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Geolocation in a PicoRadio

Environment

Diploma Thesis

Department of Electrical Engineering ETH Zürich

Department of Electrical Engineering and Computer Science

UC Berkeley

Jan Beutel

To my father

who has enabledand inspired my education

in Electrical Engineeringthrough his generous support.

— JB

Contents

1: An Introduction to PicoRadio 11.1 Goals for PicoRadio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Applications for PicoRadio . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Scenarios for PicoRadio . . . . . . . . . . . . . . . . . . . . . . . . 41.2.2 Exploratorium Environment . . . . . . . . . . . . . . . . . . . . . 51.2.3 Exploratorium Applications . . . . . . . . . . . . . . . . . . . . . 61.2.4 Navigation Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3 PicoRadio Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.3.1 Network Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.3.2 PicoNode Subsystems . . . . . . . . . . . . . . . . . . . . . . . . . 101.3.3 Required System Specifications . . . . . . . . . . . . . . . . . . . 11

1.4 Advantages of PicoRadio . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2: Fundamentals of Navigation 132.1 Radionavigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1.1 Active Radionavigation . . . . . . . . . . . . . . . . . . . . . . . . 142.1.2 Passive Radionavigation . . . . . . . . . . . . . . . . . . . . . . . 142.1.3 Communication Links and Radionavigation . . . . . . . . . . . . 142.1.4 Applications and Services for Positioning . . . . . . . . . . . . . 15

2.2 Some Geometric Principles . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.1 Relative Position . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.2 Absolute Position . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2.3 Triangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2.4 Geometric Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 Triangulation with Range Measurements . . . . . . . . . . . . . . . . . 192.3.1 Triangulation for Relative Positions . . . . . . . . . . . . . . . . 202.3.2 Triangulation for Absolute Positions . . . . . . . . . . . . . . . . 22

2.4 Issues on Radio Propagation . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.1 Radio Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.2 Noise Environment . . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.3 Computational Model . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.4 Ultra Wideband Pulse Radio . . . . . . . . . . . . . . . . . . . . . 29

iii

Contents

2.5 Accuracy and Reliability of Radionavigation . . . . . . . . . . . . . . . . 29

3: The GPS System 313.1 Fundamental Properties of GPS . . . . . . . . . . . . . . . . . . . . . . . 31

3.1.1 GPS Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.1.2 GPS Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 333.1.3 GPS Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.2 Enhancing the Performance of GPS . . . . . . . . . . . . . . . . . . . . . 383.2.1 Estimates for Underdetermined Navigation Solutions . . . . . . 383.2.2 Carrier Phase Noise . . . . . . . . . . . . . . . . . . . . . . . . . 393.2.3 Differential GPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.3 Future Drivers in GPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4: Positioning for Networked PicoNodes 414.1 Lucent WaveLAN IEEE 802.11 . . . . . . . . . . . . . . . . . . . . . . . 41

4.1.1 WaveLAN Hardware . . . . . . . . . . . . . . . . . . . . . . . . . 434.1.2 WaveLAN Software Environment . . . . . . . . . . . . . . . . . . 44

4.2 Propagation Measurements for Navigation . . . . . . . . . . . . . . . . 444.2.1 System Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.2.2 Test Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.2.3 Signal Coverage Map . . . . . . . . . . . . . . . . . . . . . . . . . 484.2.4 Signal Propagation Model . . . . . . . . . . . . . . . . . . . . . . 534.2.5 Signal Propagation in the Presence of Noise . . . . . . . . . . . 55

4.3 Networked Ranging Vectors . . . . . . . . . . . . . . . . . . . . . . . . . 574.3.1 Test Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 574.3.2 Data Model for Triangulation . . . . . . . . . . . . . . . . . . . . 574.3.3 Triangulation Estimates . . . . . . . . . . . . . . . . . . . . . . . 60

5: Navigation Scheme for PicoRadio 635.1 Cooperative Ranging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.1.1 Cooperative Ranging Algorithm . . . . . . . . . . . . . . . . . . . 635.1.2 Modes of Operation . . . . . . . . . . . . . . . . . . . . . . . . . . 665.1.3 Levels of Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665.1.4 Example of Local Clusters of Operation . . . . . . . . . . . . . . 66

5.2 Geolocation and Multihop Routing Integration . . . . . . . . . . . . . . 685.2.1 Multihop Networking for PicoNodes . . . . . . . . . . . . . . . . 695.2.2 Location Based Routing . . . . . . . . . . . . . . . . . . . . . . . 69

5.3 Constraints of a PicoRadio System . . . . . . . . . . . . . . . . . . . . . 695.4 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

