Chipless Tags ................................................................................................................................ 16

1.2.1 Inkjet printable fully passive RFID tag ................................................................................... 16

1.2.2 Multiresonator ...................................................................................................................... 17

1.2.3 SAW devices .......................................................................................................................... 18

1.3 Active Modulation ....................................................................................................................... 19

1.4 Comparison and Conclusion ......................................................................................................... 21

2 Definition of Reader and Tag Architectures ................................................................................. 23

2.1 Reader Architecture ..................................................................................................................... 23

2.1.1 Functional blocks – Transmitter Section ................................................................................ 23

2.1.2 Transmitted signal format ..................................................................................................... 24

2.2 Tag Architecture .......................................................................................................................... 26

2.2.1 Tag Functional blocks ............................................................................................................ 27

2.2.2 Backscattered signal format ................................................................................................. 29

3 Signal Processing Techniques for Communication ........................................................................ 31

3.1 Reader receiver section................................................................................................................ 31

3.2 Signal parameters design criteria ................................................................................................. 34

3.3 Spreading codes design criteria .................................................................................................... 35

3.3.1 Clutter removal ..................................................................................................................... 36

3.3.2 Multiple access ...................................................................................................................... 37

3.3.3 Codes assignment strategies ................................................................................................. 37

3.3.4 Analysis of signal backscattered pulse quantization .............................................................. 40

3.3.5 Analysis of tag clock drift ...................................................................................................... 43

3.4 Performance Analysis in the Reference Scenario ......................................................................... 45

3.4.1 Preliminary measurements analysis in anechoic chamber ..................................................... 45

3.4.2 Link budget ........................................................................................................................... 47

3.4.3 Data Rate and Bit Error Rate analysis ................................................................................... 53

3.4.4 UHF link budget for wake-up signals ..................................................................................... 59

3.5 Preliminary Performance Analysis in Real Environments ............................................................. 60



3.5.1 Measurements set up ............................................................................................................ 60

3.5.2 Bit error probability analysis ................................................................................................. 62

3.5.2.1 Performance in AWGN and single-tag laboratory scenario ................................................... 62

3.5.2.2 Performance in multi-tag scenario with artificial clutter ....................................................... 64

3.5.2.3 Performance in multi-tag laboratory scenario ....................................................................... 65

3.6 Concluding remarks ..................................................................................................................... 66

4 Signal Processing Techniques for Ranging .................................................................................... 68

4.1 Theoretical bound on TOA estimation ......................................................................................... 68

4.2 Signal generation and propagation model ................................................................................... 69

4.3 PHY layer considerations ............................................................................................................. 71

4.4 Receiver architecture ................................................................................................................... 72

4.5 Synchronization process .............................................................................................................. 73

4.6 TOA Estimation algorithms .......................................................................................................... 73

4.6.1 TOA coarse estimation .......................................................................................................... 74

4.6.2 TOA refined estimation ......................................................................................................... 75

4.7 Performance Analysis in the Reference Scenario ......................................................................... 76

4.7.1 Ranging error outage ............................................................................................................ 76

4.7.2 Ranging error standard deviation ......................................................................................... 78

4.7.3 Concluding remarks............................................................................................................... 79

5 Signal processing Techniques for Localization .............................................................................. 80

5.1 Preliminaries ................................................................................................................................ 80

5.2 Localization performance metrics ................................................................................................ 80

5.3 Theoretical Foundation ................................................................................................................ 81

5.4 Reference scenarios for localization and tracking ........................................................................ 83

5.5 Localization and Tracking techniques ........................................................................................... 85

5.5.1 TOA estimation model for localization and tracking algorithms ............................................ 85

5.5.2 Maximum Likelihood Localization ......................................................................................... 86

5.5.3 Tracking: Bayesian filters ...................................................................................................... 87

5.5.4 Tracking: Particle filters ........................................................................................................ 90

5.5.5 Tracking: Extended Kalman Filter .......................................................................................... 91

5.6 Detection and Localization of Untagged objects .......................................................................... 95

5.7 Localization Performance for static tagged and untagged objects .............................................. 100

5.8 Tracking Performance for dynamic tagged and untagged objects .............................................. 103

5.8.1 Particle Filter Tracking ........................................................................................................ 103

5.8.2 Extended Kalman Filter Tracking ......................................................................................... 109

5.8.3 Concluding remarks............................................................................................................. 113

6 Coexistence and Interference Mitigation Techniques ................................................................ 114



6.1 Preliminary analysis of spectrum sensing techniques................................................................. 114

6.1.1 Overview of the spectrum sensing algorithms ..................................................................... 114

6.1.2 Matched Filter Based Detection .......................................................................................... 115

6.1.3 Energy Detection ................................................................................................................. 116

6.1.4 Eigenvalue based detection ................................................................................................. 117

6.1.4.1 Computation of the sample autocovariance matrix ............................................................. 118

6.1.4.2 Energy – Minimum Eigenvalue detection ............................................................................. 119

6.1.4.3 Maximum – Minimum Eigenvalue detection ........................................................................ 119

6.1.4.4 ITC-based detection .............................................................................................................. 120

6.1.5 OFDM autocorrelation based Detection .............................................................................. 120

6.1.6 Cyclistationary-based detection .......................................................................................... 122

6.1.6.1 Decision statistics ................................................................................................................. 124


6.2 Preliminary analysis of the LDC constraints ................................................................................ 125


REFERENCES ................................................................................................................................................ 131

CONCLUSIONS AND FUTURE WORK ........................................................................................................... 136

CONCLUSIONS ......................................................................................................................................... 136

NEXT STEPS .............................................................................................................................................. 137

LITERATURE ................................................................................................................................................. 139





CONTENT

One of the main objectives of the SELECT project is the investigation and evaluation of new architectures based on a combination of readers and relays to significantly extend the operational detection range of the tags with high-accuracy tracking capabilities, while optimizing the complexity, energy efficiency, and cost. The easy integration with current UHF Gen2 tags is also an important aspect undertaken [6].

The requirements of a configurable SELECT system concept for identification, detection and location have been defined in work package WP1 [3], [2]. They pose several scientific and technological challenges which, in part, are analyzed in this report. For what the communication and tracking capabilities are regarded, the “centre of gravity” technology of the SELECT project is ultra-wide bandwidth (UWB). Its adoption leads to some advantages in terms of ranging/positioning accuracy, multi-tag capability, and interference rejection.

In this interim report, existing or under study UWB technologies and solutions for tag-reader communication and tracking are analyzed and compared in section 1 with reference to the requirements of the SELECT project [3], [2]. To reduce the complexity and energy consumption, the adoption of active devices is avoided in favor of semi-passive tags based on backscatter communication. They are characterized by extremely low energy consumption and hence they are capable of working for years with small green batteries or in conjunction with energy harvesting techniques [5].

Communication between tags and readers using backscatter signaling is addressed in section 2 through the definition of the tag/reader architecture as well as the signaling format. A discussion on parameters design criteria is provided and supported by some preliminary numerical results in section 3.

Section 4 addresses the issue of synchronization and ranging through the estimation of the time-of-arrival (TOA) of the response signal backscattered by the tag. Algorithms and results for localization and tracking in the SELECT reference scenario are reported in section 5. Finally coexistence and interference issues are treated in section 6 where preliminary analysis on spectrum sensing techniques and LDC constraints are presented.



1. Candidate Backscattering Communication Techniques

This section presents a state of the art survey of low-cost, low-energy consumption UWB tags of potential interest for the SELECT project.

Several UWB Real Time Locating System solutions already exist. They are supported by IEEE standards: namely IEEE 802.15.4a or IEEE 802.15.4f. For the moment, the proprietary solutions (Timedomain, Ubisense, Decawave, ZES, etc…) based on these standards are all designed for active tags.

Contrarily, passive or semi-passive RFID are particularly attractive due to their low cost. Different passive or semi-passive tags technologies are being developed:

Backscatter modulation

Active modulation (tag radio-powered)

Chipless passive tags

Regarding these kind of passive schemes, the problem lies in the powering of the tag. Indeed, it is not possible to extract energy from UWB signals. The UWB FCC/EU power emission mask constrains the transmitted power to be less than 1mW (vs. 2-4W for UHF RFID). This available RF power is not sufficient to energize the tag at distances of interest (>1m).

At the tag level, the energy can thus be obtained either from a small battery, from a UHF signal, or from energy harvesting, which increases the tag dimension and complexity.

The investigation of energy harvesting techniques is beyond the scope of the SELECT project, however the design of extremely low consumption tag would enable in the near future the adoption of such techniques. Then, within the SELECT project, energy efficient tag solutions for communication and accurate ranging will be investigated.

In this section, the three main passive or semi-passive tags technologies are described.

1.1 Backscatter Modulation

The main principles of backscatter communication using UWB signals are recalled here after, as well as alternative comparative solutions.

1.1.1 Backscatter modulation basics and SOA solutions

The main principle of a backscattering communication scheme presented in [8] is illustrated in Figure 1-1. The reader transmits an UWB pulse which is backscatter by the tag.

The problem is that the reflected signals coming from surrounding objects (clutter) and antenna structural mode are in general dominant (green pulses).

This effect emphasizes the need for an efficient signal structure design and an appropriate signal processing to mitigate the effect of clutter as will be addressed in section 2.



Figure 1-1 Backscatter scheme between a transmitting reader and a backscattering tag.

(depends on the tag load)

y

zz

y

x

rf

t

Jtr

Z L

READER

1

backscatter signal (clutter)

TAG

CLUTTER

a (f)

b (f)

1

tag reference systemreader reference system

f

J

structural mode scattering

antenna mode scattering

x

The tag can perform a load modulation of the reflected signal: the antenna reflection properties are changed according to the information data (antenna mode scattering). An architecture solution based on this idea is proposed in [8][12] and [9]. Indeed, it makes use of the antenna load modulation to perform 2-PAM in a UWB passive RFID (for both identification and localization).

This tag-reader communication appears to be robust to the presence of clutter and the interference, while enabling the tag multiple access.

Similar alternative backscattering communication schemes and characterizations are presented in [13], [14], [15], [16]. Specifically, a passive tag using UWB backscattering modulation for localization is implemented. In this architecture, a time-delay line is integrated within the antenna to set a delay between the antenna mode and the structural mode backscattering. Each tag is designed to give a unique backscattered waveform as signature/ID. The structural mode backscattered pulse is used to correlate the antenna mode backscattered pulse

Measurements done on the backscattering UWB impulse response seem to exhibit good agreement between simulated and measured backscattering response, although the mentioned possibility of structural antenna mode calibration has to be verified and confirmed.

It has to be noticed that along this study, no real operational measurements have been achieved. The measurements are only done in lab environment with a vector network analyzer (VNA).

Regarding the simulation campaign done, this scheme shows a good positioning accuracy: roughly 2 cm RMSE at 2 m reader-tag distance.



Additionally, the results show that it is feasible to identify the passive tag employing pulse-polarity and pulse-position. Moreover, this study establishes the relative easiness to get the characteristics time delay between antenna and structural mode backscattering pulses.

For Ep/N0 = 30dB, it has been demonstrated that 20 tags can be supported with a probability of detection error less than 1%. This represents a low number of tags supported far from the SELECT project requirements.

It has to be noticed that this study does not account for a proof of concept.

Another backscattering communication architecture is presented in [17]. This solution differs from the previous one with the fact that it implements a UHF signal for tag power supply and communication, and a backscattering modulation UWB for ranging. The principle of this architecture is illustrated in Figure 1-2. It is also made use here of tag load modulation. Contrarily to the previous study, this one represents a proof of concept. Nevertheless, the ranging accuracy has not been studied.

Figure 1-2 Backscattering communication pulse implementing an UHF powering signal [17].

1.1.2 Conclusion

As a conclusion, the pro and cons of the passive backscattering communication scheme can be listed.

This technology presents the following advantages:

It is a simple technology

The complexity is concentrated mainly at the reader side

It is easily integrable with UHF tags

It exhibits low energy consumption (energy needed only for the micro-controller and not for the RF stage)

Semi-passive tag

In the framework of the SELECT project, the patent license is held by the SELECT consortium

The disadvantages of this technology are summarized here after:



Code synchronization is an issue between reader and tag (design of synchronization schemes at the reader needed, problem reduced if used in conjunction with UHF technology), using for example wake-up synchronization signals.

The link budget penalty with respect to active UWB modulation tags might limit the operating range.

1.2 Chipless Tags

This section concerns the chipless fully passive tag. Backscattering solution minimizing tag cost and/or size are presented.

1.2.1 Inkjet printable fully passive RFID tag

This solution is presented in[20], where a very simple circuit is printed with a metallic ink on a support. 8-bit data are encoded by impedance mismatch along the transmission line printed on the material. Planar capacitors are pre-printed, and connected via intersection printed to the transmission line.

Figure 1-3 RFID tag using inkjet printing.

The main advantages of this technique are listed below:

Fully passive tag

Very low cost Inkjet Printing Technology can be used

No synchronization problems for the reader-tag codes

The disadvantages are summarized here after:

Link budget to be studied, no results found (it expected to be worse than other technologies, long transmission lines attenuation)

Dimensions problem: in an 8-bit code takes 40cm transmission line length using a 0.4ns Gaussian pulse as incoming signal

“Disposable” tags (not reconfigurable)

81 ID tags maximum (with 8 bits): not enough



Tag-collision issues in a multi access context: 8 bits code too short, high cross-correlation.

Time division multiplexing (TDM) with acknowledgement not possible

40 cm transmission line on a specific inkjet printed circuit: high insertion loss? (no data available)

1.2.2 Multiresonator

The chipless tag encodes data into the spectral domain in both magnitude and phase of the spectrum [24]. The RFID reader frequency range is between 5GHz and 10.7GHz. It successfully detects a chipless tag at 15cm range.

Figure 1-4 Photo of the UWB 35-bit chipless RFID tag.

It can be seen on Figure 1-4 an example of spectral signature obtained with the multiresonator.

Figure 1-5 Measured attenuation of 23-bit multiresonator.




Fully passive tag

Fully printable



Link budget to be studied, no results found (it expected to be worse than other technologies, long transmission lines attenuation)

Dimensions (delay lines length). Probably smaller with respect to solution 1.2.1

A 9x7cm tag is capable of 35bit coding


Doubts about its ability to work in the presence of multipath (no studies found)

Interrogation of the tag by sweeping the RF signal frequency: it is not IR-UWB: could yield to higher interferences and/or not respect of the 15.4a regulation mask

Detection based on the spectrum signature: high sensitivity to interference, poor ranging accuracy

Reader and tag antennas must be cross-polarized: significant restrictions in tag positioning and orientation

Ranging with the signal phase is ambiguous

Identification efficiency but high ranging error: phase estimation error up to 42°

Not more than 15 cm range

1.2.3 SAW devices

This technique makes use of orthogonal frequency coding associated with a SAW based tag. This tag consists of an input transducer that launches a surface wave on a substrate towards an array of single frequency collinear reflectors that reflect the wave back to the input after providing ID coding within the reflections[21].

Figure 1-6 SAW tag example.




Fully passive tag

Very small dimension and attenuation. [22][23] show the possibility of a 1x1mm tag with 3dB of insertion loss


PN-OFC-TDM technique: allows a greater number of users operating in the same frequency band without increasing the frequency


Reading distance below 3-5 meters for [22] (2 feet range success for [21]) =>this range is clearly too low for SELECT applications

Acoustic technology (cost?)


Autocorrelation function with large central peak (28 ns): poor ranging precision achievable

Cannot be used on every material, not working on plastic for example (due to piezoelectric nature)

Operational frequency impact? (tested one is 250MHz)

High loss and poor temporal resolution

1.3 Active Modulation

All the backscattering solutions presented in this section implement additionally to the UWB signal, a narrow band UHF signal. Very similar solutions are developed in [17], [25], [26], [27], [28].

In general, a narrowband downlink (tag interrogation command, tag powering) is used. Tags capture its power by power scavenging. The UWB uplink signal with an active pulse transmitter is used for localization purpose.

Figure 1-7 Radio-powered module with asymmetric link using UWB for RFID [25].



Figure 1-8 RF tag and reader with asymmetric communication bandwidth-solution proposed in [26].

Figure 1-9 RFID based on ultra-wideband time-hopped pulse-position modulation – solution proponed in [27].

The main advantages of these techniques are listed below:

Less path loss constraints (active UWB modulation on the tag side)

Simpler code synchronization respect to the UWB backscattering solution

Easily integrable with UHF Tags

Asymmetric UHF for downlink, IR-UWB for uplink: => reduce the complexity of the tag

TDM with acknowledgement protocol: decreases the tag-collision

High network throughput: 2000 tag per second for example in [25]

Good operational range: 10m range [25]




Tag complexity and costs (complete UWB transmitter or transceiver needed at the tag side)

Tag power consumption (no energy harvesting techniques can be used to power up the UWB tag)

Asymmetric link make the ranging more complex, and could decrease the ranging accuracy (clock impairment between tag and reader?)

TDM with acknowledgement protocol: increases the complexity of ranging procedure

System efficiency decrease rapidly when the difference between frame size and number of tag increase. Could yield problem with a high number of tag scenario

It has to be noticed that these techniques are only a conjunction of available technologies, and do not represent a major innovation. Moreover several patents are pending on these solutions.

1.4 Comparison and Conclusion

As a conclusion, the table below summarizes all the SOA backscattering techniques available described here and establishes the main points of comparison.

Table 1-1 Comparison of the different SOA backscattering techniques.

backscatter

modulation

chipless tag :

impedance

mismatch

chipless tag :

multiresonator

chipless tag : SAW

technology

active

modulation tag

basic concept

Use of the antenna load

modulation to perform

UWB passive RFID (for

both identification and

localization)

8 bit inkjet printable

fully passive RFID tag

tag encoding data into

the spectral signature

in both magnitude and

phase

UWB orthogonal

frequency and TDM

protocel coding for a

SAW chipless tag

radio-powered

module with

asymmetric link using

UWB for RFID

strong limitations- link budget penalty

- backscattering antenna

mode issue

- dimension of the

circuit

- link budget

- link budget

- short range

- poor ranging

accuracy expected

- link budget

- short range

- poor ranging accuracy

expected

- tag complexity and

cost

- tag power

consumption

- clocks synchro?

preliminary

opinion on

SELECT project

requirements

meeting

+ + - - - + +

From the above reported analysis it can be concluded that (semi)-passive tags based on UWB backscatter modulation represent a good trade-off between performance, complexity and energy efficiency. In particular the adoption of the UWB backscatter modulation technology offers:

Good design flexibility as both stand-alone UWB tag or hybrid UWB/UHF Gen2 tag architectures are possible.

Good potential ranging capabilities, thus enabling high-accuracy location and tracking.



Low energy required to power up the tag, thus allowing the adoption of small, even green, batteries or energy harvesting techniques.

Low cost.



2 Definition of Reader and Tag Architectures

The aim of this section is to describe the reader and tag architectures as well as signals format capable of enabling reliable communication through backscatter modulation. The following paragraphs present in details the functional blocks of the reader and tag, signal format, and the mathematical description of signal processing in the different parts of the system.

The basic principle of the UWB backscatter modulation has been described in section 1.1 and can be found in [12][11][10][14].

2.1 Reader Architecture

Figure 1-10 proposes the architecture scheme for the reader, which is composed of a transmitter and a receiver section. In the following paragraph we focus on the transmitter section, while the receiving part is object of paragraph 3 where signal-processing schemes for data demodulation are illustrated.

2.1.1 Functional blocks – Transmitter Section

With reference to the scheme reported in Figure 1-10, the transmitter section is composed of a UWB pulse generator with pulse template p(t) and of a spreading sequence (reader’s code) generator that produces an antipodal binary sequence {d

n} of length N

c symbols

(chips). Each chip modulates in amplitude the transmitted pulses (PAM modulator) as will be illustrated in more details later. The generated signal is sent to the antenna connector and radiated in air.

Figure 1-10 Reader functional blocks.

TOA tag code

ReceiverEstimator

TOA

Ns

decodedbits

d

d

c

n

n

n

v(t)

Transmitter

reader code pulse

generatorseq. generator

Matched

filterDetector

ym

TX antenna

RX antenna

UWB READER

ADC

tag code

seq. generator acquisition



2.1.2 Transmitted signal format

During the interrogation phase, the reader transmits a sequence of UWB pulses, each having energy

pE , modulated by a periodic binary spreading sequence }{ nd of period sN , with

1}1,{ nd , specific to that particular reader (reader's code). In general, Npc pulses are

associated to each code symbol (chip). Without loss of generality, an infinite interrogation sequence of pulses separated by

pT seconds (pulse repetition period PRP, or frame time) is

considered, that is,

)(=)( c

=

reader nTtgdts n

n

where

)(=)( p

1

0=

iTtptgpcN

i

is the waveform associated to each chip dn composed of Npc elementary UWB pulses.

The frame time pT is chosen so that all backscattered signals are received by the reader

before the transmission of the subsequent pulse, thus avoiding inter-frame interference. In indoor scenario

pT = 100 - 150 ns is usually sufficient to this purpose.

Each pulse in (2.1) is backscattered by all tags as well as by all the surrounding scatterers present in the environment that form the clutter component.