A: Positioning Error Estimates 73

iv

Contents

B: Signal Propagation Data 81B.1 Signal Level Map Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81B.2 Empirical Signal Propagation Model . . . . . . . . . . . . . . . . . . . . 84B.3 Triangulation Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 85B.4 Networked Ranging Vector Data . . . . . . . . . . . . . . . . . . . . . . 90

C: Glossary 93

v

Contents

vi

Tables

2-1 Communication Links Assist Radionavigation . . . . . . . . . . . . . . 152-2 Classification of Location Services for GSM . . . . . . . . . . . . . . . . 162-3 Geometric Error in Triangulation of a Triangle ABC . . . . . . . . . . . 23

4-1 BPSK/QPSK Encoding Table . . . . . . . . . . . . . . . . . . . . . . . . 424-2 IEEE 802.11 Frequencies and Output Power Levels According to Reg-

ulatory Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434-3 WaveLAN IEEE PC Transceiver Card Specifications . . . . . . . . . . . 434-4 WaveLAN IEEE Range Extender Antenna Specifications . . . . . . . . 444-5 WaveLAN Software Versions . . . . . . . . . . . . . . . . . . . . . . . . . 464-6 Signal and Noise Map Characterisic Parameters . . . . . . . . . . . . . 534-7 Position of Nodes for Networked Ranging Vector Experiment . . . . . . 57

B-1 Datasets Derived from Networked Ranging Vectors Set 1 . . . . . . . . 91B-2 Datasets Derived from Networked Ranging Vectors Set 2 . . . . . . . . 92

vii

Tables

viii

Figures

1-1 PicoNode System Partitioning . . . . . . . . . . . . . . . . . . . . . . . . 21-2 San Francisco Exploratorium Building . . . . . . . . . . . . . . . . . . . 51-3 San Francisco Exploratorium Environment . . . . . . . . . . . . . . . . 61-4 Typical Exploratorium Exhibit . . . . . . . . . . . . . . . . . . . . . . . 71-5 Userinterfaces for the Exploratorium . . . . . . . . . . . . . . . . . . . . 71-6 Exploratorium Tour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81-7 PicoRadio Network Topology . . . . . . . . . . . . . . . . . . . . . . . . . 101-8 PicoRadio Network Topology Basestations . . . . . . . . . . . . . . . . . 101-9 PicoRadio Network Topology Signal Range . . . . . . . . . . . . . . . . 11

2-1 Active Radionavigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142-2 Passive Radionavigation . . . . . . . . . . . . . . . . . . . . . . . . . . . 142-3 Position and Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . 162-4 Cartesian and Polar Coordinate Systems . . . . . . . . . . . . . . . . . 172-5 Vector Geometry for Positions . . . . . . . . . . . . . . . . . . . . . . . . 182-6 Triangulation with Range Measurements . . . . . . . . . . . . . . . . . 202-7 Relative Triangulation with Range Measurements . . . . . . . . . . . . 202-8 Absolute Triangulation with Range Measurements . . . . . . . . . . . 212-9 Errors in Range Measurements . . . . . . . . . . . . . . . . . . . . . . . 222-10 Errors in Triangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222-11 Three Dimensional Absolute Position . . . . . . . . . . . . . . . . . . . . 232-12 Absolute Positioning Errors . . . . . . . . . . . . . . . . . . . . . . . . . 242-13 Absolute Triangulation Error Estimation . . . . . . . . . . . . . . . . . 252-14 Absolute Positioning Error Estimate . . . . . . . . . . . . . . . . . . . . 262-15 Geometric Error on Many Nodes . . . . . . . . . . . . . . . . . . . . . . 26

3-1 GPS System Segements . . . . . . . . . . . . . . . . . . . . . . . . . . . 313-2 Global Configuration of the GPS Satellites . . . . . . . . . . . . . . . . 323-3 GPS L1 and L2 Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323-4 Satellite Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4-1 Lucent WaveLAN IEEE 802.11 Hardware . . . . . . . . . . . . . . . . . 424-2 WaveMANAGER/Client Software Tool . . . . . . . . . . . . . . . . . . . 45

ix

Figures

4-3 WaveMANAGER/AP Software Tool . . . . . . . . . . . . . . . . . . . . . 454-4 Access Point Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464-5 Laptop Computer with WaveLAN Card . . . . . . . . . . . . . . . . . . 474-6 Charting of Signal Coverage . . . . . . . . . . . . . . . . . . . . . . . . . 474-7 Test Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484-8 Basestation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494-9 SNR Map to Access Point 1, Channel 10 . . . . . . . . . . . . . . . . . . 504-10 SNR Map to Access Point 1, Channel 2 . . . . . . . . . . . . . . . . . . . 504-11 SNR Map to Access Point 2, Channel 10 . . . . . . . . . . . . . . . . . . 514-12 SNR Map to Access Point 2, Channel 2 . . . . . . . . . . . . . . . . . . . 514-13 SNR Map to Access Point 3, Channel 10 . . . . . . . . . . . . . . . . . . 524-14 SNR Map to Access Point 3, Channel 2 . . . . . . . . . . . . . . . . . . . 524-15 WaveLAN Long Range Path Loss . . . . . . . . . . . . . . . . . . . . . . 544-16 WaveLAN Short Range Path Loss . . . . . . . . . . . . . . . . . . . . . . 544-17 Signal Propagation Over Distance . . . . . . . . . . . . . . . . . . . . . 554-18 Signal Propagation without Noise . . . . . . . . . . . . . . . . . . . . . . 554-19 Signal Propagation with 0 dBm Noise . . . . . . . . . . . . . . . . . . . 564-20 Signal Propagation with +15 dBm Noise . . . . . . . . . . . . . . . . . . 564-21 Networked Ranging Vectors Set 1 . . . . . . . . . . . . . . . . . . . . . . 584-22 Networked Ranging Vectors Set 2 . . . . . . . . . . . . . . . . . . . . . . 584-23 Triangulation Estimation Set 1 on 7 Nodes . . . . . . . . . . . . . . . . 604-24 Triangulation Estimation Set 2 on 7 Nodes . . . . . . . . . . . . . . . . 614-25 Errors in Triangulation using Permutations . . . . . . . . . . . . . . . . 61

5-1 Cooperative Ranging Node Functions . . . . . . . . . . . . . . . . . . . 645-2 Kalman Filter for Positioning . . . . . . . . . . . . . . . . . . . . . . . . 655-3 PicoNode Ranging Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 675-4 PicoNode Ranging Mechanism Motion . . . . . . . . . . . . . . . . . . . 675-5 PicoNode Ranging Mechanism Clusters . . . . . . . . . . . . . . . . . . 685-6 Cooperative Ranging in the Network Context . . . . . . . . . . . . . . . 69

A-1 Position Error Estimate, 3 Nodes, 50% Range Error, 5 Iterations . . . 73A-2 Position Error Estimate, 3 Nodes, 50% Range Error, 5000 Iterations . 74A-3 Position Error Estimate, 3 Nodes, 5% Range Error, 500 Iterations . . . 74A-4 Position Error Estimate, 5 Nodes, 50% Range Error, 5 Iterations . . . 75A-5 Position Error Estimate, 5 Nodes, 50% Range Error, 500 Iterations . . 75A-6 Position Error Estimate, 5 Nodes, 5% Range Error, 500 Iterations . . . 76A-7 Position Error Estimate, 12 Nodes, 50% Range Error, 5 Iterations . . . 76A-8 Position Error Estimate, 12 Nodes, 50% Range Error, 500 Iterations . 77A-9 Position Error Estimate, 12 Nodes, 5% Range Error, 500 Iterations . . 77A-10Position Error Estimate, 35 Nodes, 50% Range Error, 5 Iterations . . . 78A-11Position Error Estimate, 35 Nodes, 50% Range Error, 500 Iterations . 78

x

Figures

A-12Position Error Estimate, 35 Nodes, 5% Range Error, 500 Iterations . . 79

B-1 Signal Level Map to Access Point 1, Channel 10 . . . . . . . . . . . . . 81B-2 Signal Level Map to Access Point 1, Channel 2 . . . . . . . . . . . . . . 82B-3 Signal Level Map to Access Point 2, Channel 10 . . . . . . . . . . . . . 82B-4 Signal Level Map to Access Point 2, Channel 2 . . . . . . . . . . . . . . 83B-5 Signal Level Map to Access Point 3, Channel 10 . . . . . . . . . . . . . 83B-6 Signal Level Map to Access Point 3, Channel 2 . . . . . . . . . . . . . . 84B-7 Signal Propagation Crossections . . . . . . . . . . . . . . . . . . . . . . 84B-8 Triangulation Estimation Set 1 on 3 Nodes . . . . . . . . . . . . . . . . 85B-9 Triangulation Estimation Set 1 on 4 Nodes . . . . . . . . . . . . . . . . 85B-10Triangulation Estimation Set 1 on 5 Nodes . . . . . . . . . . . . . . . . 85B-11Triangulation Estimation Set 1 on 6 Nodes . . . . . . . . . . . . . . . . 86B-12Triangulation Estimation Set 1 on 8 Nodes . . . . . . . . . . . . . . . . 86B-13Triangulation Estimation Set 1 on 9 Nodes . . . . . . . . . . . . . . . . 86B-14Triangulation Estimation Set 2 on 3 Nodes . . . . . . . . . . . . . . . . 87B-15Triangulation Estimation Set 2 on 4 Nodes . . . . . . . . . . . . . . . . 87B-16Triangulation Estimation Set 2 on 5 Nodes . . . . . . . . . . . . . . . . 88B-17Triangulation Estimation Set 2 on 6 Nodes . . . . . . . . . . . . . . . . 88B-18Triangulation Estimation Set 2 on 8 Nodes . . . . . . . . . . . . . . . . 89B-19Triangulation Estimation Set 2 on 9 Nodes . . . . . . . . . . . . . . . . 89