The main task of the receiver section of the reader is to detect the useful backscattered signal component (i.e., the antenna mode scattering dependent on antenna load changes) from the signals backscattered by the antenna structural mode and other scatterers (clutter), which are, in general, dominant. The corresponding signal processing required to perform this task will be detailed in section 3.

The overall signal structure is shown in Figure 1-11 whereas in Table 1-2 the glossary of the terms used in the subsequent sections according to the signal structure illustrated in Figure 1-11 is reported. Summarizing, each data symbol of duration T

s is divided in N

c chips of

duration Tc. Each chip contains N

pcreplicas of the transmitted pulse p(t) of energy E

p . In this

manner we have a total of Ns =N

c N

pc pulses per data symbol, or, i.e. the same N

s = T

s / T

p

having defined with Tp

the pulse repetition period (PRP) as Tp= T

c / N

pc. The consequent

transmitted power is given by Pt=<p

2

(t)>/(RTp).How data are associated to pulses in tag-

reader communication will be clarified in section 2.2.1

As example of possible pulse shape satisfying the EU UWB regulation in the 3.1-4.8 GHz band is the RRC (Root Raised Cosine) pulse with center frequency fc=3.95GHz, roll-off factor 0.6 and bandwidth W=1.6 GHz that is:

(2.1)

(2.2)



(2.3)

)2cos(/41

1/)1(sin

/4

1/)1(cos

4)(

2tf

tttt

tttt

ttp c

p

p

p

p

p

where β is the roll-off factor, fc the center frequency, and tp a parameter such that

.

Figure 1-11 Data packet structure. s

N2 r1

1 2 N c

d0 d1 dN −1c

1 2 N pc

time

T p

Tc

T



Table 1-2 Symbols Definition.

Symbol Description

{dn} reader’s spreading code of length N

c1}1,{ nd

{cn} tag’s spreading code of length N

c1}1,{ nd

{bn} tag’s data sequence 1}1,{ nd

Nc number of chips per data symbol (must be the same in the tag)

Npc

number of pulses emitted per chip

Ns =N

c N

pc number of pulses emitted per symbol

Nr number of bits per interrogation packet

Tp pulse repetition period (PRP) or frame time

Rp=1/T

p pulse repetition frequency (PRF)

Tc=T

pN

pc chip time

Rc=1/T

c chip rate (must be the same in the tag)

Ts= T

c N

c symbol (bit) time

Rb=1/T

s symbol rate (data rate) (must be the same in the tag)

Pt=E

p/T

p transmitted power (Watt)

Ep=<p

2

(t)>/R pulse energy (J)

R antenna impedance (Ohm)

p(t) transmitted pulse shape (V)

2.2 Tag Architecture

In order to make the reader able to distinguish between different tags and detect the useful signal immersed in the clutter a modulation scheme has to be adopted by the tag as will be presented in this section. A quite general backscatter modulator architecture is presented in Figure 1-12 which allows for different signaling schemes such as PAM (Pulse Amplitude Modulation), PPM (Pulse Position Modulation) and ON-OFF keying.



Figure 1-12 Backscatter modulator architecture.

However, the general structure of Figure 1-12 requires costly and space consuming UWB delay lines leading to a tag not satisfying the SELECT requirements. Then a simplified tag architecture version that considers 2-PAM signaling is analyzed. In this case, the backscatter modulator reduces to a simple switch (switch S1 as in figure).

2.2.1 Tag Functional blocks

Figure 1-13 shows the functional blocks that compose the tag. The device is connected to a wideband UHF/UWB antenna (or also two separated antennas) and two different sections (UHF and UWB) are responsible for signal processing and power management of the narrowband and wideband signal components, respectively. More in particular the UHF part is composed of a standard Gen2 UHF RFID driven by appropriate control logic. Since a standard UHF tag is already composed of a power management unit that extract energy from the interrogation UHF signal, the same unit could be used to detect an eventual wake up signal to switch the UWB section on and provide a synchronization signal [6].

The UWB section is powered by a battery (semi-passive tag) and composed of a simple control logic that generates the data bits to be transmitted (for example the tag ID) and the spreading code necessary to allow multiple access and clutter removal as will be explained in the following sections. The RF stage consists of a simple UWB switch driven by the composite sequence given by the modulo-2 product between the data bit and the spreading code.



Figure 1-13 Tag functional blocks.

{c }

UHF/UWB antenna

RFID modem

Power

management

control

batterycontrol logic

data (ID)

memory

spread code

memory

switch

m(t)XOR

n

f

f

data

chip

mem

ory

wake−up

UWB TAG

STANDARD UHF TAG

logic

Consider now a scenario where a reader interrogates tagN tags located in the same area. To

make the uplink communication between the k -th tag and the reader robust to the presence of clutter, interference, and to allow multiple access, each tag is designed to change its status (short or open circuit) at each chip time

cT according to the data to be

transmitted and a periodic tag's code }{ )(k

nc , with 1}1,{)( k

nc , of period sN . Specifically,

each tag information symbol 1}1,{)( k

nb is associated to sN pulses, thus the symbol

duration equals sps = NTT . In this way the polarity of the reflected signal changes according

to the tag's code sequence during a symbol time, whereas the information symbol affects the entire sequence pulse's polarity at each symbol.

The reader and the tags have their own independent clock sources and hence they have to

be treated as asynchronous. Let us denote )(

f

)()( = kkk uT , with )(ku integer and

f

)( <0 Tk , the clock offset of the k -th tag with respect to the reader's clock. In case a

wake-up signal is available, then reader and tags spreading codes can be considered aligned

(synchronous) and hence 0)( ku , whereas )(k in general may be different from zero due to

the unknown propagation delays.

Therefore, the backscatter modulator signal, commanding the tag's switch, can be expressed as

)(

cs

c

)()(

1s

0=

1

0=

)( 1=)( kk

n

k

i

N

i

N

n

k iTnTtT

bctmr



)(

c

)(

c

)()(1

0=

)(1

= kkk

nf

k

n

N

n

TuntT

bcr

having defined s/Nnnf and 1)( t for [0,1]t and zero otherwise.1

2.2.2 Backscattered signal format

In the following analysis the tag response due to the antenna mode is examined whereas the antenna structural mode will be treated as part of the clutter since it does not depend on data symbols. As a consequence, any clutter removal technique adopted will be also effective on the antenna structural mode component.

The signal received by the k -th tag is given by:

)(~=)( c

)(

=

)(

tag nTtgatr k

n

n

k

where )(~ )( tg k is the downlink (reader-tag) channel response to )(tg which includes also the

propagation delay.

According to (2.4) and (2.5), and considering perfect pulse symmetry in the two antenna load conditions, the signal scattered by the k -th tag can be written as

)()(=)( )()(

tag

)(

tag tmtrts kkk

)(~)(~= c

)(

II

)(

1)(

)(

1)(c

)(

I

)(

)(

)(

)(

=

nTtgbcnTtgbcd kk

kunf

k

kun

kk

kunf

k

kun

n

n

where we defined

)(

c

)()()(

I )(~)(~k

kkk

T

ttgtg

.)(~)(~)(

)()(

II

k

kk ttgtg

Here we assumed a perfect switch, i.e., a switch characterized by an instantaneous switching time as well as absence of ringing effects. These effects may not be negligible during the synchronization phase when load changes could happen in the middle of a pulse thus leading to pulse distortion. However, they do not affect the system performance once the reader has synchronized to the backscattered signal.

This signal is then sent to the UWB antenna and re-irradiated to the reader.

1Operator x denotes the smallest integer larger than or equal to x .

(2.4)

(2.5)

(2.6)

(2.7)

(2.8)



In Figure 1-14 an example of signals exchange between the reader and the tag is illustrated in the case of N

pc=1 pulses per chip (then )()( tptg ).

Figure 1-14 Example of signal exchange between the reader and the tag



3 Signal Processing Techniques for Communication

3.1 Reader receiver section

We now analyze the reader receiver section and the processing needed for demodulating the bits transmitted by the tag.

An equivalent scheme of the backscatter link is presented in Figure 1-15: pulses radiated by the reader according to the sequence {d

n} are received by the tag and multiplied by the code

{cn} and the data. Successively they are backscattered and received by the reader. A

despreading stage realizes the product of the received signal and the composite sequence given by the reader and tag code, and a decision stage demodulates the data bit sent by the tag [12].

Figure 1-15 Equivalent scheme of the backscatter link.

Nc chips)

tag code

tag ID

(one data symbol everysymbols

n/Nc

pulse

generator

nd

nd

seq. generator

transmitter

Detector

decoded

receiver

TAG

READER

Despreader

nc

b

cn

tag code

reader code

The received signal at the reader is2

2Coupling effects between close tags are not considered here. They deserve future investigations even though we

expect in most cases their impact on system performance is negligible thanks to the different spreading codes

adopted in each tag.



(3.2)

,)()()(=)( c

)C(

=

)(

reader

tag

1=

reader tnnTtwdtrtr n

n

k

N

k

where )(tn is the additive white Gaussian noise (AWGN) with two-sided power spectral

density /20N and )((C) tw is the backscattered version of the pulse )(tg due to the clutter

component which also accounts for pulse distortion, multipath propagation, and tag's antenna structural mode. The signal )()(

reader tr k represents the received useful component due

to the k -th tag, i.e.,

)()(=)( c

)(

II

)(

1)(

)(

1)(c

)(

I

)(

)(

)(

)(

=

)(

reader nTtwbcnTtwbcdtr kk

kunf

k

kun

kk

kunf

k

kun

n

n

k

having denoted )()(

I tw k and )()(

II tw k , respectively, the uplink channel response to )(~ )(

I tg k

and )(~ )(

II tg k . Note that )()(=)( )(

II

)(

I

)( twtwtw kkk is the round-trip response to )(tg of the

backscatter.

Consider the reader's receiver scheme reported in Figure 1-10, where the received signal is correlated with a local waveform template )(th with unitary energy (or, equivalently,

filtered through a filter matched to the template h(-t)). The output is then sampled at intervals

0sc, = TmTit mi, with 10,1,...,= s Ni , thus obtaining the samples

,=)()(=)()(= ,

)(

,

)(

,

tag

1=

,,reader,reader

c

0, mi

C

mi

k

mi

N

k

mimimi

T

mi zvvthtrdtttrthv

where3

)(= c0sc

)(

I

)(

)(

)(

)(

=

)(

, nTmTiTbcdv kk

kunf

k

kun

n

n

k

mi

,)( c0sc

)(

II

)(

1)(

)(

1)(

nTmTiTbc kk

kunf

k

kun

.)(= c0sc

)(

=

)(

, nTmTiTdv C

n

n

C

mi

In (3.3), (3.4) and (3.5) we have defined )()()( )(

I

)(

I thtwt kk ,

)()()( )(

II

)(

II thtwt kk , )()()( )C()C( thtwt , )()()( thtntz , and

)( 0sp, mTiTzz mi.4

Without loss of generality, we consider the problem of detecting the data bit (1)

mb of tag #1

(useful tag). As shown in Figure 1-10,to detect the first tag at the receiver, the sampled

3 denotes the convolution operator.

4According to the design criteria for

pT illustrated before, the support of )(t belongs to ][0, cT and no inter-

frame interference is present.

(3.1)

(3.3)

(3.4)

(3.5)



signal miv ,

is multiplied by the composite sequence }{ (1)

nn dc , which identifies both the

reader and the desired tag #1.5 In particular, all sN resulting samples at the output of the

correlator composing a data symbol are summed up to form the m th decision variable at

the detector input. Considering that )()(

s= k

i

k

mNi cc , and imNi dd =

s

i , the decision variable

for the m -th symbol (1)

mb becomes

miii

N

i

m vdcy ,

(1)

1s

0=

=

(1)

1(1)

(1)

1(1)

(1)

0

2

00

(1)

II

(1)

(1)

(1)

(1)

(1)2

1s

0=

0

(1)

I )()(=

umfuumfuiii

N

i

bccdbccd

m

C

mmumfui

ii

N

i

zybccd

)((1)

1(1)

(1)

1(1)

(1)2

1s

1=

0

(1)

II )(

where

(1)

1s

0=

0

)(

c0sc

)(

=

(1)

1s

0=

)( )(=)(= i

N

i

CC

n

n

ii

N

i

C

m cnTmTiTdbcy

and miii

N

im zcdz ,

1s

0=

is a Gaussian distributed random variable (RV) with zero mean and

variance /2= 0s

2 NNz .

The component m accounts for the MUI and can be expressed as follows[10]

)(

,

(1)

1s

0=

tag

2=

= k

miii

N

i

N

k

m vdc

)(

1)(

)(

1)(

(1)

0

2

00

)(

II

)(

)(

)(

)(

(1)2

1s

0=

0

)(

I

tag

2=

)()(= k

kumf

k

ku

kk

kumf

k

kui

ii

N

i

k

N

k

bccdbccd

)(

1)(

)(

1)(

(1)2

1s

1=

0

)(

II )( k

kumf

k

kui

ii

N

i

k bccd

whose effect on the decision variable strictly depends on the cross-correlation property between codes }{ (1)

ic and }{ )(k

ic .

In the following we assume that code synchronization is achieved after an initial acquisition

phase, i.e., 0=(1)u . To this purpose a wake up signal available through the UHF part could

5Multiple readers may access the same tag by using different reader codes provided that they are designed with

good cross-correlation properties.

(3.6)

(3.7)

(3.8)



(3.11)

archive this requirement enabling the possibility of avoiding complex acquisition techniques. From (3.6) we have

.)()()(= )((1)

1

(1)

0

(1)

10

(1)

II

(1)(1)

1

1s

1=

0

(1)

IIs0

(1)

I

(1)

m

C

mmmii

N

i

mm zybccccNby

Looking at (3.9) it can be noted that the useful term depends on the partial autocorrelation properties of code }{ (1)

ic .

As a further hypothesis we assume that a perfect TOA (Time of Arrival) estimate is available. Details on how the TOA estimated can be achieved are given in Sec. 3 and in [16]. Once the TOA is known, the reader can adjust its internal clock so that it becomes synchronous to that

of the intended tag, i.e., 0=(1) , and the optimal choice for 0 can be derived. In such a

case of perfect synchronization6

dttwE2

(1)

w0

(1)

I )(==)(

and (3.9) can be further simplified leading to

,== )(

s

(1))(

ws

(1)

mm

C

mmm

C

mmmm zyEbzyENby

where wss = ENE , and is the normalized cross-correlation between pulses )((1)

I tw and

)(th , which accounts for the mismatch due to pulse distortion. Parameters wE and

sE represent the average received energy per pulse and symbol, respectively.7 For further

convenience we define the SCR as cs

s=SCREN

E, where dttwE C

T 2)(p

0c )(= is the energy

per pulse of the clutter component.

3.2 Signal parameters design criteria

When choosing the signal parameters several constraints have to be taken into account.

The pulse repetition period Tp should be larger than the maximum backscatter delay, e.g., 133 ns means maximum 20 meters. This number does not affect tag’s parameters and can be changed on-the-fly by modifying Npc

6Note that under perfect timing condition it is )(=)( (1)(1)

I twtw and 0=)((1)

II tw . Moreover, as already

mentioned, any non ideal switching effect becomes negligible because it affects parts of the received signal not

interested by the useful backscattered pulse. 7Note that the energy of the useful tag response may vary for different delays and codes when the reader is not

synchronized. In such a case (22) does not hold anymore.

(3.9)

(3.10)



The chip time Tc affects the tag power consumption [6] as well as the data rate together to the number of chips per data symbol Nc. Higher values of Tc reduce the tag consumption but also decrease the data rate.

As will be shown in section 3, high values of Npc reduce the sensitivity of the communication performance to tag clock drifts.

The communication performance is dominated by the total number of pulses per data symbol Ns=NcNpc. By increasing Ns the total SNR is increased as will be shown in section 3.3.3. However, the data rate Rb decreases correspondingly. Then there is a trade-off between performance in terms of bit error rate and data rate.

An example of realistic values for the parameters described in Table 1-2 is reported below:

Tc=1us Rc=1 Mchip/s

Nc=1024 Ts=1.024ms, Rb=976 bit/s

Npc=5 Tp=200ns (can be changed on-the-fly by the reader without requiring modifications at tag level)

3.3 Spreading codes design criteria

The aim of this section is to define the design criteria for the reader and tag spreading codes in order to allow multiple access of different tags and permit the clutter removal process necessary to detect the information bits. As reference we consider the example of Figure 1-1 related to the PAM backscatter modulation already treated in Sec. 2.2. Red waveforms are referred to the useful signal modulated in amplitude by the reader code (supposed in figure to be equal to one for all the pulses), the tag code and the data symbol, while green waveforms are related to the clutter modulated uniquely by the reader code.



Figure 1-16 Example of PAM backscatter modulation format in case of Npc=1.

3.3.1 Clutter removal

Looking at (3.6), (3.7) and Figure 3-1, it can be noted that only the antenna mode scattered signals are modulated by the combination of the tag's and reader's codes }{ )(k

ic and }{ id ,

whereas all clutter signals components (including the antenna structural mode scattering) are received modulated only by the reader's code }{ id . This suggests, as can be deduced

from (3.7), that to completely remove the clutter component, and hence the antenna structural mode component, it is sufficient that the tag's code }{ (1)

ic has zero mean, i.e.,

0=(1)1s

0= n

N

nc

, leading to 0=)(C

my , if a quasi-stationary scenario within the symbol time sT is

assumed.

This behavior can be explained looking at Figure 1-17. After a coherent summation of the waveforms multiplied at the reader side by the zero-mean tag code, the clutter signals cancel out each other, assuming a stationary channel in the symbol time, while the useful components backscattered by the tag are coherently and in-phase summed up and the unique waveform polarity is due to the data bit transmitted in the current frame by the tag that the reader is detecting.



Figure 1-17 Zero-mean tag code behavior.

3.3.2 Multiple access

Regarding the MUI, the situation is similar to what happens in conventional code division multiple access systems where the performance is strictly related to the partial cross-correlation properties of codes }{ (1)

ic and }{ )(k

ic . Classical codes such as Gold codes or m -

sequences offer good performance. Unfortunately they are composed of an odd number of symbols and hence there is no way to obtain a zero mean code to completely remove the clutter. However, considering that m -sequences have a quasi-balanced number of "+1" and "-1". i.e., their number differs no more than 1, one option to achieve clutter removal is to lengthen the code by one symbol so that the resulting code had zero mean. As a consequence we expect certain degradation in terms of multiple access performance, especially when short codes are adopted. In the numerical results this aspect will be investigated.

When the scenario is quasi-synchronous, i.e., 0=)(ku k and 0)( k , orthogonal codes, such as Hadamard codes, represent a good choice and 0=m . This could be the situation

where a wake up signal is sent by the reader to switch the tag on and reset the code phase. An extensive code behavior analysis will be object of further investigations.

3.3.3 Codes assignment strategies

One of the key that allows the proper functioning of the RFID UWB system is the codes behavior in order to permit multiple access and clutter removal. These codes have to be assigned to the tag. Different strategies are possible in order to archive this:



• Option 1: the system assigns a unique code cn to each tag entering the area through the UHF link (login phase with a portal UHF reader/writer). Then all readers in the same area know the number of tags in the area and their respective cn.

• Option 2: each tag has a fixed cn code (e.g., fixed at manufacturing time) out of a small set of possible codes (e.g., 16 codes). No login phase is requested as all readers in the area are "tuned" on the 16 possible codes. Tags are distinguished because of the different unique ID transmitted as data. There is a high possibility of code collisions but, due to the extremely low duty cycle of the UWB transmission, the probability that collisions occur also in time should be low.

The first option, because of the login phase, requires a tighter interaction between the UWB and UHF parts of the tag for code assignment, and more flexibility at reader side as it has to be tuned to a larger set of different codes. The code length must be very large (>1000 for asynchronous scenarios). However, no data (ID) transmission between tag and reader is necessary as there is a unique correspondence between ID and spreading code. This fact implies a less stringent requirement on communication data rate as well as packet length and opens a new interesting scenario. In fact, in the extreme case, only the transmission of one bit is sufficient to detect the tag and hence the tag detection is concluded just after the acquisition phase (only preamble, no payload). In such a case, a simplified tag architecture is possible as shown in Fig. 4-3a, where no data memory is present and the spreading code can be changed through the UHF link. Since no data are transmitted, the most meaningful performance index is not anymore the bit error probability (BEP), but the probability of detection (Pd), i.e., the probability that the tag is detected, and the probability of false alarm Pf, i.e., the probability that, due to interference and noise, the tag is detected even if it is not present. The processing at the reader is simplified (no data demodulation) and the tag detection time is speeded up.

The second option does not necessary require a login phase and an interaction between the UWB and UHF tags. However, some performance degradation due to code collisions might be present. Shorter spreading codes can be adopted. Consequences of these possible collisions and related performance degradation, as well as synchronization issues, have to be studied with more detail in future.



Figure 4-3a Alternative tag structure without payload

chip

UHF/UWB antenna

RFID modem

Power

management

control

batterycontrol logic

switch

m(t)

mem

ory

wake−up

UWB TAG

STANDARD UHF TAG

spread code

memory

{c }n

f

logic

In view of what emerged from the analysis presented the following considerations about code choice can be underlined:

• The tag’s code cn must have zero mean (equal number of +1 and -1) to remove the clutter during the de-spreading operation in the reader (in slow-varying channels). This requirement is less stringent if long codes are adopted (this will be analyzed in Section 3.3 through numerical results).

• The tag’s code cn can belong to an orthogonal codes set (e.g., Hadamard) if tags are quasi-synchronized through a wake-up signal. Up to Nc codewords (simultaneously detectable tags) are in this case available.

• The tag’s code cn must belong to PN-like code sets (e.g., Gold, m-sequences) if tags are not synchronized through a wake-up signal. Usually N<<Nc codewords (simultaneously detectable tags) are available, then very long codes are needed to support a large number of tags. For example adopting m-sequences with Nc =1023 we have only N=60 different codes. The number of codes increases to N=176 adopting a Nc =2047 m-sequence. Gold codes are obtained starting from the addition of two m-sequences and provides better cross-correlation properties but only a part of them is quasi-balanced (number of +1 and -1 that differs no more than 1).



• Multiple readers can access simultaneously the same tag using different codes provided that they have good crosscorrelation properties (e.g., Gold codes if readers are asynchronous, orthogonal if readers are synchronous).

• If tags are asynchronous, code acquisition might take a very long time and synchronization algorithms could become highly complex.

• Data payload can be avoided if a login phase is foreseen and the system assigns a unique code cn to each tag entering the area (option 1).

3.3.4 Analysis of signal backscattered pulse quantization

Obviously, the quantization operation yields losses as regards to the backscattered pulse detection and then TOA estimation. In order to evaluate the impact of the quantization, a preliminary simulation study based on waveforms generated according to the IEEE 802.15.4a channel model has been carried out without accounting for the effect of clutter which deserves future investigations.

The quantization is done in full scale (without saturation) regarding the structural response for a tag-reader distance of 70 cm. No automatic gain control is considered. The quantization of the RF section output sample II, IQ, QI, QQ (see section 4.4) is done with 5 bits signed.

The TOA estimation is done on the antenna mode pulse scattering response, the tag is static, the channel is kept constant static during simulation (one realization of Residential LOS channel), code is 1023 length, and channel is Industrial LOS.



Figure 1-18 Comparison of the quantized and not quantized channel energy profile at the output of the RF section. On the X-axis is the index of the energy bin, on the Y-index is the level of energy of the signal. The blue curve represents the RF output signal energy not quantized, whereas the red curve represents the RF output signal energy quantized

220 240 260 280 300 320 340 3600

2

4

6

8

10

12

x 10-3Comparison energy channel profile quantized vs non quantized - Rx-Tx distance : 1m

RF output signal energy

RF output signal energy quantized

220 240 260 280 300 320 340 360 3800

1

2

3

4

5

6

7

x 10-4 Comparison energy channel profile quantized vs non quantized - Rx-Tx distance : 2m



a) tag-reader distance = 1m b) tag-reader distance = 2m

200 250 300 350 400 4500

1

2

x 10-4 Comparison energy channel profile quantized vs non quantized - Rx-Tx distance : 3 m



240 260 280 300 320 340 360

1

2

3

4

5

6

7

8

9

10

11x 10

-5 Comparison energy channel profile quantized vs non quantized - Rx-Tx distance : 4m



c) tag-reader distance = 3m d) tag-reader distance = 4m

200 250 300 3500

0.5

1

1.5

2

2.5

3

3.5

4

x 10-5 Comparison energy channel profile quantized vs non quantized - Rx-Tx distance : 4.5m



e) tag-reader distance = 4,5 m

In the following table, the tag-reader maximum range that can be estimated versus the number of quantization bits has been summarized. The tag-reader maximum range is considered as the Tx-Rx distance for which the RF section output signal falls below the quantization step. It has to be remarked that these results do not account for the clutter



(3.12)

effects that might saturate the ADC if the despreading operation is performed in the digital domain. In such a case the expected number of ADC bits increase.

Table 1-3Tag-reader maximum range versus the number of quantization bits.

Number of bits of the ADC Distance max than can be

estimated (meters)

5 4.4

6 6.65

7 >20

Tackling the quantization issue by another angle, it has to be ensured that the structural mode amplitude of the backscattering signal should not lead to any saturation even when the backscattering signal is received with its highest energy (when being closest to the reader). In Figure 1-19, has been represented the measured waveforms without noise of backscattering Antenna Mode with maximum amplitude AAM and Structural Mode with maximum amplitude ASM

Figure 1-19 Measured waveforms of backscattering Antenna Mode and Structural Mode

98 99 100 101 102 103 104

-0.2

-0.1

0

0.1

0.2

0.3

delay (ns)

am

plit

ude

Measured structural mode

Measured antenna mode

ASM

AAM

If q is denoted as the quantization step, and Nb is the number of bit used to quantize the signal, and if it is assumed that the structural mode does not lead to any saturation, q can be expressed as:

12

bN

SMAq



(3.13)

(3.14)

The condition to ensure that the useful signal, the antenna mode signal, is quantized a minima is:

qAAM

The calculation is straightforward to give the minimum number of bits to quantize the signal:

1log 2

AM

SM

bA

AN

If using best cases values for AAM and ASM , we obtain :

3bN

3.3.5 Analysis of tag clock drift

The tag clock drift regards to the reader clock results in correlation losses at the reader side when correlating the backscattered code signal with the reference code. Another effect that might result in correlation losses is the reader-tag synchronization jitter. The importance of this effect is strictly correlated to the specific code adopted (e.g., orthogonal) and it is not accounted for in this section.

To evaluate the impact of this tag clock drift, two figures of merit have been computed:

- the ratio of the highest peak maximum amplitude of the ideal code autocorrelation over the highest peak maximum amplitude of the code correlation with code with drift. We call it here CPAPR : Cross-correlation Peak to Autocorrelation Peak Ratio.

- the ratio of the highest peak maximum amplitude of the code correlation with code with drift over the secondary max peak of correlation without main peak (peak of the code grass level) . We call it here CPGPR : Cross-correlation Peak to Grass Peak Ratio.

The CPAPR represents the pure correlation losses. The CPGPR represents the separation between code cross-correlation and code grass level, i.e. the increase ratio on the code synchronization false alarm probability.

These quantities have been evaluated for different parameters of signal structure:

- a 128 length code with 1 pulse per code chip





Figure 1-20 Tag clock drift impact for a 1 x 128 code length.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Code clock Drift (in %)

maxim

um

s R

ati

oRatio of max peak amplitude of correlation function with drift

over max peak amplitude of correlation function without drift

Seq Length = 1 x 128, i.e. 1 pulse per chip code

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

1

2

3

4

5

6

7

8

9

10


maxim

um

s R

ati

o

Ratio of max peak amplitude of correlation function with drift

over max peak of the correlation function grass (without CF peak)

SeqLength = 1 x 128, i.e. 1 pulse per chip code

a) CPAPR b) CPGPR


0 5 10 15 20 25 30 35 40 45 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


maxim

um

s R

ati

o


over max peak amplitude of correlation function without drift - Seq Length = 8 x 128

0 1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

14

16

maxim

um

s R

ati

o



over max peak of the correlation function grass (without CF peak) - SeqLength = 8 x 256

a) CPAPR b) CPGPR

If the limits are (very lowest limit) set to:

• -10dB correlation loss max on the CPAPR

• And min ratio of max main peak over grass max peak CPGPR is 1 (hard limit of code synchronization)

Then, the results obtained are:

• Clock drift limit for a 1x128 (only one pulse per code chip) sequence length is around 0.5%

• Clock drift limit for a 8x128 sequence length is around 5%

• Clock drift limit for a 8x256 sequence length is around 2%

We can thus conclude that against tag clock drift, it is beneficial to have several pulses per code chip. It is also possible to extrapolate the results for a 8x1023 sequence: the tag clock drift limit should be in this case of around 0.5 %.




0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


maxim

um

s R

ati

o


over max peak amplitude of correlation function without drift - Seq Length = 8 x 256

0 5 10 15 20 25 30 35 40 45 500

2

4

6

8

10

12

maxim

um

s R

ati

o



over max peak of the correlation function grass (without CF peak) - SeqLength = 8 x 128

a) CPAPR b) CPGPR

3.4 Performance Analysis in the Reference Scenario

This section we show preliminary performance analysis conducted in ideal scenario (single tag, no multipath). First of all a link budget analysis will be presented in order to define the average received power taking in consideration the constraints of the FCC and EU UWB emission masks, to serve also as input for the work packages responsible for the reader and tag design.

Subsequently performance in terms of bit-error-rate and system constraints will be shown basing the analysis on theoretical waveforms and measured waveforms taken in anechoic chamber.

3.4.1 Preliminary measurements analysis in anechoic chamber

Figure 1-23 shows an example of measured backscattered signal at reference distance dref= 1.44 m, taken in anechoic chamber for different antenna load conditions (open-short). A BAV (Balanced Antipodal Vivaldi) antenna in the direction of maximum radiation is considered. Antenna mode scattering is divided by the structural mode scattering by means of a delay line (in a real system these two contributions will be received overlapped). As can be seen, the structural mode scattering is independent on the load condition while the antenna mode scattering is different in the case of open circuit, short circuit or matched load.



Figure 1-23 Signal measurements in anechoic chamber. The plot represents the measured backscattered signals amplitude as a function of the time

0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 1.45

x 10-8

-6

-5

-4

-3

-2

-1

0

1

2

3

4x 10

-5 RAW reponse

open

short

load

Figure 1-24 shows with more detail the behavior of the antenna mode scattering as a function of time. The cross-correlation between the 2 measured waveforms is ρ = −0.98, which confirms good pulse symmetry between the two load conditions: 2-PAM signaling scheme is in this manner possible.

Figure 1-24 Antenna mode measured in anechoic chamber.



3.4.2 Link budget

A preliminary link budget analysis with theoretical waveform is now presented. This analysis is conducted operating at center frequency and has the scope to understand the signal level that can be expected at the receiver side. The RRC (Root Raised Cosine) pulse (2.3) with center frequency 3.95 GHz, satisfying the EU UWB regulation in the 3.1-4.8 GHz band has been taken as transmitted waveform. Simulations are performed for two different sets of parameters, more in particular for two different PRP and receiver noise figure. The complete list of simulation parameters (case A and B) are reported in Table 1-4 and Table 1-5.

Table 1-4 Simulation parameters for link budget analysis at center frequency – case A.

Parameter Value Description

Tp 128 ns Pulse repetition period

Pt -16 dBm Reader transmitted power

Gr 5 dBi Reader antenna gain

Gt 2 dBi Tag antenna gain

EIRP -11 dBm Equivalent isotropically radiated power

Tw 0.95 ns Pulse width parameter

Γ 0.6 Pulse roll-off factor

fc 3.95 GHz Pulse center frequency

W 1.7GHz Bandwidth

F 4 dB Reader Noise figure

BPSK Modulation scheme

Table 1-5 Simulation parameters for link budget analysis at center frequency – case B.


Tp 64 ns Pulse repetition period

Pt -16 dBm Reader transmitted power

Gr 5 dBi Reader antenna gain

Gt 2 dBi Tag antenna gain

EIRP -11 dBm Equivalent isotropically radiated power

Tw 0.95 ns Pulse width parameter

Γ 0.6 Pulse roll-off factor

fc 3.95 GHz Pulse center frequency

W 1.7GHz Bandwidth

F 7 dB Reader Noise figure



BPSK Modulation scheme

Figure 1-25 and Figure 1-26 show the transmitted RRC pulse taking in consideration the parameters presented in Table 1-4 and Table 1-5. The different amplitude is due to the different PRP. In fact the system is constrained by the emission mask regarding the radiated PSD: increasing the PRP leads to an increase of the pulse amplitude and, consequently, the pulse energy, maintaining the same EIRP value. The correspondent radiated PSD, taking into consideration the peak reader antenna gain, is reported in Figure 1-27: note that this PSD is the same for both the parameter sets (both case A and case B). The correspondent received power is reported in Figure 1-28 considering the free-space attenuation of a single path in presence of different losses due to the backscattering process in the tag (i.e. losses due to the presence of a not-ideal switch, delay lines,…). Also in this case, since the EIRP is the same and the reader antenna has the same gain, the power link budget results equal for cases A and B.

Figure 1-25 Transmitted pulse according to EU UWB regulation – case A.



Figure 1-26 Transmitted pulse according to EU UWB regulation – case B.

Figure 1-27 Power spectral density of the radiated signal.



Figure 1-28 Received power at the reader side.

Figure 1-29 Peak voltage at reader side – case A



Figure 1-30 Peak voltage at reader side – case B

The peak voltage at the reader side is also plotted in Figure 1-29 and Figure 1-30Errore. L'origine riferimento non è stata trovata., where also the noise RMS level WRN0 (Volt),

with FkTN 00 and noise figure F=4 dB and 7 dB, is reported8.

Table 1-6 and Table 1-7 report the level of the useful signal in transmission and the level of the noise at the receiver according to parameters of Table 1-4 and Table 1-5.

Table 1-6 Signal and noise levels – case A.


Pt -16 dBm Transmitted power

Vpeak 0.67 V Transmitted pulse peak amplitude

Ep 4.95e-10 V^2 s Transmitted pulse energy

N0 -110 dBm/MHz Noise PSD

Pn 17 pW Noise power in a band W=1.7 GHz

Vrms 29 uV Noise RMS value

Table 1-7 Signal and noise levels – case B.


Pt -16 dBm Transmitted power

Vpeak 0.47 V Transmitted pulse peak amplitude

8k represent the Boltzmann constant, T0=290K the reference temperature and R=50Ω the load resistance.



Ep 2.47e-10 V^2 s Transmitted pulse energy

N0 -107 dBm/MHz Noise PSD

Pn 34 pW Noise power in a band W=1.7 GHz

Vrms 41uV Noise RMS value

The single-pulse signal-to-noise ratio SNRp at the receiver side is reported in Figure 1-31 and Figure 1-32.

The dynamic range with respect to one meter, i.e. the ratio between the peak amplitude at one-meter distance and the generic peak amplitude at distance d, is reported in Figure 1-33: also this parameter, defined as a ratio, results independent on system parameters. Note that the final signal-to-noise ratio, SNR, at the demodulator is given by SNR=SNRp Ns. As a consequence, one approach to improve the SNR without increasing the transmitted power is to increase the number of pulses per symbol Ns by reducing the bit rate.

Figure 1-31 Single-pulse signal-to-noise ratio at the reader side – case A.



Figure 1-32 Single-pulse signal-to-noise ratio at the reader side – case B.

Figure 1-33 Dynamic range at the reader side.

3.4.3 Data Rate and Bit Error Rate analysis

A first performance analysis, in terms of data rate – bit error rate, is here reported.

As receiver solution we consider two alternatives:

Single-path matched filter (SPMF)

In the absence of other information the matched filter (MF) template is chosen to be proportional to the received pulse in free-space propagation at the orientation of



maximum tag’s antenna radiation. This receiver is optimal in AWGN but it is sub-optimal in a multipath scenario. In such a case, the receiver can easily be extended to obtain a Rake structure composed of a number

pL of fingers, each of them synchronized to a

different path.

Ideal Matched Filter (IMF)

The IMF receiver is a Rake receiver with unlimited correlators (all Rake Receiver) and a perfect estimate of the received waveform to be used as local template.

The performance of the SPMF, or any other receiver solutions such as those based on Rake structures, are bounded by the ideal matched filter (IMF) receiver which can be used as a benchmark solution. Both solutions suppose a coherent receiver with perfect synchronization.

We now present the performance in a single-tag and multi-tag scenarios in order to gain some insights about the attainable ultimate performance using the backscatter communication mechanism. In the absence of other tags in the environment we have

0=m , whereas clutter contribution is completely suppressed thanks to the adoption of

zero mean codes.

The performance in the ideal scenario can be obtained by

0

2

sb erfc

2

1=

N

EP

where )( erfc is the complementary error function and accounts for the mismatch

between the received signal and the template in the SPMF receiver. The performance of the

IMF can be easily obtained by setting 1

For further convenience, we define prefref /= EEG , i.e., the round-trip channel power gain at

the reference distance refd and at the maximum direction of radiation

maxΘ in AWGN

scenario, where refE is the received energy per pulse at the reference distance

refd and

pE the transmitted energy. In addition, we assume a typical exponential path loss law where

the power path loss exponent typically ranges between 1.8 and 4 . The bit error

probability can be rewritten as [12]

b0

2

ref2

reft

b erfc2

1=

RN

d

dGP

P

where )1/(= psb TNR is the data rate (symbol rate). It is interesting to note that the

exponent 2 is present instead of in (3.16) to account for the two-way link.

(3.15)

(3.16)



(3.17)

Figure 1-34 shows the distance-data rate relation for single path matched filter (SPMF) in AWGN for different TAG orientation angles (BAV antenna considered), with target bit error probability 10-3. In this case waveforms taken by measurements conducted in anechoic chamber have been used.

Figure 1-34 Distance-Data rate relation for single path matched filter (SPMF) in AWGN for different TAG orientation angles, with target bit error probability 10

-3.

For what concerns the analysis conducted with signals in agreement with the EU UWB mask, the number of pulses, considering an AWGN channel, necessary to reach the SNR needed to assure the target bit error probability 10-3 (i.e. the number of pulses needed to reach the target SNR=6.79dB considering a matched filter) is reported in Figure 1-35 and Figure 1-36. The correspondent data rate is reported in Figure 1-37 and Figure 1-38.

As example if we consider a distance of 10 meters, looking at Figure 1-31 for 0dB tag losses, we notice that the single-pulse SNRp is near to -30 dB; since about 7 dB are required for a matched filter to reach the 10-3 BEP, a gain of 37 dB is required. This gain corresponds to about 5000 pulses, as visible in Figure 1-35.

For what concerns the receiver implementation, a strong limitation is given by the ADC dynamic: the number of quantization levels has to guarantee low distortion for the useful signal part without incurring in saturation due to the presence of the clutter component. The signal-to-quantization-noise-ratio (SQNR), i.e. the ratio between the signal power and the noise power introduced by the quantization process, can be calculated as

SQNR=E[X2 ]

M 23L2



where M is the ADC dynamic, X is the random variable that represents the sampled signal, and L=2m is the number of quantization levels obtained using m bits. Considering the hardware proposed by LETI, presented in Figure 4.5, the digital conversion is operated after the correlation process. In order to evaluate the SQNR in necessary to characterize the statistics of the clutter falling within the integration window at the receiver, information not available at this moment. As first results we now assume to directly sample the waveform taking M as the maximum clutter amplitude and substituting E[X2] with the square of the signal peak amplitude. By inverting equation 3.17 it is possible to compute the maximum tolerable clutter amplitude M, for different numbers of ADC bits, corresponding to a minimum SQNR target of 10dB. These results are presented in Figure 3.25 and Figure 3.26. As an example let us suppose to work with the parameters of simulation case A: considering a reader-tag distance of 10m and assuming that the ADC digitalizes the signal using m=7 bits

(pink curve), the receiver works with SQNR10dB only if the clutter has a peak amplitude equal of less of 10-4 Volt.

Figure 1-35 Number of pulses necessary to reach the target BEP of 10-3

– case A.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1510

0

101

102

103

104

105

106

distance [m]

Ns

Tag loss: 0dB

Tag loss: 3dB

Tag loss: 5dB



Figure 1-36 Number of pulses necessary to reach the target BEP of 10-3

– case B.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1510

0

101

102

103

104

105

106

distance [m]

Ns

Tag loss: 0dB

Tag loss: 3dB

Tag loss: 5dB

Figure 1-37 Data rate considering a target BEP of 10-3

- case A.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1510

2

103

104

105

106

distance [m]

Data

Ra

te (

bp

s)

Tag loss: 0dB

Tag loss: 3dB

Tag loss: 5dB



Figure 1-38 Data rate considering a target BEP of 10-3

- case B.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1510

2

103

104

105

106

distance [m]

Data

Ra

te (

bp

s)

Tag loss: 0dB

Tag loss: 3dB

Tag loss: 5dB

Similarly, assuming from measurements that the clutter as a certain amplitude, it is possible to find, by looking at Figure 3.25 and Figure 3.26, the minimum number of required ADC bits

compliant with the requirement SQNR10dB at a given reader-tag distance.

Figure 1-39 Maximum clutter amplitude tolerable to have a minimum SQNR=10dB – case A.



Figure 1-40 Maximum clutter amplitude tolerable to have a minimum SQNR=10dB – case B.

3.4.4 UHF link budget for wake-up signals

Since the utility of the wake up signal has been underlined a simple link-budget considering a UHF 866 MHz carrier frequency with ERP=2W is shown in Figure 1-41. For the calculation 1 dB tag’s antenna gain and free-space conditions are considered. In order to understand the maximum wake up distance, a careful analysis of the minimum received power required to trigger the circuit is need.

Figure 1-41 UHF link budget.



3.5 Preliminary Performance Analysis in Real Environments

In this section we present some preliminary performance analysis based on measurements campaign realized in real indoor environments (for a more detailed description of the measurements see[4] and [11]).

3.5.1 Measurements set up

Figure 1-42 Measurements at ENSTA laboratory.

The backscattering signal from a UWB antenna was measured in a typical indoor environment at ENSTA-ParisTech laboratory. Measurements were performed in the frequency domain, by means of a vector network analyzer (VNA) in the 2-12GHz band with steps of 5MHz. Two Horn Lindgren 3117 antennas were employed as reference antennas. They were placed in a quasi-monostatic configuration, separated by 18cm, guaranteeing a high isolation between the transmit and receive channels as shown in Figure 1-42.

The scattering from a UWB Monopole Dual Feed Stripline (DFMS), which is a small planar

antenna, was measured. Its dimensions ( 3mm32440 ) make this antenna quite suitable

for RFID tags. Three different load conditions were considered.

In indoor environment, a rectangular grid of nine points as showed in Figure 1-43 spaced out of about 1m in depth and cm70 in width, was defined in a room with furniture and

having dimensions ( 2m4.495.13 ). The tag was positioned alternatively in each point on a

vertical support. For both cases, a simple data processing was performed to obtain the

antenna backscattering response from the measured 21S . The collected data was first

filtered in the frequency domain to avoid ringing effect. Then, applying the inverse Fourier transform, the signal in the time domain was derived.



Figure 1-43 Indoor scenario considered for the measurement campaign at ENSTA-ParisTech. The distances between each point and the antennas connected to the VNA are also reported.

VNA

1m

A B C

G H I

D E F

0.7m1m

A B C

G H I

D E F

0.7m

TX RX

dA=1.30m dD=2.22m

dE=2.12m

dG=3.16m

dH=3.10mdB=1.10m

dC=1.31m dF=2.26m dI=3.15m

In Figure 1-44an example of backscattered signal received from the tag placed at point H (distance 3.10m) is reported. As can be noted several clutter components (including the antenna structural mode) are present.

The second plot shows the antenna mode backscattered signal (that of interest) after clutter removal. Due to its small amplitude (about 2 orders of magnitude less than the clutter) it results to be completely buried below the clutter component.

Then clutter and the antenna structural mode scattering have a significant impact at the reader's antenna, thus making the detection of the antenna mode scattered signal (which carries data) a main issue in passive UWB RFID systems.



Figure 1-44 Example of the laboratory impulse response (grid point H, at distance of 3.1 m) and of the antenna mode contribution (after clutter removal).

3.5.2 Bit error probability analysis

3.5.2.1 Performance in AWGN and single-tag laboratory scenario

We now present the bit error probability of the UWB-RFID system as a function of the data rate

bR . The transmitted pulse shape and power have been chosen so that the transmitted

signal is compliant with the 3.1-10.6 GHz FCC mask. Specifically, the 6th derivative Gaussian

monocycle has been considered. The other parameters are 100=fT ns and the effective

isotropically radiated power is 6.70= EIRP dBm. Results related to the SPMF receiver in

AWGN channel (obtained using measurement data in anechoic chamber considering a distance 1.46=d m) and in every grid point location inside the laboratory are shown in

Figure 1-45 and Figure 1-46 respectively[10].



Figure 1-45 Bit error probability as a function of the bit rate in different tag locations. SPMF receiver is considered.

As expected, the performance of the IMF receiver is significantly better than that obtained using the simple SPMF receiver because all useful energy coming from multipath is captured and hence the performance depends only on the received power. In fact, the SPMF receiver is not able to collect the energy from multipath and suffers from pulse distortion due to propagation as well as antenna effects. The problem can be mitigated by considering more complex receiver structures, such as those based on Rake solutions. The performance obtained with the tag located at point B is better than AWGN condition because of the shorter distance (1.10 m vs 1.46 m). It can be noted that in some cases tags placed at larger

distances correspond to higher performance. This result depends on the higher amount of energy that can be collected in some locations due to the presence of richer multipath.

If we fix the target bit error probability (e.g. 3

b 10= P ), results show that data rates up to

200 kbit/s at a distance of 3.10 m with a transmitted power lower than 1 mW are feasible in

a realistic environment. It is important to remark that similar performance is achievable by the conventional UHF RFID technology only using a transmitted power higher than 1 W (i.e.,

more than 3 orders of magnitude higher level).



Figure 1-46 Bit error probability as a function of the bit rate in different tag locations. IMF is considered.

3.5.2.2 Performance in multi-tag scenario with artificial clutter

We now show the BER in the presence of interfering tags as a function of the SIR. The SNR

has been fixed to 7 dB, corresponding to an error floor of about 310 in the absence of

interference. Results have been obtained by Monte Carlo simulations starting from antenna backscatter measurements in anechoic chamber. The interfering tag, the clutter and the thermal noise signal components have been added artificially according to the set SNR, SIR, and SCR values. The clutter waveform has been taken from measurements in the laboratory environment. The worst case scenario with the interfering signal completely overlapped to the useful one is considered.

In Figure 1-47 results associated to different spreading codes are compared. In the quasi-synchronous scenario orthogonal Hadamard codes are used[10]. As expected the performance is not sensitive to the presence of both the interference and clutter (because of the zero-mean code used). In the asynchronous scenario m -sequences spreading codes of length 7 and 63 have been considered. From the curve corresponding to m -seq 7 it can be observed that when clutter is present and significative ( 79=SCR dB), even for large SIR

values, the performance is limited by the clutter. A remarkable improvement can be obtained by extending the length of the code by one (zero-mean code). When longer codes are used (e.g., m -seq 63) the impact of clutter becomes less significant and good performance can be achieved even using non zero-mean codes. These considerations suggest that the adoption of extended m -sequences is an interesting choice, especially when working with short code sequences.



Figure 1-47 BER as a function of the SIR in the anechoic chamber scenario where clutter is summed up artificially.

3.5.2.3 Performance in multi-tag laboratory scenario

In Figure 1-48 the BER as a function of the SNR when 6=tagN tags are present is reported

[10]. The signal measured from the location D of the grid is considered as the signal backscattered by the useful tag, and then we associate the backscattered signals coming from locations A, B, C, E, F (see the map in Figure 1-43) to the interfering signals. Different SNR values have been obtained by adding the correspondent artificial thermal noise.

It is possible to see the performance gain obtained when the extended m -sequence 63 is adopted. This indicates that the effect of the clutter is dominant with respect to the effect of the MUI. A similar conclusion can be drawn when only one interfering tag located in F is present and an extended m -sequence 7 is considered.



Figure 1-48 BER as a function of SNR (Laboratory scenario).The useful tag is located in point D. Five interfering tags are present in locations A,B,C,E,F when m-sequence 63 is adopted. One interferer tag in location F is considered when m-sequence 7 is used.

3.6 Concluding remarks

The frame design has been determined as regards to the tag and reader architecture and the system performance. It impacts the refresh rate of the TOA measurements, the SNR at the reader through the acceptable signal coherent integration time, and the multi-tag access capability via specific spreading codes.

Parameters value choice has to be taken as a trade off of several contrasting effects. The key parameter by means of which the performance vs data rate trade-off can be determined is the total number of pulses per symbol Ns.

The adoption of backscatter modulation and the extremely low transmitted power levels, correspond to a poor link budget that limits the transmission range. Moreover the dominant effect of the clutter imposes severe constrains on the ADC resolution.

In order to extend the transmission range, the SNR should be increased via long duration signal coherent integration. Preliminary numerical results show that operating ranges up to 15-20 meters are feasible in AWGN with a sufficiently large Ns. Good performance can also be achieved in realistic scenarios with multipath even using SPMF receivers. Several pulses per code chip are needed to minimize the effect of the tag clock drift on the TOA estimation.

Zero-mean codes allow removing the clutter components as expected. This requirement of zero-mean codes usage is less stringent adopting long codes. The code length should be well



chosen in order to mitigate the clutter impact on the ranging accuracy and to decrease the multiple access interference.

Results confirm the superior performance of orthogonal codes with respect to PN codes: the former is applicable to a quasi-synchronous system, i.e., readers and tags must be synchronized with an error not larger than the chip time. This requires the adoption of a wake-up strategy using the UHF down-link able to guarantee low wake-up time jitter.

However, a more detailed study is necessary in order to define the performance expected in case of multi tag and multi reader scenario using different types of codes.



4 Signal Processing Techniques for Ranging

The ranging technique choice is of paramount importance since it has a crucial impact on the overall system tag localization accuracy. In the SELECT framework, since the requirement on localization accuracy is stringent and the link budget of backscattered pulse is poor, a TOA-based ranging technique is adopted. In this section, we first describe the signal generation and propagation model used, then few considerations about PHY layer are developed, the receiver front-end architecture is presented. Then, the TOA estimation algorithm chosen is investigated, and finally, TOA estimation performance is showed.

4.1 Theoretical bound on TOA estimation

In an ideal scenario characterized by AWGN, the theoretical limit on the mean square TOA estimation error is given by the Cramer-Rao Lower Bound (CRLB). For a generic pulse of shape p(t) it is given by:

SNRCRLB

228

1

with

dffP

dffPf

2

22

2

)(

)(

where SNR is the signal to noise ratio per pulse and β represents the effective bandwidth of the pulse. )( fP is the spectrum of p(t). Then as can be seen, to improve the TOA estimation

accuracy (and hence ranging accuracy), it is beneficial to adopt very large bandwidth signals (e.g., UWB) and improve the SNR. The latter can be achieved by energy accumulation through the transmission of several pulses.

Figure 1-49 shows the CRLB and Ziv-Zakai bounds for TOA estimation RMSE using RRC and Gaussian pulses [65].

(4.1)



Figure 1-49 CRLB and Ziv-Zakai bounds for TOA estimation RMSE using RRC and Gaussian pulses.

4.2 Signal generation and propagation model

The signal generation and propagation model is detailed in deliverable [4], but the main features are here recalled.

The channel model is derived from antenna backscattering measurements. A backscattered UWB pulse is measured in anechoic chamber for different angle of arrival. This measured pulse waveform is then convolved with a Channel Impulse Response (CIR) which is generated using a dynamic channel model preserving IEEE 802.15.4a statistics described in [7]. In this analysis, Wire-Patch antenna measurements are used. An example of a measured backscatter pulse at 0° angle is displayed in Figure 1-50. Figure 1-51 represents an example of an industrial LOS type CIR realization. The result of the convolution of the pulse by the CIR is plotted in Figure 1-52.



Figure 1-50 Backscattered pulse measured in anechoic chamber-wire patch antenna.

-10 -5 0 5 10-0.1

-0.05

0

0.05

Delay (in ns)

Am

plitu

de

Backscattering pulse waveform

Figure 1-51 Example of a realization of the channel - dynamic channel model -CM1.

0 0.2 0.4 0.6 0.8 1 1.2 1.4

x 10-7

-0.4

-0.2

0

0.2

0.4

0.6

Delay (in s)

Am

plitu

de

Channel - Residential LOS



Figure 1-52 Example of backscattering CIR obtained with the proposed channel model.

0 50 100 150 200 250-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

Delay (in ns)

Am

plitu

de

Pulse after convolution with channel

and backscattering waveform

At the beginning of the simulation, a realization of the CIR is computed. Then, at each instant, depending on the relative location of the backscattering tag regarding the transmitting reader, the CIR evolves dynamically respecting the IEEE 802.15.4a statistics. At each instant, each ray composing the CIR is associated with the correct backscattered pulse measurement which is dependent on the angle of arrival, to result in the received signal at the reader. The overall signal path loss is proportional to the tag-reader distance at the exponent 4 (as remarked in [4]) since the signal emitted by the reader is simply backscattered by the tag before being received by the reader.

4.3 PHY layer considerations

As analyzed in section 3, the frame design is determined as regards to the system performance. It impacts the refresh rate of the TOA measurements, the SNR at the reader through the acceptable signal coherent integration time, and the multi-tag access capability via specific spreading codes.

Specifically, the preamble during which several symbols are repeated should be long enough to enable the signal synchronization process. The symbol duration during the preamble should be sufficiently extended to permit a long coherent integration time to increase the Signal to Noise Ratio (SNR). The spreading code should be used to enables tag multiple access capability and to remove the clutter. The code length should be well chosen in order to mitigate the clutter impact on the ranging accuracy and to decrease the multiple access interference as shown in section 3. Finally, several pulses per code chip are needed to minimize the effect of the tag clock drift on the TOA estimation.

An example of a frame format that could be correctly designed to meet the system requirements is displayed in Figure 1-53.



Figure 1-53Frame format example.

PreamblePreamble128 bits128 bits

ID Code128 bits

Spreading CodeSpreading Code1023 bits1023 bits

512 ms

Tbit = 2 ms

Tchip = 2 us

PRP = 256 ns8 pulses Tag Clock @ 500kHz

T Tag Clock ≈ T Chip

PreamblePreamble128 bits128 bits

ID Code128 bits

Spreading CodeSpreading Code1023 bits1023 bits

512 ms

Tbit = 2 ms

Tchip = 2 us

PRP = 256 ns8 pulses Tag Clock @ 500kHz

T Tag Clock ≈ T Chip

4.4 Receiver architecture

The proposed receiver front-end is based on the architecture of a UWB low power receiver developed at CEA-LETI. The architecture of this RF front-end is detailed in the following.

The incoming signal is first down-converted to base-band and low pass filtered. A second mixer is then used to translate the signal on an intermediate frequency. This operation is done in order to project the signal on a sinusoidal basis, which makes a refined TOA estimation possible. Successively, the signal is integrated over one IF sinusoid period, which is here equal to 2ns. The four channel output signals II, IQ, QI, QQ are finally digitized with a 5bits ADC at 62.5 MHz. The actual ADC resolution appears to be not sufficient to satisfy the requirements shown in section 3.3.4. Then the possibility to extend/change the ADC will be investigated in the following up research activity.

The index of the integrators, i.e. the time position of the integration window, must be controlled by an external digital circuit. The windows are 50% overlapped (i.e. one start of 2 ns window integration every 1 ns). Additionally, hardware constraints impose to have at ones disposal four windows available per 64 ns period.



Figure 1-54 UWB RF front-end architecture.

LNA

Low pass filter

tunable cut-off frequency:

250 1200 MHz

Low pass filter

tunable cut-off frequency:

250 1200 MHz

ADC5 bits

ADC5 bits

II

IQ°

°

Indexes of the 2ns integration

windows

62.5 MHz

4 GHz

62.5 MHz

°

°

ADC5 bits

ADC5 bits

QI

QQ

Indexes of the 2ns integration

windows

62.5 MHz

62.5 MHz

°

°

500 MHz

The four digitized sampled signals - II, IQ, QI, QQ - are correlated with the reference code at digital processing level, and then squared and summed up together. From this operation, the signal energy present in each integration windows is computed. A threshold detection test over the energy signal present in each integrated windows enables the signal coarse TOA estimation.

4.5 Synchronization process

Before being able to estimate the TOA of the signal, the frame synchronization has to be realized. The synchronization is done during the preamble of the frame. The aim is to determine in which time windows are located the signal pulses and which window corresponds to the start of the data payload which is here the tag’s ID code.

Taking into account the RF Front-end hardware constraints, the synchronization algorithm performs circular correlation of the received signal with the reference code (with a given tag-reader code dn cn). The highest value of this correlation and its position (window index) is stored if this value exceeds a threshold. During this processing, for each integration window position within a pulse repetition period (i.e. code period), a new circular correlation is computed and compared to the threshold. The process stops when 2 same positions for windows index have been found in two successive laps of the algorithm.

4.6 TOA Estimation algorithms

In the SELECT framework, harsh signal propagation conditions have to be faced. The received pulse signal which energy is low due to the backscattering is affected by noise, dense multipath and strong clutter. Even in LOS conditions, the TOA-based ranging can be biased. Thus, specific TOA estimation algorithm has to be employed.



The TOA estimation is done in two steps. First, a coarse estimation that determines the integration windows index corresponding to the TOA, leading to a 1ns precision. Second, a refined TOA estimation is performed within the window, leading to a theoretical 125 ps precision. 125 ps is the precision limit induced by the hardware design of the analog front-end and the digital signal processing implementation choices (e.g. atan look-up table implementation, etc....

4.6.1 TOA coarse estimation

The TOA estimation algorithm described in [19] appears to be of particular interest in our context. This leading edge TOA signal estimation algorithm has demonstrated good multipath mitigation performance ([18], [19]). The main features of this algorithm are illustrated in Figure 1-55.

This detection test algorithm is done on the signal energy kz where,

kz is expressed as

follows:

2222

kkkkk QQQIIQIIz

where II, IQ, QI, QQ, are the RF front-end digitized output samples after correlation with the reference code mentioned in previous section.

Each rectangular bin in Figure 1-55 represents a k-index window of energykz .

Once the channel is estimated, the highest energy window has to be found. The TOA search process begins from the index of this highest energy window. It continues backward on the sample index scale, till the following dual condition: the energy of the current tested sample exceeds a threshold and the D successive following samples has energy below this threshold.

Analytically, the window index corresponding to the estimated leading edge TOA is expressed by the relation:

SBWnDkk

kSB

TOA zzand

zWnnnkn

max,max1

max1maxmax

,....max

/,....,,max

where

is the threshold to which are compared the energy samples

SBW is the index window (i.e. number of index) which limits the backward search

maxnis the index of the highest signal energy window

D is the number of samples backwardly tested successive to the current tested

energy sample

(4.2)

(4.3)



Figure 1-55 Serial Backward Search for Multiple Cluster TOA algorithm principles illustration.

1 2 3 Sample indexK

Threshold ξ

nTOA nmax

Wsb : search back window

1 2 3 Sample indexK

Threshold ξ

nTOA nmax

Wsb : search back window

The threshold adopted is given by [19]:

edD

FAed PQ

11 11

where Q() is the Gaussian Q-function, ed is the energy signal

kz standard deviation

ed is the mean of the energy signal kz .

FAP is the false alarm probability

The value of the search back window SBW is linked to the statistics of the channel. In our

case, it has been chosen equal to the RMS delay spread. The chosen value for the parameter D, result of simulations, is chosen equal to 7. As discussed in [18], these parameters are typically determined in an empirical way. The false alarm probability is an application-dependent parameter. Typically values ranges from 0.1% to 1%.

It has been shown in [18] that, for high SNR values, a simple threshold comparison algorithm can lead to higher estimate accuracy than the SBS-MC algorithm. Consequently, these two algorithms are combined. Along the simulations, the SNR is estimated. If the SNR exceeds a fixed value (in our case chosen to 25 dB), the simple threshold comparison output is used as the TOA estimate. If the SNR is below this fixed value, the SBS-MC algorithm is used as the TOA estimate.

4.6.2 TOA refined estimation

The front-end architecture presented in 4.4 enables with the presence of the second mixer onto an IF, the computation of a refined estimate of the TOA. Once the TOA coarse estimate is obtained, the refined estimate is computed within the corresponding window of index k as follows:

2222

2222

arctan

arctan

kkkk

k

k

kkkk

k

k

FINE

QIQQIIIQifQI

QQ

QIQQIIIQifII

IQ

TOA

(4.4)

(4.5)



4.7 Performance Analysis in the Reference Scenario

It is assumed that the reference scenario is defined by tags located in a square cell of 10m x 10m area with four readers at the corner of this square at 2m height. In this context, TOA estimation simulation has been done for a tag-reader distance from 1m to 12m. The signal generation and propagation model of section 4.2 has been used. Two IEEE802.15.4a channel has been considered: Residential LOS (CM1) and Industrial LOS (CM7). The front-end architecture presented in section 4.4 is modeled and employed in the simulation. The frame synchronization process is assumed to be successfully realized. The TOA estimation is done as described in section 4.6.1 and 4.6.2.

The emitted signal power is equal to -10dBm. The length of the spreading code is fixed to Nc=1023 chips with Npc=8 pulses per chip, and it is assumed that the coherent integration is realized over the duration corresponding to the length of the code.

It is assumed that no tag clock drift affects the TOA estimation process and no quantization is done on the received backscattered signal. These impairments will be analyzed in the next research activity, however these results are still important as they provide a sort of benchmark which different implementation can be compared to.

In this section, since the simulation are done for a tag considered static, for each realization of the TOA estimation, a new IEEE802.15.4a channel independent from the previous ones has been drawn.

Two metrics have been retained: the localization error outage and the ranging error standard deviation.

4.7.1 Ranging error outage

The ranging error outage is a metric defining the integrity of the process. It can also be used here to characterize the ranging estimation performance. This metric is expressed by:

thexeREO )(Pr

with e(x) being the TOA estimate error.

eth is a fixed ranging error threshold

It represents the probability for the ranging error to exceed a fixed error threshold.

Figure 1-56 shows the error outage resulting of a simulation using the CM1 channel for several tag-reader distances. For each tag-reader distance from 1m to 12m, the tag is set static and 2000 realizations of the ranging estimation process have been drawn.

(4.6)



Figure 1-56 Ranging error outage as a function of the error threshold for CM1 channel.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

0

20

40

60

80

100

error threshold - th

(in m)

ran

gin

g e

rro

r o

uta

ge

(in

%)

ranging error outage function of error threshold - CM1 Channel

Tx-Rx distance = 1m

Tx-Rx distance = 2m

Tx-Rx distance = 3m

Tx-Rx distance = 4m

Tx-Rx distance = 5m

Tx-Rx distance = 6m

Tx-Rx distance = 7m

Tx-Rx distance = 8m

Tx-Rx distance = 9m

Tx-Rx distance = 10m



It can be noticed here that for a tag-reader distance below 9m, a 25% REO is achieved for an estimate error below 20 cm. For tag-reader distance values higher than 9m, the error increases significantly.

Figure 1-57 shows the error outage resulting of a simulation using the CM7 channel for several tag-reader distances. For this Industrial LOS channel simulation, it can be observed that the performance is much decreased. A 25% REO is achieved for an estimate error below 45 cm for a tag-reader distance below 8m.



Figure 1-57 Error outage as a function of the error threshold for CM7 channel.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

10

20

30

40

50

60

70

80

90

100


(in m)

Ra

ng

ing

Err

or

ou

tag

e -

LE

O (

in %

)ranging error outage function of error threshold - CM7 channel

Tx-Rx distance = 1m

Tx-Rx distance = 2m

Tx-Rx distance = 3m

Tx-Rx distance = 4m

Tx-Rx distance = 5m

Tx-Rx distance = 6m

Tx-Rx distance = 7m

Tx-Rx distance = 8m

Tx-Rx distance = 9m




4.7.2 Ranging error standard deviation

For this simulation, the tag-reader distance varies from 1m to 13m. Each step of 1m represents 500 realizations of the ranging estimation process.

The Root Mean Square Error (RMSE) of the TOA-based ranging estimation has been plotted for Residential LOS (CM1) and Industrial LOS (CM7) on Figure 1-58.

It can be noticed that the TOA-based ranging error stays below 30 cm in the operational range of [0 m, 8 m]. However, it is expected that the presence of quantization effects would degrade the performance.



Figure 1-58 Ranging RMSE as a function of the tag-reader distance for CM1 and CM7 channels.

2 4 6 8 10 120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

TOA estimation RMSE

Tag-Reader distance (m)

Ra

ng

ing

err

or

sta

nd

ard

de

via

tio

n (

m)

CM1

CM7

4.7.3 Concluding remarks

In this section, the CEA-LETI analog front-end design has been described. The digital signal processing envisioned to be developed in this framework has been detailed. The performance of the TOA-based ranging estimation has been analyzed.

It can be concluded that for a CM1 channel, the SELECT requirement in terms of accuracy is almost fulfilled. For CM7 channel, Industrial LOS channel, it is not the case, and improvements in terms of ranging estimation still have to be made (optimization of the estimation thresholds, etc…) to meet the requirements even if the localization/tracking module will obviously enable to enhance the location accuracy.



5 Signal processing Techniques for Localization

This section describes models, algorithms, and performance results for localization and tracking of tagged and untagged objects in single cell and multicell scenarios of interest.

5.1 Preliminaries

Network localization and navigation represents a new paradigm where wireless networks support data communication, contextual information collection, and navigation. The purpose of such networks is to determine the unknown positions of agents based on intra- and inter-node measurements. In conventional approaches such as in the global positioning system (GPS), each agent infers its position using only measurements made with respect to anchors (nodes with a priori position knowledge belonging to the infrastructure). Such systems usually fail in harsh environments where network coverage is limited and measurements behavior is highly complex [61]. The localization and navigation process typically consists of two phases: (i) a measurement phase, during which agents make intra- and inter-node measurements using different sensors; (ii) a location-update phase, during which agents infer their own positions using an algorithm that incorporates both prior knowledge of their positions and new measurements. For instance, an agent can update its position estimate based on inertial measurements using an inertial measurement unit (IMU) and distance measurements with respect to some fixed anchors using a range measurement unit (RMU). The localization accuracy strongly depends on the quality of the measurements, which are affected by impairments such as network topology, multipath propagation, environmental conditions, interference, noise, and clock drift. In addition to the underlying technologies used in the measurement phase, the localization performance is also dependent on the specific processing or data fusion algorithm used in the location-update phase. Hence, a deep understanding of information evolution in different phases of the localization process is necessary for the design and analysis of localization systems.

5.2 Localization performance metrics

The requirements of location-aware networks are driven by applications. A local performance metric is the position estimation error (localization error) given by the Euclidean distance between the estimated position and the true position as

.

The root mean squared error (RMSE) of position estimates is then given by

where and are the estimated and true positions respectively.

A global performance metric evaluated over the entire localization area and time is the localization error outage (LEO) defined by

(5.1)

(5.2)

(5.3)



where is the target (i.e., the maximum allowable) position estimation error, and the probability is evaluated over the ensemble of all possible spatial positions and time instants. Other requirements include localization update rate (i.e., the number of position estimates computed per second) and coverage area of the localization system. In particular, localization update rate is important for navigation (navigation of pedestrians and vehicles typically requires different localization update rates), which drives algorithm complexity and node cost.

5.3 Theoretical Foundation

Understanding the fundamentals theoretical limits (performance bounds) of localization and navigation systems is important not only to provide performance benchmarks when assessing the performance of any practical algorithms, but also to guide algorithm development and network design. In this section we illustrate a general framework for the evaluation of the theoretical bounds in a generic localization scenario where nodes with unknown positions (we refer to agents for the sake of generality) try to estimate their position. To be as general as possible, we consider the possibility that agents could also cooperate each other [62], [63], [64].

Consider a network with fixed nodes (anchors) and agents, where each agent is equipped with multiple sensors that can provide intra- and inter-node measurements (e.g., using IMU and RMU, respectively) for the purpose of localization and navigation. Using these intra- and

inter-node measurements, represented by , the agents aim to infer their

positions . The accuracy of location estimates is inherently limited due to random phenomena affecting , and fundamental limits of such accuracy have been derived using the information inequality [61], [64].

For a static network, or a dynamic network at a given time instant, only the spatial cooperation among the agents can be exploited. By using the notion of equivalent Fisher information matrix (EFIM)[65], the squared position error for agent is bounded by

where is the EFIM for [as depicted in Fig. 5.1(b)], and are the

expectation and trace operators, respectively, and denotes the square submatrix on the diagonal corresponding to . The right-hand side is referred to as the squared position error

bound (SPEB). It can be shown that the EFIM is a sum of two parts, the localization information from anchors (show as block-diagonal matrices consisting of ’s in Fig. 5.1) and that from agents’ spatial cooperation (shown as the structured matrix consisting of ’s in Fig.

5.1). The basic building blocks of the EFIM represent the localization information

between pairs of nodes in the form , where is the unit vector with direction given

(5.4)



by denoting the angle from one node to the other, and is a positive scalar that characterizes the ranging information intensity (RII) [65]. The value of depends on the ranging technique as well as the power and bandwidth of the received waveform, multipath propagation, and prior statistical channel knowledge. In particular, the RII is proportional to the square of the effective bandwidth [65]. Moreover, the localization information from anchors can be expressed in a canonical form as a weighted sum of “one-dimensional” information from individual anchors; while cooperation always improves the localization accuracy since it adds a positive semi-definite matrix to the EFIM corresponding to non-cooperative localization. It was shown in [65]that the accuracy of localization is affected by two factors: the quality of point-to-point measurements, reflected by the expression of RII,

and the network topology, reflected by the block structure of the matrix . In the

absence of cooperation, every corresponding to cooperation between nodes and

becomes , resulting in a total EFIM with block-diagonal structure [as depicted in Fig. 5.1]. In such cases, localization information for different agents are uncorrelated, and the SPEB can be calculated using only local information at the agents.

Building on the understanding of cooperative localization, we now discuss the case of cooperative navigation where agents in a dynamic network cooperate in both space and time domains. For each time instant, the contribution of cooperation in space is similar to what we have seen in cooperative localization. In addition, another layer of cooperation in time, exploiting intra-node measurements and mobility (dynamic) models, yields new information for navigation. Such information is characterized by matrices in the total EFIM

as depicted in Fig. 5.1, where consists of the positions of all agents from time 1 to . Consequently, the SPEB for each agent at a given time instant can be obtained by a formula similar to (1). Some observations can be drawn from the structure of the EFIM for cooperative navigation in Fig. 5.1. First, the total EFIM consists of two major components, the cooperation in space as well as in time. The former characterizes the localization information from inter-node measurements in the entire network at each time instant (shown as the diagonal block matrices); and the latter characterizes the information from intra-node measurements and mobility models at each individual agent (shown as components outside the main block-diagonal). Second, since intra-node measurements and the mobility models for different agents are independent, the corresponding matrices form a block diagonal matrix in the upper-right and lower-left quarter of the total EFIM. Third, the components can be viewed as the temporal link which connects localization information from spatial cooperation of the previous time instant to the current one. If the temporal link is not available (i.e., components are zero) then the total EFIM is block diagonal, implying that position inference is independent from time to time. The structure of the EFIM for cooperative navigation allows a recursive method to calculate the EFIM at each time instant. This view also provides insights into the information evolution of spatio-temporal cooperation in cooperative navigation.



Figure 1-59 EFIM structures and corresponding Bayesian networks for three agents: (a) noncooperative localization, (b) spatial cooperation, and (c) spatio-temporal cooperation for two time steps.

17

K 1

K 2

K 3

K 1

K 2

K 3

−C12

−C12

−C13

−C13

−C23

−C23

−C112

−C112

−C113

−C113

−C123

−C123

−C212

−C212

−C213

−C213

−C223

−C223

K 11

K 12

K 13

K 21

K 22

K 23

T 121

T 121

T 122

T 122

T 123

T 123

x1

x1

x1

x1

x2

x2

x2

x2

x3

x3

x3

x3

z1

z1

z1

z1

z2

z2

z2

z2

z3

z3

z3

z3

z12

z12

z12

z13

z13

z13

z21

z21

z23

z23

z23

z32

z32

z32

z31

z31

z31

z11

z22

z33

z21

Time 1 Time 2

Time 1

Time 2

(a) Noncooperative

(b) Spatial

(c) Spatio-temporal

localization

cooperation

cooperation

Fig. 2. EFIM structures and corresponding Bayesian networks for three agents: (a) noncooperative localization, (b) spatial

cooperation, and (c) spatio-temporal cooperation for two time steps.

5.4 Reference scenarios for localization and tracking

As discussed in[2], two different scenarios have been considered for the localization and tracking. First, a single cell squared scenario of dimension 10 m with four readers located in the corners has been considered for the localization of a single tagged object. Two multicells scenarios are considered for the tracking, such as deterministic trajectory (conveyor belt) and stochastic trajectory (random walking). These scenarios are composed of a certain number of squared cells of dimension 5 m with four readers in the corners of each cell, as depicted in Figure 5.3.

The conveyor belt shown in Figure 5.4 is defined by four points of coordinates in meters A={-10,-15}, B={-10,0}, C={10,0}, D={10,15} a Cartesian coordinate system having origin in the center of the multicell scenario is assumed. The random walking coordinates at time k

are statistically determined as

where R is the localization update rate and

(5.5)



and, for instance, θ = π or θ = π/3. We denoted with the Gaussian distribution with mean µ and standard deviation σ, and with U[a,b] the uniform distribution in the interval [a,b].An example of random walking is shown in Figure 5.5.

Figure 1-60 Single cell scenario.

Figure 1-61Conveyor Belt.

Figure 1-62 Multicell scenario.

Figure 1-63Random Walking.

(5.6)



5.5 Localization and Tracking techniques

The task of network localization and tracking algorithms is to determine positions from measurements (observations) and prior knowledge. For TOA –based localization and tracking techniques, the objects positions are determined from TOA estimations of signals exchanged with readers. The main difference between the case of tagged and that of untagged objects is on TOA estimation which depends on hardware and signal processing. At a layer above on top of TOA estimation localization and tracking techniques operate independently of the nature of the objects. Hence, in the following we will summarize localization and tracking techniques in an unified way for tagged and untagged objects.

5.5.1 TOA estimation model for localization and tracking algorithms

The measured TOA for the transmitter to target to receiver path is affected by an error caused by additive white Gaussian noise and the propagation environment in which the system is operating. In general, we can model the TOA error as a Gaussian distributed

random variable where the mean takes into account the presence of positive bias in obstructed path conditions, and it can be considered zero in line-of-sight (LOS) conditions[65]. The standard deviation depends on the propagation environment, hardware dependent factors altering TOA measurements and received SNR. The estimated TOA results in

The presence of TOA error makes the real target position not yet belonging to the detection ellipse/circle for each transmitter/receiver pair. Thus, detection ellipses/circles intersection, if still existing, define a wrong position estimation resulting in a localization error. To conduct a performance analysis, the knowledge of the relation between and is needed. The function on is in general non-increasing and can be defined as

The function depends on the hardware solution for TOA estimation. If an energy detector is employed[65], it is possible to distinguish three different SNR values intervals. The regions of small and large SNR values respectively correspond to regions of large and

small standard deviation . The SNR at a single receiver is given by

(5.7)

(5.8)



where is the number of pulses combined to perform a TOA estimation, is the power received per pulse, is the pulse repetition frequency (PRF), and denotes the one-side noise power spectral density (PSD).

For moderate SNR values a transition zone of standard deviation between the upper and lower levels is experienced. The upper and lower levels of respectively depend on the

observation and integration time of the energy detector as follows

Figure 5.6 shows a real example of suggested by measurements.

Figure 1-64 Example of derived in the FP7 European Project EUWB.

We now obtain the received SNR for a generic scenario of NT transmitter and NR receivers. In the case of monostatic readers, transmitters and receivers are co-located.

5.5.2 Maximum Likelihood Localization

Once ranging measurements are conducted at each receiver detecting the target, the estimated position is determined by localization algorithms elaborating the measurements. Thus, the localization accuracy depends on the localization algorithm adopted. In the following we will focus on maximum likelihood (ML) estimator that select the estimated target position maximizing a TOA likelihood function which depends on the statistics of TOA

(5.9)

(5.10)



estimation error. The ML algorithm examines all the possible target position and, for Gaussian distributed TOA estimation error, determines the estimated position as follows

In addition, measurements can be refined contrasting the presence of non-zero mean errors (e.g. bias caused by the presence of walls) and reducing the impact of zero mean noise by averaging over several measurements as in active localization systems [66]. In many cases the ML approach could lead to high complexity algorithms, the often sub-optimal schemes may be preferred [29].

5.5.3 Tracking: Bayesian filters

Recursive Bayesian estimation is a general probabilistic approach for estimating an unknown probability density function (PDF) over time using incoming measurements and a mathematical process model. It enables the probabilistic estimation of a system dynamic state from noisy observations. The state can be a simple 2D position or a complex vector including, for instance, 3D position, pitch, roll, yaw, linear and rotational velocities [67]. We consider the estimation of the 2D position of the target (system state) at a given time slot ,

represented by the random variable (RV) , starting from inter-node intra-node

measurements (observations). Denote the set of position-dependent measurements collected at time slot . The time interval between 2 consecutive

observations is . At each time slot, a probability distribution over , called belief,

, represents the uncertainty. The Bayesian filter sequentially estimates such beliefs over the state space conditioned on all information contained in the sensed data. To

illustrate, we denote the sequence of time-indexed sensor observations . The

belief is then defined by the posterior density over the random variable conditioned on all sensor data available at time

.

From a posteriori PDF it is possible to determine the most probable state at time

given the history of the observations . The complexity grows with the time, because the number of measurements of the sensors grows over time unless a window of a given number of previous states is considered. To make the computation tractable, we assume that the dynamic system is a first-order Markov system, that is the window length is one and

the current state variable contains all relevant information for position updating. For locating objects, the Markov assumption implies that sensor measurements depend only on the current physical location of the agent and that the location at time depends only on

the previous state . Given the at time , the Belief at time is

(5.11)

(5.12)

(5.13)



where the term is the mobility model for the agent, that gives the

probability to be in given the fact that it was in . Once a new observation

(measurement) is available at time , and the state estimation is updated as

where . Now we can calculate the new Belief function

where is the perceptual model which provides the probability to observe

given the position . The structure of Bayesian filter is reported in Figure 1-65. For location estimation, the perceptual model is usually considered a property of a given sensor technology and captures an error characteristics of the sensor. The implementation of

Bayesian filters requires the specification of the perceptual model , mobility

model , and the computation of the belief . Different

implementations of Bayesian filters differ in the representation of PDF for each state In particular, we consider the efficient suboptimal implementation via Particle Filter (PF).

Figure 1-65 Structure of a Bayesian filter.

(5.14)

(5.15)



Mobility Models:

A common way to model the mobility uncertainties is through the Gaussian distribution, i.e.,

where the variance accounts for the uncertainty in the movements and depends

on as will be shown in the following. We evaluate two mobility models which will be adopted in the numerical results to asses the performance of the tracking algorithm.

Mobility without direction information M1: in this case the PDF corresponds to a Gaussian distribution centered in

where it is assumed that the speed intensity is known and the speed direction is uniformly

distributed between .

Mobility with speed learning M2: in this model the speed is evaluated from previously estimated positions in a sliding window of length

Perceptual models

Assuming a 2D motion of the object and considering independent identically distributed observations, the perceptual model can be defined as

where is the observation at time . The perceptual model for each observation is assumed Gaussian distributed, that is,

(5.16)

(5.17)

(5.18)

(5.19)

(5.20)



where is the position of anchor node from which the measure comes. The

variance depends on the technology used for localization. For example the model introduced in section 5.5.1 can be adopted.

5.5.4 Tracking: Particle filters

PFs compute the Belief update according to a sampling procedure, which is often referred to as sequential importance sampling with re-sampling. The key advantage of PFs is their ability to represent arbitrary PDF efficiently by automatically focusing their resources (particles) on regions in state space with high probability. The PF implementation requires attention when applied to high-dimensional estimation problem since the complexity grows exponentially with the dimension of the state space.

PFs are based on sets of samples weighted distributed according to the belief

approximated as

where is the number of particles, is the weight for particle at time , and

. The quality of the approximation depends on the number of particles [67]. To decrease the complexity, a non-uniform distribution of samples can be considered (e.g., more dense in the regions where it is more probable that the object is located). The sequential importance sampling (SIS) technique allows to extract samples from a PDF based on their importance. The a posteriori PDF is then approximated by weighted samples as

where is the Delta pseudo-function.

Since the is not known, a proposal distribution is chosen such that it can be factorized

and allows a recursive computation of the weights

(5.21)

(5.22)

(5.23)

(5.24)



PFs typically adopt the following approximation

which transforms (1) into

The SIS algorithm has a drawback: after several iterations only few particles have a weight which is not negligible [68].To overcome this problem a resampling is done aiming to delete the inconsistent samples (those who have negligible weights) and increase the number of particles with high weights. This procedure is known as sequential importance resampling (SIR).

5.5.5 Tracking: Extended Kalman Filter

The Extended Kalman Filter (EKF) yields to optimal solutions of parameter estimation problem in a linear Gaussian context. In our framework (non linear, non Gaussian channel), Extended Kalman filtering is sub-optimal but remains still a very efficient solution for localization and tracking objects. We assume that the location tracking of the tag is performed in two dimensions.

The state (associated to the object) estimation is realized through a classical two step prediction-correction process. This implies to set an evolution model for the state, to define which the system’s observables are, and how the state and the observables are linked. It is detailed in the following developments how the EKF formalism can be adapted to our location estimation context.

The state model equation is given by:

kkk WXFX 1

where kX is the state vector describing the dynamic state of the tag at instant k

F is the state transition matrix from instant k-1 to instant k, setting the dynamic linear evolution model

kW is the state noise vector

The state vector is expressed as follows:

Tk

Y

k

X

k

X

k

X

kk

k AAVVyx ][)()()()()()(X

(5.25)

(5.26)

(5.27)

(5.28)



where )()( , kk yx , )()(,

k

X

k

X VV , and )()(,

k

Y

k

X AA are respectively the position , speed,

and acceleration components of the tag.

The state noise vector is given by:

Tk

A

k

A

k

V

k

V

k

y

k

xk YXYXwwwwww ][

)()()()()()(W

where v

k

y

k

x Nww ,0,)()( is the position coordinate noise; v

k

V

k

V NwwYX

,0,)()( is

the speed coordinate noise, and a

k

A

k

A NwwYX

,0,)()( is the acceleration coordinate

noise. It is assumed that these noise processes are independent, and thus not correlated. Therefore, the state covariance matrix is diagonal, given by:

A

A

V

V

P

P

T

kkk E

0

0

WWQ

The state transition matrix F represents the predicted evolution of the state, and the noise

process kW is the incertitude relative to this prediction.

In our implementation, the state transition matrix F is expressed by:

100000

010000

01000

001002

10010

02

1001

2

2

dt

dt

dtdt

dtdt

F

where dt is the refresh rate of the filter.

The Kalman filter implements an observation vector with an associated observation model.

The observation vector kZ is composed of system measurement which enables the

correction of the predicted state.

The observation equation is expressed as:

kkk H VXZ )(

where kV represents the measurement noise vector at instant k.

In the chosen configuration, observations are composed of four values obtained from four TOAs, which are measured with respect to the four closest readers positioned at known positions. Thus, the observation vector is :

Tk

R

k

R

k

R

k

Rk zzzz ][)(

4

)(

3

)(

2

)(

1Z

(5.29)

(5.30)

(5.31)

(5.32)

(5.33)



Where )(k

Rizis the round-trip time-of-flight measurement at instant k of the i-th reader’s

transmitted signal.

The non-linear function )(H in the observation equation links the state to each

measurement via the following relation:

)(2)()(2)()()( 1

,4,...,1k

Ri

k

Ri

kk

Ri

kk

Ri vyyxxc

zi

where )()(,

k

Ri

k

Ri yx are the coordinates of the i-th reader, )(k

Riv is the

measurement noise process assumed Gaussian and centered with the diagonal measurement covariance matrix being given by:

4

3

2

1

z

z

z

z

T

kkk E

0

0

VVR

In the chosen implementation, the standard deviation of the TOA estimation error given by a simplified model, which has been empirically fitted to simulations :

CRLBi mzi ,4,...,1

m represents the intrinsic precision of the TOA estimation associated with to the

receiver architecture. In our application: m = 125 ps (fixed parameter since it

represents the intrinsic limitation of the hardware). is tuned regarding simulation results

of section 4. The CRLB expression is given in section 4.1 where 2 is computed numerically

according to measured received pulses in anechoic chamber.

We assume that the state noise and observation noise are independent processes, and that

the initial state 0X is a vector independent of kV and kW .

We define 1/ˆ

kkX as an a priori state prediction at step k given knowledge of the process

from instant 0 to instant k-1, and kk /X̂ as an a posteriori state estimate at step k given

observations at instant k.

The a priori estimate error covariance matrix is defined by :

])ˆ()ˆ[( 1/1/1/

T

kkkkkkkk E XXXXP

Similarly, the a posteriori estimate error covariance matrix is given by :

])ˆ()ˆ[( ///

T

kkkkkkkk E XXXXP

The non-linear measurement function )(H is linearized around 1/ˆ

kkX through a Taylor

series, assuming that the derivate components from 2nd order are negligible:

(5.34)

(5.35)

(5.36)

(5.37)

(5.38)

(5.39)



)ˆ()ˆ()( 1/1/ kkkkkkk HH XXHXX

Where the components of index (i,j)of the Jacobian matrix kH are expressed as:

1/ˆ

][

][],[

kkk

j

k

iji

k

H

XXX

H

Once the measurement model linearized, a linear formulation of the observation equation is obtained:

k

kkkkkkkkk H

V

XHXVXHZ~

1/1/ˆ)ˆ(

As mentioned before, the Kalman filtering is a two steps estimation algorithm: the prediction step, and correction step. The main equations governing the Kalman Filtering are reminded hereafter:

State prediction at next instant:

1/11/ˆˆ

kkkk XFX

A priori estimate error covariance:

11/11/ k

T

kkkk QFPFP

Kalman gain update:

1

1/1/

k

T

kkkk

T

kkkk RHPHHPK

Correction of the state prediction with the knowledge of measurement:

1/1//ˆˆˆ

kkkkkkkkk XHZKXX

A posteriori state estimate covariance:

1/1// kkkkkkkk PHKPP

Due to the harsh environment with poor link budget and dense multipath considered in the SELECT applicative context, the propagation channel can lead to inaccurate and biased ranging estimation. The TOA estimation algorithm (section 4) has been chosen in order to mitigate the multipath impact, but the TOA estimate can still be sometimes biased,e.g. due to false or late/missed detections. Moreover, depending on the position, on the mobility of the tag and on the channel, several successive TOA measurements are erroneous.

For high SNR values, these errors are generally centered on a value corresponding to a secondary path, leading to a high bias but with a low deviation. For, low SNR values, the errors are mostly centered but exhibits a high deviation.

Such measurements can lead the EKF to result in biased position estimates. Thus, to mitigate these effects, a monitoring of the measurements has to be performed. The

observation vector kZ at instant k, is compared to the stored observation vector 1kZ at

instant k-1. If the difference between these two successive measurement vectors exceeds a

threshold linked to the mobility of the tag, the measurement kZ is discarded and the EKF is

(5.40)

(5.41)

(5.42)

(5.43)

(5.44)

(5.45)

(5.46)



fed with the last good measurement 1kZ . In order to avoid feeding the EKF with the same

measurement for a long duration, a counter is incremented each time that a measurement vector is discarded and that the last good measurement is used. When this counter reaches a typical value (preliminary empirically set), this system is reset, the counter is set again at zero, and the incoming measurements are taken into consideration.

The threshold for comparison between last and current measurement is expressed by:

2

maxmax2

11LLm TATV

c with lmRL NRT 1

where :

maxV is the maximum speed of the considered scenario

maxA is the maximum acceleration

LT is the time from the last good measurement considered

RR is the measurement refresh rate

lmN is the counter value mentioned above

The state model is based on the general dynamic equation. This state model can not predict maneuvers leading to abrupt direction changes that can appear in the trajectory of the tag. Such sudden direction changes can lead the tracking of the EKF to loose lock, the filter being then divergent from the real trajectory.

To cope with these abrupt direction changes, an EKF innovation monitoring is implemented. At each instant, the innovation computed out of the EKF process is compared to a threshold. If K successive innovation values exceed this specific threshold, a direction change is

detected, and then the state noise covariance kQ is increased and the estimate error

covariance kP is reinitialized. This method enables the EKF to loosen its state model when

direction change is detected and to converge again once the maneuvers is finished.

The threshold is expressed by:

2

maxmax2

11RRI RARV

c

5.6 Detection and Localization of Untagged objects

A wireless sensor radar (WSR) network is generally composed of a set of nodes that cooperates to detect and localize a target passive object. This set is composed of a subset of nodes that emit a sequence of pulses and a subset of receiver nodes that

(5.47)

(5.48)



receive reflections by the target object and obstacles present in the propagation environment.

We assume a Cartesian coordinate system where and

with and represent the positions of the transmitter

and the receiver. For a target in the transmitter to target to receiver path length is

where and are the transmitter to target

(incident) and target to receiver (scattered) path lengths, respectively; is the speed of

light; is the time-of-arrival (TOA) estimation at the receiver for a signal emitted by the transmitter after scattering from the target. If the TOA is exactly known and there is a

deterministic relation between distance and received power, it is easy to verify that the

target is on an ellipse, called detection ellipse, with foci in the transmitter and the receiver coordinates.

Target position coincides with the intersection between detection ellipses. The coverage area of each transmitter/receiver pair depends on the minimum signal-to-noise ratio required to satisfy the receiver sensitivity.

Detection and localization performance depends on the received power. Thus, it is necessary to derive the radar equation for UWB waveforms in realistic environment such as the IEEE 802.15.4a channel model.

In particular, the PSD of the received signal is given by

where is the transmitted PSD that feed the transmitting antenna; and are the transmitter and receiver antenna efficiencies, respectively; and are

the solid angles subtended between transmitter and target and target and receiver, respectively; is the reference distance (typically 1 m); is the reference frequency (typically the central frequency of the transmitted signal spectrum); is the path-loss exponent; is the frequency decaying factor; is the path-loss at and .

From (3), the received power from the transmitter to target to receiver path can be written as

(5.49)

(5.50)

(5.51)



where is the frequency range of the transmitted signal PSD, and ; is the frequency dependent radar cross section (RCS). To derive an analytical tool for

the analysis and design of a WSR network, in the following we will assume constant RCS.

To ensure reliable target detection and localization accuracy, a minimum (threshold) SNR at the receiver, , must be guaranteed. From (2) and (4), the corresponding minimum

(threshold) received power, , provides a maximum distance product that limits the transmitter/receiver coverage

The area in which backscattered signals satisfy the SNR requirement is called coverage area of the transmitter/receiver pair which is the locus of point of coordinate satisfying the following inequality

Depending on whether transmitters and receivers are co-located or not the system is referred to as monostatic or multistatic radar network. For the nominal scenario that will be considered in the case study we will refer to the monostatic case for which ,

and with . Thus, detection ellipses degenerate in circumferences. It is easy to verify that the coverage area correspond to a maximum Cassini oval or a maximum circumference for the multistatic and monostatic radar, respectively.

Differently from the monostatic case, in a multistatic WSR a single pulse transmission causes two received pulse: the direct one via transmitter to receiver path and the reflected one via transmitter to target to receiver path. Then, a temporal separation between the two pulses is needed to resolve multipath and it is called minimum resolvable delay . Thus, a target

can be localized if that is the locus of points outside an ellipse called minimum ellipse.

We define the coverage area of the transmitter/ receiver pair that correspond to the maximum circumference or Cassini oval for the monostatic or multistatic scenario respectively and a function indicating if a point is in as

where the indicator function is if is true, and otherwise.

Let be the surveillance area (SA) and a function indicating if a point is in as

(5.52)

(5.53)

(5.54)

(5.55)



For the multistatic scenario let be the area outside the minimum ellipse and a function indicating if a point is in as

It follows that in a monostatic WSR network the coverage area for the radar is . Differently, is the coverage area for the transmitter

and receiver pair for a multistatic WSR network.

Since a target can be detected only if it is inside the coverage area of at least one transmitter/receiver pair, the detection probability for the WSR is given by

where stands for the summation with boolean OR rule; denotes the expected value of with respect to all possible target positions ; is the set of active pairs

transmitter/ receiver such that the signal received from the path transmitter to target to receiver is above the receiver sensitivity. To simplify the notation we consider

for all . By denoting with the area of the region , that is

, for target position uniformly distributed inside the SA (10) can be

rewritten as

We now determine the probability that a target is detectable by transmitter/receiver pairs. To do this, it is essential to understand which part of the SA is covered by and only receivers, that is the area covered at least by receivers minus the area covered by at least

receivers.

The area covered by receivers is the union of areas covered by each -ple

(there are possible -ples).

We define the area covered by at least receivers as

(5.56)

(5.57)



Therefore, the area covered by receivers results in

To avoid ambiguities, in the presence of perfect TOA estimation, target localization is possible if at least three receivers detect the target ( ) and then the target position coincides with the intersection of the respective three detection ellipses. The probability of localization without ambiguities depend on the coverage area of three receivers at least that we call the localization area without ambiguities of the system . The localization probability results in

In the case of uniform probability of target location inside the SA, the localization probability becomes

Detection and localization probability for the monostatic and multistatic cases are shown respectively in Figures 5.8, 5.9, 5.10 and 5.11 by assuming the ratio between the transmitted

PSD, St and the receiver sensitivity for each transmitting node ranging from 10Hz-1 to

100Hz-1 with step of 10Hz-1.

Figure 1-66 Monostatic radar detection probability

Figure 1-67 Multistatic radar detection probability

3 4 5 6 70.0

0.2

0.4

0.6

0.8

1.0

Number of Sensors

Pro

babi

lity

ofD

etec

tion

(5.58)

(5.59)

(5.60)

(5.61)

St/

St/



Figure 1-68 Multistatic radar localization probability

Figure 1-69 Multistatic radar localization probability

3 4 5 6 70.0

0.2

0.4

0.6

0.8

1.0

Number of Sensors

Pro

babi

lity

ofL

ocal

izat

ion

By assuming the same transmitting PSD per node the solution with monostatic reader provide better detection and localization probabilities despite the lower complexity.

5.7 Localization Performance for static tagged and untagged objects

We now present localization accuracy for static objects in single a single cell scenario (Figure 5.2). The results aim to show relation between localization accuracy and TOA estimation characteristics. This can drive in hardware and signal processing design for TOA estimation toward the fulfillment of localization requirements. In particular, we consider ML localization algorithm and the parameters setting reported in the following.

The UWB transmitted signal has PSD over the frequency range with and bandwidth W=1.7 GHz that is the European UWB Lower

Band.

The number of received pulses coherently summed is with frequency of pulse repetition . Omnidirectional antennas with efficiencies

are considered together with a RCS constant over the signal bandwidth. The

reference distance is set to , and .

Being a nominal scenario representing best possible conditions, the path-loss exponent has been chosen equal to 2 as for free space propagation. The minimum receiver sensitivity is . Note that with the chosen parameters the localization area covers entirely the SA. Figure 5.12(b) shows the localization area of the WSR when

while 5.12(a) shows the received SNR for a single radar as target position varies in the SA. To evaluate the effects of TOA error standard deviation on the localization error, in Figure 5.13 the LEO is plotted for different and a target located in the center of the SA. For example, a LEO of for a target localization error of

is reached for a TOA error standard deviation not grater than . The corresponding localization error for this case is shown in Figure 5.14 where a number of estimated positions are shown.

Then we consider various target positions in the SA. We fix the relation between TOA error standard deviation and SNR at the receiver as shown in Figure 5.6 where the equation (20) is

PSD/ PSD/



considered with the following values:

. Figure 5.15

shows LEO calculated with random position in the SA shifting the values of and of . For example, a LEO of is reached for a target localization error of , and when , respectively. The corresponding localization error is shown in Figure 5.16 where real target positions and estimated target position are compared with an arrow starting from true position and ending at estimated position of the target.

Figure 1-70 Reference scenario with four monostatic radars at the corner of the SA.

Figure 1-71 LEO for 100 target located in the SA. Position estimation is averaged on 100 measurements.

(a) SNR at the receiver of a single radar

(b) Localization area without ambiguities for different St

= 1ns = 0.1ns = 10ns = 100ns



Figure 1-72 Position estimation for a target located in the center of the SA

Figure 1-73 LEO for different values of ΔSNR and 100 targets located in the SA.

Figure 1-74 Localization error for 100 random targets located in the SA.



5.8 Tracking Performance for dynamic tagged and untagged objects

We now present tracking accuracy for dynamic objects in multicell scenario (Figure 5.3). The results aim to show relation between tracking accuracy and TOA estimation characteristics. This can drive in hardware and signal processing design for TOA estimation toward the fulfillment of localization requirements. Both deterministic and stochastic trajectories are considered. The deterministic case is given by the conveyor belt shown in Figure 5.4 and the stochastic case is given by the random walking (Figure 5.5). In particular, we consider PF and EKF algorithms (described in Section 5.6).

5.8.1 Particle Filter Tracking

In the case of conveyor belt, the vector of velocity is known and the localization update becomes a ML over the trajectory. In Figure 5.17, 5.18, 5.19, 5.20, 5.21, 5.22 true and estimated trajectories and LEO for the ML tracking are shown varying the localization update rate R. For instance, for eth = 20 cm the outage probability Po is about 25% for the case with v=0.5 m/s, R=10Hz and mobility model M2. We obtained an RMSE of 0.17 m , 0.34m , and 1.4m for R=10Hz,5 Hz and 1 Hz, respectively.

Figure 1-75 True and estimated trajectories Figure 1-76 LEO for R =10 Hz

for R = 10 Hz

0.0 0.1 0.2 0.3 0.4 0.5 0.6

0.0

0.2

0.4

0.6

0.8

1.0

eth

Po



Figure 1-77 True and estimated trajectory for R = 5 Hz Figure 1-78 LEO for R =5 Hz

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0.0

0.2

0.4

0.6

0.8

1.0

eth

Po

Figure 1-79 True and estimated trajectory for R = 1 Hz Figure 1-80 LEO for R =1 Hz

0 1 2 3 4

0.2

0.4

0.6

0.8

1.0

eth

Po

We now consider the random walking case. A comparison between the true and the estimated trajectories have been obtained for the PF tracking varying the tag speed, the localization update rate and the mobility model (M1, M2 described above). In particular, Figures 5.23, 5.24, 5.25 refers to the case of tag speed v= 0.5 m/s and localization update rate of R=10 Hz. Figures 5.26, 5.27, 5.28 refers to v=0.5 m/s and R=1 Hz. For the case with v=1 m/s and R=1 Hz results are shown in Figures 5.29, 5.30, 5.31. Figures 5.32, 5.323 5.34 refers to v=3 m/s and R=10 Hz. In general the performance improve as the localization update rate increases. Table 5.1 shows the RMSE values for different random walking obtained varying the localization update rate, mobility model and tag speed. For instance, for eth = 20 cm the outage probability Po is about 50% for the case with v=0.5 m/s, R=10Hz and mobility model M2.



Figure 1-81 True and estimated trajectory for R = 10 Hz, v =0.5 m/s and mobility model M1.


2

Figure 1-83 LEO for R = 10 Hz, v =0.5 m/s and mobility model M1 (blue) and M2 (purple).

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0.0

0.2

0.4

0.6

0.8

1.0

eth

Po





Figure 1-86 LEO for R = 1 Hz, v =0.5 m/s and mobility model M1(blue), M2 (purple).

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

eth

Po



Figure 1-87 True and estimated trajectory for R = 10 Hz, v =1 m/s and mobility model M1.


Figure 1-89 LEO for R = 10 Hz, v =1 m/s and mobility model M1(blue), M2 (blue).

0.0 0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

0.8

1.0

eth

Po





Figure 1-92 LEO for R = 10 Hz, v =3 m/s and mobility model M1 (blue), M2 (purple).

0.0 0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

0.8

1.0

eth

Po

Table 1-8 Localization RMSE for random walking, PF algorithm and mobility model M1 and M2 for various speed and localization update rate.

RMSE [m]

Speed

R

3 m/s 1 m/s 0.5 m/s

M1 M2 M1 M2 M1 M2

10 Hz 0.39 0.35 0.33 0.28 0.25 0.23

1 Hz 0.53 0.45 0.56 0.55 0.52 0.5



5.8.2 Extended Kalman Filter Tracking

In this section, localization and position tracking simulation results are given using the extended Kalman filter. First, the capacity of our EKF implementation to track specific deterministic tag trajectories is illustrated.

Then, performance of our algorithm evaluated over stochastic trajectories is provided for two cases: static tag configuration, moving tag configuration. In all configurations, it is considered that the tag is in LOS conditions with all the readers.

Two tag trajectories have been evaluated. In the first scenario, the tag is moving within a square room of 10m x 10m with only four readers at the corners. The maximum speed is 2m.s-1, and the refresh rate is set to 10Hz. The random initial guess is centered around the true position with a uniform distribution over [0 m, 3 m]. The initial speed is set randomly centered around the true speed with a uniform distribution on [0 m.s-1, 1 m.s-1]. The TOA measurements composing the observation vector results of the TOA estimation process (section 4). We recall here that a backscattered pulse measured in anechoic chamber convoluted to an IEEE802.15.4a compliant dynamic model has been used for the simulations. The signal’s angle of arrival has been taken into account in this model. A delay and path loss corresponding to the transmitter-tag distance has been applied to the signal.

Figure 1-93Example of 2D positioning estimation simulation result.

-2 0 2 4 6 8 10 12-2

0

2

4

6

8

10

12

2D display of the trajectory

X coordinate (in m)

Y c

oo

rdin

ate

(in

m)

Real trajectory

Estimated trajectory

Reader 1

Reader 2

Reader 3

Reader 4



Figure 1-94Position estimation instantaneous error.

0 1 2 3 4 5 6 7 8 9 10-0.5

0

0.5

1

X i

nsta

nta

neo

us e

rro

r (i

n m

)

simulation time (in s)

Position estimation error

0 1 2 3 4 5 6 7 8 9 10-0.2

0

0.2

0.4

0.6

Y i

nsta

nta

neo

us e

rro

r (i

n m

)


In the example shown on Figure 5-35, it can be noticed that the estimated trajectory matches well the real trajectory. For this simulation, the Root Mean Square Error for the position estimation along the trajectory once the filter has converged is 22.6cm.

For Eth=20cm, which is the targeted precision, the Localization Error Outage (LEO) equals 32.3%. For Eth=26,5cm, the LEO reaches 25%.

The second simulation corresponds to a scenario for which the tag is moving in a 30m x 30m area within a grid of readers positioned at each corners of smaller cells of size 5m x 5m.

The tag is moving a rectilinear trajectory with two direction changes. The maximum speed is 3m.s-1, the refresh rate is set to 10Hz. The random initial guess is centered around the true position with a uniform distribution over [0 m, 3 m]. The initial speed is set randomly centered around the true speed with a uniform distribution on [0 m.s-1, 1 m.s-1].



Figure 1-95 Example of rectilinear piece-wise trajectory in a 30mx30m area, comprising a continuous grid of 5m x 5m readers

0 5 10 15 20 25 30

0

5

10

15

20

25

30

X coordinate (in m)

Y c

oo

rdin

ate

(in

m)

2D display of the trajectory

Real trajectory

Estimated trajectory

Readers

Figure 1-96 Instantaneous errors on estimated tag coordinates

0 5 10 15 20 25-3

-2

-1

0

1

X i

nsta

nta

neo

us e

rro

r (i

n m

)


Position estimation error

0 5 10 15 20 25-0.5

0

0.5

1

Y i

nsta

nta

neo

us e

rro

r (i

n m

)


In this new example as well, it can be noticed that the EKF converges rapidly towards the correct tag position and succeeds in tracking the location of the tag. Even when the tag changes its direction, the filter enables the tracking of the position after some new convergence time.

For this simulation, for Eth=20cm, the LEO equals 33.4%. For Eth=22.7cm, the LEO reaches 25%.

The RMSE of the position estimation computed along the trajectory is equal to 23 cm.

In this section, random tag static positions have been generated to evaluate the performance of the position tracking algorithm. The tags are located within a 10mx10m cell,



and their positions are random. 500 realizations for each tag position estimates have been processed, for 50 different tag positions, and this for three different refresh rate values. The initial conditions and initial state for the EKF are the same as previous simulations.

Figure 5-39, giving the LEO for the simulation for three different refresh rate show that the LEO in the static case hardly depends on the refresh rate.

Figure 1-97 LEO for random static tag position

0 0.5 1 1.5 2 2.5 30

10

20

30

40

50

60

70

80

90

100


(in cm)

Lo

cali

zati

on

Err

or

ou

tag

e -

LE

O (

in %

)

Refresh rate = 0.1s

Refresh rate = 0.2s

Refresh rate = 0.3s

Table 1-9 LEO and average RMSE results for static stochastic tag position estimation summarizes the results for this set of simulations in terms of LEO and RMSE. The RMSE mentioned herein is computed for the total number of tag position estimates, and averaged for all the generated tag positions. The average RMSE is also not strongly dependent on the refresh rate in a static tag configuration.

Table 1-9 LEO and average RMSE results for static stochastic tag position estimation

Refresh rate = 0.1s Refresh rate = 0.2s Refresh rate = 0.3s

LEO=25% εth = 0.29 εth = 0.31 εth = 0.34

Average RMSE 0.24 0.27 0.31

In this section, random tag trajectories have been generated to evaluate the performance of the position tracking algorithm. For this simulations set, the tag is moving in a 30m x 30m area with readers positioned at each corners of 5m x 5m cells. Several maximum tag speed values have been tested. The impact of the refresh rate has also been estimated (with three different values of refresh rates). The trajectory of the tag follows a Gaussian random walk defined in section 5.1 which is recalled hereafter.

Current tag position coordinate are set via a Gaussian distribution as expressed by:



))),(cos()1(()( xR tRVtxNtx

))),(sin()1(()( yR tRVtyNty

)(t is uniformly distributed over the interval ],[ , and in our simulations )(t is

constant over 15 RR . The initial tag position is randomly set (with uniform distribution)

within the 30mx30m area.

For each simulation, the RMSE computed along each trajectory has been averaged over all the trajectories.

The simulation results are presented in Table 1-10 and Table 1-11.

Table 1-10 Average RMSE for dynamic stochastic tag trajectory tracking for different values of refresh rate

Vmax=1 Refresh period = 0.01s Refresh period = 0.5s Refresh period = 1s

Average RMSE 0.23 0.64 0.82

Table 1-11 Average RMSE for dynamic stochastic tag trajectory tracking for different values of tag maximum speedy

Rr = 0.2s Vmax=1m.s-1 Vmax=2m.s-1 Vmax=3m.s-1 Vmax=4m.s-1

Average RMSE 0.3 0.37 0.38 0.43

The RMSE increases dramatically with the refresh period. Contrarily to the static scenario, the refresh rate impacts strongly the estimation error of the EKF since the correction step is done more rarely.

As expected, the RMSE increases also with the maximum speed but less significantly. This is due to the fact that the information of the maximum speed is known as an a priori parameter in the EKF.


The EKF implementation developed here, which is a feasible solution with regards to the complexity, seems very promising and exhibits performances very close to the SELECT system requirements. Although some features of this localization/tracking algorithm, like for example the innovation monitoring, should be improved.



6 Coexistence and Interference Mitigation Techniques

UWB-based systems are intended to operate as underlying technology in frequency bands already occupied by other wireless systems (e.g., WiMax)[30][31]. Therefore the interference is in general present and might degrade the performance of the system. Detection and avoidance (DAA) techniques represent a useful approach to deal with coexistence issues. They can be viewed as one enabling technology for the cognitive radio paradigm [33][34]. In addition the EU spectrum usage regulations in the UWB band require the adoption of DAA techniques [32]. Spectrum sensing, i.e., the detection of used portions of the spectrum, is the first step needed to have DAA schemes working properly. Starting from the sensed spectrum the reader can adapt its transmitted waveform in order to fit spectrum holes and hence to avoid overlapping with other wireless systems operating in the same area. Since the tag is a passive device, no adaptation is required at the tag side and all sensing and avoidance schemes have to be implemented at the reader. DAA aspects will not be implemented in the SELECT test bed, although they will be analyzed from the theoretical point of view.

6.1 Preliminary analysis of spectrum sensing techniques

In this section we address the detection phase of the DAA process. Many spectrum sensing algorithms can be adopted in order to perform signal detection. In particular the algorithms proposed in the recent literature on cognitive radio [33], [34]. In the following review the most promising techniques emphasizing their pros and cons. 6.1.1 Overview of the spectrum sensing algorithms

From the classical detection theory it is known that the optimum detector, i.e. the detector which decision metric has the maximum SNR before threshold comparison, is the matched filter (MF) (section 6.1.2). This detector provides the best detection performance, but has the disadvantage that it requires the knowledge of the signal to detect, condition that in general in not satisfied. However MF-based detectors can be adopted, when the signal is only partially known, e.g. using known preamble sequences. The optimum detector when the signal is unknown is the energy detector (ED). This detector estimates the received energy in the band of interest and compares it to a threshold that is related to the noise power level. The ED has a low computational complexity and is widely used because it has a simple implementation. The main disadvantage of the ED is that it requires knowledge the noise power to properly set the threshold, which depends on the receiver and the environment properties. This requirement is often critical, in particular in low SNR environments, in which an imperfect knowledge of the noise power can cause severe performance losses. Moreover the ED has cannot distinguish between interference and signal. ED is described in 6.1.3. The ED detector can be considered a blind detection algorithm, in the sense that it does not require any knowledge about the signal to be detected. However it requires the knowledge



of the noise power. Completely blind detection algorithms can be developed studying the autocovariance properties of the received signal. This algorithms does not require any a priori knowledge, but are based on the observation of some correlation properties in the received waveforms. Therefore specific solutions such as oversampling or the adoption of multiple antennas at the receiver are required. Typically these algorithms imply a high computational complexity. In section 6.1.4 three algorithms based on eigenvalues of the sample autocovariance matrix of the received samples are presented. When the signal to be detected has some known characteristics, the detection of such features is an effective method to identify such kind of signal. In particular we address the detection of orthogonal frequency division multiplexing (OFDM) signals, that today are widely adopted in the modern communication systems. OFDM signals can be detected through the correlation induced by the presence of the cycle prefix (CP) of the OFDM symbols. In section 6.1.5 an algorithm to detect OFDM signals is described.

The previous sensing methods mentioned, except the MF, are all detection algorithms, then their objective is a simple binary decision on the presence or absence of signals in the observed band. The cyclostationary analysis instead is a technique that can be adopted for signal classification. All man-made signals from a statistical point of view are cyclostationary, because they are generated through specific operations that contain inner periodicities (sampling, numerical modulations, presence of carriers, etc.). The cyclostationary analysis is an extent of the traditional spectral analysis, that can disclose the inner periodicities in the observation. Each signal has its characteristic features, that can be used to recognized the particular transmission and/or extract its parameters. Moreover these techniques allow to separate between signal and noise components. This promising sensing methods require however high computational and implementation requirements. The basics of cyclostationary analysis are described in section 6.1.6.

6.1.2 Matched Filter Based Detection

The matched filter (MF) is known to be the optimum detector of the transmitted signal, in the sense that it maximize the SNR at the output of a linear filter used to compute the detection metric [33], [50], [51]. Assume that the M samples observed by the MF are

yn = xn + wn, Mn ,,, 21 (6.1)

Where xn is the transmitted signal samples, and wn are the noise samples, realization of as additive white Gaussian (AWGN) process.

Let us assume that the MF impulse response vector is given by

T

N21 ) h,,h,(h= h (6.2)

where

(×)T

denote the transposition, and the coefficients are given by

hn = xM -n+1

*

. (6.3)



Then the MF requires the knowledge of the receiver waveforms. Figure 1-98shows the block diagram of MF detector.

Figure 1-98 Principle of MF based detection.

The output of the MF, T, can be expressed as

T = ynhM -n+1

n=1

M

å

(6.4)

Substituting (6.1) and (6.3) in (6.4) we obtain

ynxn

* = | xn |2

n=1

M

å +n=1

M

å wnxn

*

n=1

M

å.

(6.5)

From the classical detection and estimation theory it is known that, if the number of samples observed is large, the MF statistic above can be considered a Gaussian distributed random variable (RV) [52].

The MF detector requires short observation intervals to achieve a good detection performance. Anyway it is an ideal detector, that cannot be adopted in a CR scenario in which the cognitive user has not the knowledge of the primary interfere waveform.

However we mentioned the MF because its performance can be adopted as reference, being the optimal detector. Moreover other correlation-based detection algorithms (often called waveform-based detectors [53]) in which, e.g., only a portion of the transmitted signal, such as preamble scan be derived from the MF.

6.1.3 Energy Detection

The energy detector (ED) is the most popular method used to detect signals. It consists in estimating the received energy and comparing it with a threshold that is proportional to the noise power in the observed band. After the pass band filter, with pass band B (that can be narrower than the bandwidth of the transmitted signal), the received signal r(t) is observed for T seconds, downconverted and sampled with sampling frequency

fs = 1/B, obtaining the

vector of the received samples

),,,( Nyyy 21y (6.6)

where N = TB is the number of the samples take into account for detection.

Only two hypothesis are possible, absence of a signal into the band B (H0), or presence of at least one signal (H1):

NiwxyH

wyH

iii

ii,,, ,

:

:21

1

0

(6.7)



where

xi is the i-th sample of the transmitted signal (that eventually comprises the channel

effect), and the i-th noise sample of noise is assumed to be circularly symmetric complex Gaussian (CSCG):

wi ~ CN (0,2s 2).

The ratio between the energy of the signal and the two-sided noise power spectral density (that is a dimensionless metric) is given by [35]

2B

s 2[r(t)]2 dt »

1

s 2

0

T

ò | yi |2

i=1

N

å . (6.8)

The ideal ED test that we consider is

N

i

H

H

iyN 1

2

2

1

0

11

||)(y (6.9)

where

x is the detection threshold. Assume that the signal samples are

xi ~CN (0,2S) and

the signal to noise ratio is

SNR= S s 2.

The ideal ED metric in (6.9) assumes a Chi-squared distribution in both the hypothesis [37], but for high values of N, it can be approximated as a Gaussian random variable. Typically the threshold is chosen using the Neyman-Pearson criterion, i.e. taking the value that in (6.9) provides a desired PFA.

Generally the variance of the noise is unknown and variable, so the test (6.9) is not useful. It’s possible to fix this problem replacing

s 2 with an estimate of the noise power

ˆ s 2. This version of ED, called ENP-ED (Estimate Noise Power-ED), present the following test

N

i

H

H

iyN 1

2

2

1

0

11

||

ˆ)(y (6.10)

To estimate the noise power, the optimum solution is to implement the maximum likelihood (ML) estimator

ˆ s 2 =1

M| wi |2

i=1

M

å . (6.11)

To estimate this, it’s necessary to have a band and a time window where there is no signal. In alternative, the noise-only samples can be obtained from the same band, from a previous detection interval.

The statistic of the ENP-ED are related to the F-distribution, that for large number of samples can be approximated with a Gaussian curve [36].

6.1.4 Eigenvalue based detection

In this section describe three algorithms based on the eigenvalues of the autocovariance matrix of the received signal:

Energy – Minimum Eigenvalue ratio detector (EME), based on the ratio of the received energy in the observed band and the minimum eigenvalue of the autocovariance matrix of the received samples [39];



Maximum – Minimum Eigenvalues ratio detector (MME), based on the ratio of the maximum and the minimum eigenvalues of the autocovariance matrix [38], [39];

Information Theoretic Criterion (ITC) detector, based on the ratio of the arithmetic and geometric mean of the eigenvalues of the autocovariance matrix [40];

If the received signal is sampled at the Nyquist frequency, we obtain uncorrelated samples. The eigenvalue-based detectors instead are based on the analysis of correlated observations; therefore specific strategies must be adopted. For single user systems the key strategy can be the adoption of oversampling or the use of multiple antennas. In case of distributed detection, observations from different CR users can be combined in order to exploit their mutual correlation.

These algorithms are based on the properties of the eigenvalues of the autocovariance matrix. In the observed samples are noise-only samples, the all eigenvalues will be equal to the noise power. Otherwise if the signal is present, it will introduce some degree of correlation in the autocovariance matrix. In particular there will be eigenvalues greater than the noise power level. These properties are exploited by the EME, MME and AGM detectors for spectrum sensing.

6.1.4.1 Computation of the sample autocovariance matrix

For the multiple antennas case, with K receiving antennas, at the sampling time t the CR user receives a K-length vector of samples )( ty . Then the sample autocorrelation matrix, that is

an estimate of the true autocovariance matrix, can be simply computed as

1

0

1 sN

ns

sy nnN

N )()()( *yyR . (6.12)

In the oversampling-based case the sample autocovariance matrix can be computed averaging the products )(*)( tt yy , where the vectors )( ty are overlapped sequences

extracted from the received samples flow. E.g. using an oversampling factor M=2, we can study the ML-length vector as shown in Figure 1-99. Then, as in the multiple antenna case, the sample autocovariance matrix is computed averaging on

sN products.



Figure 1-99 Vector extraction for the calculation of the sample covariance matrix. Oversampling factor M=2.

6.1.4.2 Energy – Minimum Eigenvalue detection

The EME metric consists in the ratio between the estimated received energy )( sNT (that

can be computed as maximum likelihood estimate, as for the ED, or as average on the eigenvalues) and the smallest eigenvalue of the sample covariance matrix

ML

EME

H

HML

sNT

1

0

)( (6.13)

Under H0 both the numerator and the denominator of the metric above are estimate of the

noise power; then the ratio will be approximately 1. Under 1H the numerator will exceed

the denominator. To study the performance and the design of the EME detector, the statistic of Wishart matrices can be adopted [38].

6.1.4.3 Maximum – Minimum Eigenvalue detection

To improve the performance of the EME algorithm, we can replace the estimate of the received energy in (5.2) with the maximum eigenvalue of

Ry(Ns)[38], [39]. The system

model is the same as the one described for EME detector.

In the hypothesis

H0 then we have

ML 21 and

l1 /lML = 1. In the hypothesis

H1

generally it’s verify that

l1 /lML > 1. Hence the test (5.2) can now turn in

MME

H

HML

1

0

1

. (6.14)

To study this detector we can use a semi-asymptotic technique to set the threshold as in [38] or statistics of the maximum [41], [42], and minimum (in its limit forms) eigenvalues [43]. In alternative, a new exact method to set the threshold has been proposed in [39]. This algorithm exploits the expression found by Chiani and Zanella of the joint distribution of an arbitrary subset or ordered eigenvalues of a Wishart matrix.



6.1.4.4 ITC-based detection

A different algorithm based on the eigenvalues of the sample covariance matrix (SCM) has been proposed in [40]. This algorithm has been originally developed to count the number of the transmitting signals, and then extend to perform the detection task. The algorithm is based on the computation the ratio between the arithmetic and geometric means of the eigenvalues of the SCM.

Let

NT be the number of signal transmitted, and

NR the number of receiving antennas with

TR NN . Then the

NR - NT smaller eigenvalues of the autocovariance matrix

R, are equal

to the noise power. The aim of the algorithm is to determine the multiplicity of the minimum eigenvalue of the sample covariance matrix

ˆ R observing the received signal for

NS time

instants [45].

The problem of finding the number of signal can be described as a model selection problem, in which we must choose among

kmax models [40]

max

)( ,, ,)),((ˆ;logminargˆ kkNkLk S

k

k0 Y (6.15)

where

ˆ q (k) is the ML estimate of the parameters vector )(

)(

)()()( ,,,k

k

kkk

21 and

u(k) is the number of the free-adjusted parameters. The penalty function is defined as

SS Nk

NkL log)(

)),((2

. (6.16)

In our problem the parameters vector is the (k+1)-length vector [40]

),,,()( 2

1 k

k (6.17)

where 2 is the noise power and k 1

are the eigenvalues of the autocovariance matrix

different from 2 .

From this algorithm that estimates the number of signals present in the observed band, we

can extract a detector, that decides for 1H if 1k̂ . Otherwise, it decides for

H0.

6.1.5 OFDM autocorrelation based Detection

OFDM is used in various applications such as digital television, audio broadcasting, wireless networking and broadband internet access. In particular, it is adopted by BWA services complying the WiMAX standards. The presence of a cyclic prefix (CP) gives OFDM signals the property that the autocorrelation coefficients are nonzero at delays

t = Td, where

Td is the

number of samples corresponding to useful symbol length.

An OFDM signal is a sum of narrowband subcarriers typically modulated by using PSK or QAM. Without loss of generality below we assume sampling factor equal to 1, hence

Td also

represents the number of subcarriers for the OFDM system.

Let

Tc be the number of CP symbols 1 dcd TcTTc ,, , added in front of each OFDM

symbol. The total OFDM symbol length is therefore

Td + Tc.



)(),(),(,,, 1101 ddcd TcccTcTTc (6.18)

Figure 1-100CP in a OFDM symbol.

The aim of the algorithm is to detect the presence of an OFDM signal exploiting the autocorrelation due to the CP. We define the autocorrelation coefficient as

r(t) =E y(t)y(t + t)*[ ]

E y(t)y(t)*[ ] (6.19)

that for a lag

t = Td becomes

H0 : r = r(+Td ) = r(-Td ) = 0

H1 : r = r(+Td ) = r(-Td ) = r1

ì í î

. (6.20)

where

122

2

1

SNR

SNR

TT

T

TT

T

dc

c

wx

x

dc

c

(6.21)

and

SNR= s x

2 s w

2 .

Observing K consecutive OFDM symbols it is possible to create a vector )(,),(),( 110 dTMyyy where

M = K(Td+Tc) >> Td.

Omitting the intermediate steps described in [46] it’s possible to reach the Log-Likelihood Ratio Test (LLRT)

1

0

H

H

ML

ˆ (6.22)

where

ˆ r ML is the ML estimate of the autocorrelation coefficient based on the received signal

ˆ r ML =

1

2MÂ y(t)y*(t + Td ){ }

t =0

M -1

å

ˆ s z2

(6.23)

And

ˆ s z2 is the ML estimate of the received signal power

ˆ s z2 =

1

2(M + Td )| y(t) |2

t =0

M +Td -1

å . (6.24)



This algorithm requires the knowledge of

Td that is not known a-priori. But since it generally

assumes only few possible values (e.g., 2 or 3) it is possible to compute

ˆ r ML for each possible

Td and choose the maximum.

Figure 1-101 Comparison between perfect synchronization and no synchronization.

Time synchronization is not required since the autocorrelation coefficient is estimated by all samples into K OFDM symbols. If there’s no synchronization the performances are the same as consider only K-1 symbol than the case of perfect synchronization (Figure 1-101). For

K >>1 performances are the same. However the performance of the AC detector can be severely compromised due to frequency offset. Therefore frequency offset correction is required.

Note that the AC algorithm, as detector specific for OFDM signals, can be also considered as identification algorithm for such kind of modulation scheme.

6.1.6 Cyclistationary-based detection

From a statistical point of view all man-made signals are cyclostationary, because they are generated through specific operations that contain inner periodicities (sampling, numerical modulations, presence of carriers, etc.). The cyclostationary analysis is an extent of the traditional spectral analysis, that can disclose the inner periodicities of the observation. Each signal has its characteristic features, that depends on its typical periodicities such as carrier frequency, symbol rate, etc., and can be used to recognize the particular transmission. Noise instead does not present inner periodicities, and therefore cyclostationary analysis is useful for distinguish between signal and noise. In this section we analyze cyclostationary features that can be used both for detection and classification purposes.

The cyclic spectrum represents essentially the correlation between two spectral components of the observed waveform, and the cyclic frequency is the frequency gap between these components. Then it is a bi-dimensional support function. Cyclic-spectrum is an interesting spectral analysis tool, because some contributes than occupy the same spectral bands, present distinct features in the cyclic frequency domain, and then in theory can be detected without interfering with each other.

In particular AWGN is concentrated on the zero cyclic frequency, and does not affect other regions. The cyclostationary signal theory has been described e.g. in [56].



Here we introduce a method to realize the cyclic spectrum analyzer. A detailed analysis of cyclic spectrum analyzers can be found in[54], [55], [58]. All these methods are based on the cyclic periodogram

(6.25)

where

(6.26)

is the so called short time Fourier transform of the process X(t), that is the Fourier transform in[t-T/2, t +T/2] and X(t) is the input signal.

Eq. (6.25) leads to two approaches: time-averaging method and frequency-averaging method:

1. the time-averaging (or smoothing) method:

(6.27)

2. the frequency-averaging method:

(6.28)

It can be demonstrated that these two methods are equivalent if ΔtΔf >>1 [56]. Obviously, the limit operations in (6.27) and (6.28), are always approximated in an experimental approach; these methods produce only an estimation of the real cyclic spectrum, that will be more accurate as the observation time and the smoothing amount increase.

The simplest implementation of a cyclic spectrum estimator so called time-smoothing periodogram, that is based on (6.28), removing the limit operations. Its discrete-time version is given by:

(6.29)

in which T = 1/Δf and <.>Δt is simply a time average operation on a time interval Δt. The discrete time version of XT (t; f) is:

(6.30)



where a(n) is a generic window. The time-smoothing periodogram at frequency f0 and cycle frequencyα0 can be computed as

(6.31)

where f1 = f0 + α0/2 and f2 = f0 - α0/2, and g(n) is a generic window. Replacing (6.30) into (6.31) yields:

(6.32)

where m(q, r) is called the kernel of the transformation and is given by:

(6.33)

The kernel of the system is the more important function in the project of an efficient cyclic spectrum analyzer [54], [55], [57], [58].

6.1.6.1 Decision statistics

Cyclostationary detectors are typically based on the implementation of a cyclic spectrum estimator. As we mentioned above, cyclostationarity is essentially related to correlation in the frequency domain. AWGN is uncorrelated, therefore its cyclic spectrum has no significant components for 0 , therefore from a detection point of view if a cyclic components (for 0 ) are detected, then a signal is present.

If the CR user knows that the signal to be detected has a cyclostationary component at a

specific cyclic frequency, ̂ , the cyclic spectrum can be computed only for ̂ ; )(ˆ ˆ fS X

. In

this case we are talking about a single cycle detector [59]. The decision metric in this case

can be the maximum, in the spectral frequency domain, of the absolute value of )(ˆ ˆ fS X

.

The single cycle detector can be extended to the multi-cycle case that can be adopted if the CR user knows a set of possible cyclic frequencies in which the signal to be detected has significant cyclostationary components.

In a more general case, the CR user must scan all the cyclic frequency domain because it the characteristic cyclic frequencies of the secondary user are not known, or in order to detect other interferers present. In this situation a possible decision test could be

|),(|maxarg,

fST Xf

.

Alternatively the cyclostationarity of a signal can be analyzed observing its estimated CAF [60].


In the following table the main advantages and disadvantages of each spectrum sensing schemes are summarized and compared.



Table 1-12 Pros. and Cons. of the sensing algorithms studied.

Pros. Cons.

Waveform-based detector

Identification capabilities; Specific detector;

Synchronization problems;

ED / ENP-ED Good performance;

Simple;

Universal;

The noise variance must be known or estimated;

OFDM-based detector Identification of OFDM signals;

Good performance with long observation time;


Performance losses if the CP length is unknown;

Cyclostationary-based detector

Good performance with long observation time;

Identification/classification

Requires oversampling


Knowledge of the cycle frequencies is required;

Long observation time;

EME detector Blind detector Requires oversampling / cooperation / multiple antennas;

Poor estimation of the noise variance;

MME detector Blind detector;

Good performances;

Requires oversampling / cooperation / multiple antennas;

ITC-based detector Blind detector;

Even good performances;

Requires oversampling / cooperation / multiple antennas;

6.2 Preliminary analysis of the LDC constraints

The ELECTRONIC COMMUNICATIONS COMMITTEE decisions on Low Duty Cycle (LDC) constraints on UWB devices operating in the 3.1 to 4.8 GHz band are given in [69]. An extract of this decision is given hereafter.

During the elaboration of the ECC decision, several mitigation techniques were considered in order to increase the maximum EIRP in the band 3.1 to 4.8 GHz to a level sufficient to enable viable UWB operation in this band whilst ensuring the protection of the Radio Services. Low duty cycle (LDC) mitigation implemented on UWB devices has been identified as one possibility allowing coexistence with radio communication services.



UWB devices implementing LDC will be permitted to operate at a level of -41.3dBm/MHz in the frequency band 3.4 to 4.8 GHz with the following requirements:

Ton max = 5 ms Toff mean ≥ 38 ms (averaged over 1 sec) Σ Toff > 950 ms per second

Σ Ton < 5% per second and 0.5% per hour

Ton is defined as the duration of a burst irrespective of the number of pulses contained.

Toff is defined as the time interval between two consecutive bursts when the UWB emission is kept idle.

If Ton is considered as the frame emission time, the LDC constraint could not be acceptable for the SELECT system. Indeed, the duration limit of 5ms is not compatible with a frame minimum length of several tens of milliseconds, which is mandatory to achieve the aimed performance.

The hypothesis has been done herein that Ton is referring to the pulse emission time, and the analysis below is based on this hypothesis.

Table 1-14 Number of PRP per frame maximum for a given refresh rate in order to respect the requirements

Number of PRP per frame maximum

Refresh rate = 0.5 Hz 5001216 (e.g. 16x1221x256)

Refresh rate = 1 Hz 2500608 (e.g. 16x1221x128)






In Figure 1-102, Figure 1-103, Figure 1-104 and Figure 1-105 the values of Σ Ton have been drawn as a function of the number of PRP per frame and the refresh rate respectively for PRP = 64 ns, PRP = 128 ns, PRP = 192 ns, PRP = 256 ns. The dark blue area on the figures represents the potential values of the pair (refresh rate, number of PRP per frame). The coloured area, represents among those values, the values of the pair (refresh rate, number of PRP per frame) which enable to meet the requirements of the ECC decision [69].

The table below highlight some values that could be used and which fulfils the requirements.

Table 1-13 Refresh rate maximum for a given number of PRP per frame in order to respect the requirements

Refresh rate maximum

Number of PRP per frame : 262144 for example : 8 pulses x 1024 code chips x 32 symbols

8.33 Hz


4.54 Hz


2.38 Hz


1.19 Hz

Table 1-14 Number of PRP per frame maximum for a given refresh rate in order to respect the requirements

Number of PRP per frame maximum

Refresh rate = 0.5 Hz 5001216 (e.g. 16x1221x256)







Figure 1-102 Possible values of refresh rate and number of PRP per frame with PRP = 64ns







The constraint on LDC limits significantly the choice on the parameters to design the signal structure. Although, with regards to the preliminary analysis done in this paragraph, and taking into account the hypothesis done herein that Ton is referring to the pulse emission time, it seems that values of operational parameters such as refresh rate, PRP, Frame duration, and number of PRP per frame required to meet the expected performances of SELECT applications are possible to find.



The hypothesis on Ton referring to the pulse emission time, is still under discussion in the standardization. The analysis on LDC constraints will be updated, following the outcomes of T6.3 “Contribution to standardization bodies”.



REFERENCES

TABLES

Table 1-1 Comparison of the different SOA backscattering techniques. .............................................. 21

Table 2-1 Symbols Definition................................................................................................................. 26

Table 3-1Tag-reader maximum range versus the number of quantization bits. .................................. 42

Table 3-2 Simulation parameters for link budget analysis at center frequency – case A. .................... 47

Table 3-3 Simulation parameters for link budget analysis at center frequency – case B. .................... 47

Table 3-4 Signal and noise levels – case A. ............................................................................................ 51

Table 3-5 Signal and noise levels – case B. ............................................................................................ 51

Table 5-1 Localization RMSE for random walking, PF algorithm and mobility model M1 and M2 for various speed and localization update rate. ....................................................................................... 108

Table 5-2 LEO and average RMSE results for static stochastic tag position estimation ..................... 112

Table 5-3 Average RMSE for dynamic stochastic tag trajectory tracking for different values of refresh rate ...................................................................................................................................................... 113

Table 5-4 Average RMSE for dynamic stochastic tag trajectory tracking for different values of tag maximum speedy ................................................................................................................................ 113

Table 6-1 Pros. and Cons. of the sensing algorithms studied. ............................................................ 125

Table 6-2 Number of PRP per frame maximum for a given refresh rate in order to respect the requirements ....................................................................................................................................... 126

Table 6-3 Refresh rate maximum for a given number of PRP per frame in order to respect the requirements ....................................................................................................................................... 126

Table 6-4 Number of PRP per frame maximum for a given refresh rate in order to respect the requirements ....................................................................................................................................... 126

FIGURES

Figure 1-1 Backscatter scheme between a transmitting reader and a backscattering tag. ................. 14

Figure 1-2 Backscattering communication pulse implementing an UHF powering signal [17]. .......... 15

Figure 1-3 RFID tag using inkjet printing. ............................................................................................. 16

Figure 1-4 Photo of the UWB 35-bit chipless RFID tag. ........................................................................ 17

Figure 1-5 Measured attenuation of 23-bit multiresonator. ............................................................... 17

Figure 1-6 SAW tag example. ............................................................................................................... 18

Figure 1-7 Radio-powered module with asymmetric link using UWB for RFID [25]. ........................... 19



Figure 1-8 RF tag and reader with asymmetric communication bandwidth-solution proposed in [26]. ............................................................................................................................................................... 20

Figure 1-9 RFID based on ultra-wideband time-hopped pulse-position modulation – solution proponed in [27]. ................................................................................................................................... 20

Figure 2-1 Reader functional blocks. .................................................................................................... 23

Figure 2-2 Data packet structure. ......................................................................................................... 25

Figure 2-3 Backscatter modulator architecture. .................................................................................. 27

Figure 2-4 Tag functional blocks. .......................................................................................................... 28

Figure 2-5 Example of signal exchange between the reader and the tag ............................................ 30

Figure 3-1 Equivalent scheme of the backscatter link.......................................................................... 31

Figure 3-2 Example of PAM backscatter modulation format in case of Npc=1. .................................... 36

Figure 3-3 Zero-mean tag code behavior. ............................................................................................ 37

Figure 3-4 Comparison of the quantized and not quantized channel energy profile at the output of the RF section. On the X-axis is the index of the energy bin, on the Y-index is the level of energy of the signal. The blue curve represents the RF output signal energy not quantized, whereas the red curve represents the RF output signal energy quantized ..................................................................... 41

Figure 3-5 Measured waveforms of backscattering Antenna Mode and Structural Mode ................ 42

Figure 3-6 Tag clock drift impact for a 1 x 128 code length. ................................................................ 44



Figure 3-9 Signal measurements in anechoic chamber. The plot represents the measured backscattered signals amplitude as a function of the time .................................................................. 46

Figure 3-10 Antenna mode measured in anechoic chamber. .............................................................. 46

Figure 3-11 Transmitted pulse according to EU UWB regulation – case A. ......................................... 48

Figure 3-12 Transmitted pulse according to EU UWB regulation – case B. ......................................... 49

Figure 3-13 Power spectral density of the radiated signal. .................................................................. 49

Figure 3-14 Received power at the reader side. .................................................................................. 50

Figure 3-15 Peak voltage at reader side – case A ................................................................................. 50

Figure 3-16 Peak voltage at reader side – case B ................................................................................. 51

Figure 3-17 Single-pulse signal-to-noise ratio at the reader side – case A. ......................................... 52

Figure 3-18 Single-pulse signal-to-noise ratio at the reader side – case B. ......................................... 53

Figure 3-19 Dynamic range at the reader side. .................................................................................... 53

Figure 3-20 Distance-Data rate relation for single path matched filter (SPMF) in AWGN for different TAG orientation angles, with target bit error probability 10-3. ............................................................. 55

Figure 3-21 Number of pulses necessary to reach the target BEP of 10-3 – case A. ............................ 56

Figure 3-22 Number of pulses necessary to reach the target BEP of 10-3 – case B. ............................. 57

Figure 3-23 Data rate considering a target BEP of 10-3 - case A. .......................................................... 57



Figure 3-24 Data rate considering a target BEP of 10-3 - case B. .......................................................... 58

Figure 3-25 Maximum clutter amplitude tolerable to have a minimum SQNR=10dB – case A. .......... 58

Figure 3-26 Maximum clutter amplitude tolerable to have a minimum SQNR=10dB – case B. .......... 59

Figure 3-27 UHF link budget. ................................................................................................................ 59

Figure 3-28 Measurements at ENSTA laboratory. ................................................................................ 60

Figure 3-29 Indoor scenario considered for the measurement campaign at ENSTA-ParisTech. The distances between each point and the antennas connected to the VNA are also reported. ............... 61

Figure 3-30 Example of the laboratory impulse response (grid point H, at distance of 3.1 m) and of the antenna mode contribution (after clutter removal). ...................................................................... 62

Figure 3-31 Bit error probability as a function of the bit rate in different tag locations. SPMF receiver is considered. ......................................................................................................................................... 63

Figure 3-32 Bit error probability as a function of the bit rate in different tag locations. IMF is considered. ............................................................................................................................................ 64

Figure 3-33 BER as a function of the SIR in the anechoic chamber scenario where clutter is summed up artificially. ......................................................................................................................................... 65

Figure 3-34 BER as a function of SNR (Laboratory scenario).The useful tag is located in point D. Five interfering tags are present in locations A,B,C,E,F when m-sequence 63 is adopted. One interferer tag in location F is considered when m-sequence 7 is used. ...................................................................... 66

Figure 4-1 CRLB and Ziv-Zakai bounds for TOA estimation RMSE using RRC and Gaussian pulses. .... 69

Figure 4-2 Backscattered pulse measured in anechoic chamber-wire patch antenna. ....................... 70

Figure 4-3 Example of a realization of the channel - dynamic channel model -CM1. .......................... 70

Figure 4-4 Example of backscattering CIR obtained with the proposed channel model. .................... 71

Figure 4-5Frame format example. ........................................................................................................ 72

Figure 4-6 UWB RF front-end architecture. ......................................................................................... 73

Figure 4-7 Serial Backward Search for Multiple Cluster TOA algorithm principles illustration............ 75

Figure 4-8 Ranging error outage as a function of the error threshold for CM1 channel. .................... 77

Figure 4-9 Error outage as a function of the error threshold for CM7 channel. .................................. 78

Figure 4-10 Ranging RMSE as a function of the tag-reader distance for CM1 and CM7 channels. ..... 79

Figure 5-1 EFIM structures and corresponding Bayesian networks for three agents: (a) noncooperative localization, (b) spatial cooperation, and (c) spatio-temporal cooperation for two time steps. ............................................................................................................................................. 83

Figure 5-2 Single cell scenario. ............................................................................................................. 84

Figure 5-3Conveyor Belt. ...................................................................................................................... 84

Figure 5-4 Multicell scenario. ............................................................................................................... 84

Figure 5-5Random Walking. ................................................................................................................. 84

Figure 5-6 Example of derived in the FP7 European Project EUWB. ........................................ 86

Figure 5-7 Structure of a Bayesian filter. .............................................................................................. 88

Figure 5-8 Monostatic radar detection probability .............................................................................. 99



Figure 5-9 Multistatic radar detection probability ............................................................................... 99

Figure 5-10 Multistatic radar localization probability ............................................................................ 1

Figure 5-11 Multistatic radar localization probability ........................................................................ 100

Figure 5-12 Reference scenario with four monostatic radars at the corner of the SA. ..................... 101

Figure 5-13 LEO for 100 target located in the SA. Position estimation is averaged on 100 measurements. .................................................................................................................................... 101

Figure 5-14 Position estimation for a target located in the center of the SA .................................... 102

Figure 5-15 LEO for different values of ΔSNR and 100 targets located in the SA. ................................ 102

Figure 5-16 Localization error for 100 random targets located in the SA. ......................................... 102

Figure 5-17 True and estimated trajectories Figure 5-18 LEO for R =10 Hz ................................ 103

Figure 5-19 True and estimated trajectory for R = 5 Hz Figure 5-20 LEO for R =5 Hz ................... 104

Figure 5-21 True and estimated trajectory for R = 1 Hz Figure 5-22 LEO for R =1 Hz ................... 104

Figure 5-23 True and estimated trajectory for R = 10 Hz, v =0.5 m/s and mobility model M1. ........ 105

Figure 5-24 True and estimated trajectory for R = 10 Hz, v =0.5 m/s and mobility model M2. ........ 105

Figure 5-25 LEO for R = 10 Hz, v =0.5 m/s and mobility model M1 (blue) and M2 (purple). ............. 105

Figure 5-26 True and estimated trajectory for R = 1 Hz, v =0.5 m/s and mobility model M1. .......... 105

Figure 5-27 True and estimated trajectory for R = 1 Hz, v =0.5 m/s and mobility model M2. .......... 106

Figure 5-29 True and estimated trajectory for R = 10 Hz, v =1 m/s and mobility model M1. ........... 107


Figure 5-31 LEO for R = 10 Hz, v =1 m/s and mobility model M1(blue), M2 (blue). .......................... 107



Figure 5-34 LEO for R = 10 Hz, v =3 m/s and mobility model M1 (blue), M2 (purple). ...................... 108

Figure 5-35Example of 2D positioning estimation simulation result. ................................................ 109

Figure 5-36Position estimation instantaneous error. ........................................................................ 110

Figure 5-37 Example of rectilinear piece-wise trajectory in a 30mx30m area, comprising a continuous grid of 5m x 5m readers ...................................................................................................................... 111

Figure 5-38 Instantaneous errors on estimated tag coordinates ....................................................... 111

Figure 5-39 LEO for random static tag position.................................................................................. 112

Figure 6-1 Principle of MF based detection. ...................................................................................... 116

Figure 6-2 Vector extraction for the calculation of the sample covariance matrix. Oversampling factor M=2. .................................................................................................................................................... 119

Figure 6-3CP in a OFDM symbol. ........................................................................................................ 121

Figure 6-4 Comparison between perfect synchronization and no synchronization. ......................... 122

Figure 6-5 Possible values of refresh rate and number of PRP per frame with PRP = 64ns .............. 128

Figure 6-6 Possible values of refresh rate and number of PRP per frame with PRP = 128ns ............ 128







CONCLUSIONS AND FUTURE WORK

CONCLUSIONS

After an analysis of current UWB passive tags solutions, it has been shown that the backscatter modulation architecture, for what regards tag-reader communication and ranging, appears the solution that best fits the SELECT project requirements in terms of ranging accuracy, operating range, cost, complexity and possible integration with the UHF RFID technology.

The system level architectures for the tag and reader have been defined and described. In addition, a signal structure has been proposed. Some preliminary results related to the communication range and ranging capabilities in terms of TOA estimation have been reported. It has been shown how the choice of system parameters is affected by several constraints related to propagation, energy consumption, multi-tag capabilities, etc. In particular, the final performance of both communication (BER) and ranging (TOA estimation RMSE) is mainly determined by the number of pulses adopted per symbol Ns. In fact, passive backscattering systems are affected by poor link budget that limits the transmission range. In order to extend this transmission range, the SNR should be increased via signal coherent large integration. Long operating ranges are feasible but require the integration over a large number of pulses (e.g., Ns=1000, d=8 meters; Ns=10000, d=15 meters in AWGN).

Regarding the design of the spreading codes, zero-mean codes allow removing the clutter components. This requirement of zero-mean codes usage is less stringent adopting long codes. The code length should be well chosen in order to mitigate the clutter impact on the ranging accuracy and to decrease the multiple access interference. Several pulses per code chip are needed to minimize the effect of the tag clock drift on the TOA estimation. Preliminary results on multi-tag scenario show that orthogonal codes provide better performance with respect to PN codes. However, the adoption of orthogonal codes requires a quasi-synchronous system (this requirement can be fulfilled by adopting a wake-up strategy through the UHF down-link). The use of the wake-up strategy also affects the code assignment strategy as well as the interaction required between the UHF and UWB sections of the tag. Two possible code assignment strategies have been identified. The assignment strategy which allocates a unique code to each tag in the system appears quite interesting as it makes unnecessary the transmission of the payload, with the consequent possibility to drastically reduce the packet size down to a few bits. This allows for fast signal acquisition and reduced reader complexity. However, a more extensive study is necessary with the aim to define the performance expected in case of multi-tag and multi-reader scenario using different types of codes and decoding strategies.

Results have shown that a good performance in realistic scenarios with multipath can be obtained even using simple single-path matched filters (SPMF) receivers. The adoption of Rake receivers might improve significantly the performance at the expense of a higher receiver complexity. A coherent receiver is in any case necessary.



Other specific aspects have been investigated such as the effect of quantization in TOA ranging, the signal dynamic range, and the UHF link budget for tag wake-up.

Wireless tracking and sensor radars (WSR) network design require a clear understanding on how impairments and system parameters affect detection and localization accuracy.

A mathematical framework for the analysis of tracking networks has been developed, which allows us to consider different scenarios, network topologies, and system configurations. Starting from foundations of network localization the aim of this activity has been oriented to the analysis of solutions and their performance in scenarios of practical interest. A model to relate the UWB time-of-arrival estimation on the SNR received by each reader has been proposed and its impact on the localization accuracy analyzed. For the tracking of mobile objects, Bayesian approaches have been investigated and, in particular, the role of mobility models to predict the new position has been studied. This is a fundamental aspect for both deterministic and stochastic trajectories. The correction of the position estimate is then dependent on the perception model used and the refresh rate of the tracking system. The results have been given for a case study of UWB monostatic radars. They show how tracking techniques based on particle filters and Kalman filters can provide results close to the specifications in the nominal multi-cell scenario defined if all the aspects mentioned above are properly addressed and jointly considered in a unique framework.

NEXT STEPS

In the following we list the next planned activity by identifying, when present, the main critical aspects to be investigated:

Performance evaluation in the reference scenario (multi-tag and multi-reader) using channel measurements and models (from WP1) – Task 2.5

Algorithm design for efficient multi-tag detection – Task 2.2

Synchronization, code acquisition and TOA estimation in multi-tag scenario, definition of the packet preamble structure - Task 2.2

Choice of the spreading codes assignment strategy – Task 2.5

Performance assessment of localization and tracking schemes using realistic ranging models from Task 2.2 and antenna radiation and backscattering characterization in real environments from Task 2.1 to fulfill the requirements in harsh environments and compare them under the same setting – Task 2.3

Complexity evaluation of tracking systems for identification of proper upper layer architectures – Task 2.3

Coexistence – Task 2.4 o Performance evaluation of some spectrum sensing schemes in the reference

scenario o Study of pulse shaping techniques



One important critical aspect is the design of fast, low-complexity and performing synchronization schemes able to work in the presence of a large number of tags (multi-tag scenario). In addition, some important implementation issues have to be addressed in the view of the final demonstrator as listed below:

Analysis of implementation issues (in cooperation with WP4) o Dynamic range of the reader RF frontend wrt clutter effects o Maximum number of pulse accumulations o Quantization effects o Multi-code demodulations and synchronization o Low-jitter wake-up strategies



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