xi

Figures

xii

Introduction

It has long been my intention to visit another university as part of my curriculumat the Swiss Federal Institute of Technology (ETH) in Zürich. Lectures, several jobsand semesters in Zürich flew along and were very fruitful. Suddenly the grand finalewas right up ahead of me and I had only this last chance to study abroad. Theknowledge about the Infopad project brought me in contact with Bob Brodersenand Jan Rabaey at UC Berkeley. They invited me to spend the summer and fallresearching at their newly opened facility, the Berkeley Wireless Research Center.

Motivation

I had since been mostly working in the area of microelectronic packaging and inter-connect as well as system design, although I had always been interested in networksand mobile computing as well. The proposed project in Berkeley called PicoRadiowas a good opportunity to gain more experience in this domain. The efforts onPicoRadio were only to start when I arrived here in Berkeley and have maturedquite a bit since.

Contents and Overview

The first chapter gives an introduction to PicoRadio and develops an applicationscenario to be used as a guideline throughout the development process. In the secondchapter basic principles about navigation, especially radionavigation and geometryare introduced. It concludes with several aspects of radio signal propagation. Thethird chapter deals on the Global Positioning System (GPS) and the limitations ofthis well established navigation system. The GPS system is used as a comparisonto the positioning mechanism outlined for PicoRadio. In the fourth chapter severalexperiments based on a commercial wireless LAN technology are presented thatlead to the development of a novel positioning mechanism for PicoRadio in the fifthchapter. This positioning mechanism is titled cooperative ranging. It exploits theproperties of the PicoRadio network environment, namely the amount of nodes thatform the network and their behavior.

Acknowledgements

I would like to thank all the people at the Berkeley Wireless Research Center formaking the time I spent researching here so very pleasant and successful. Discus-sions and the inputs of all of you have contributed to this thesis. Without you thiswould not have been possible. Certainly Jan Rabaey and Bob Brodersen have to bementioned first. Sherry Hsi, Josie Ammer, Dani Patel, Mats Torkelson and Matthew

xiii

Figures

Muh have all been great contributors, too. At the Swiss Federal Institute of Tech-nolgy (ETH) I would like to thank Gerhard Tröster and Marcel Kreuzer for guidingmy studies in such an uncomplicated manner. My special thanks go to Andi Thielfor being a great mentor during my time with the Electronics Laboratory and µ-blox in Zürich. Last but not least I would like to thank my family for supporting meall these years and Eveline for keeping up with me over the times when I was notaround.

The work outlined in this thesis was supported by the rector of the Swiss Federal In-stitute of Technology (ETH) Zürich, Prof. Dr. K. Osterwalder and the Karolus Fondsof ETH Zürich.

Berkeley, December 13th, 1999

Jan Beutel

xiv

1An Introduction to PicoRadio

PicoRadios are small, ultra low power, inexpensive digital radio transceivers thatallow flexible communication at a relatively low bitrate and over short distances.Possible applications are to be found among distributed sensor networks, personalcommunicators, access and remote control as well as locator devices. The PicoRadionetwork is comprised of PicoNodes that form the configuration of the network.

1.1 Goals for PicoRadioThe ultimate goal for a PicoNode is a single-chip implementation of a tiny, very lowpower, configurable radio [4].

• Small form factor

x Low system cost

• Energy efficient

• Configurable partitioning

• Range 3-10 meters

• In- and outdoor usage

• 16 user per cell

• Low bitrate compared to other networks

• Selfconfiguring, multihop network

• Routing of data

• Node to node and broadcasting communication

• No special infrastructure necessary

• Geolocation capability for every node

1

Chapter 1: An Introduction to PicoRadio

Sub-elements of a PicoNode include an embedded microprocessor, a reconfigurablemicroprocessor, reconfigurable logic e.g. FPGA, a dedicated (custom) digital signalprocessing block, and an analog block with RF, sensor interfaces, possibly GPS, etc.[34] as seen in figure 1-1. These blocks will be interconnected in a potentially differ-ent way for each type of PicoNode core.

Dedicated

Digital Signal

ProcessingReconfigurable

Logic

Analog RF, GPS

receiver and

sensor interface

Embedded

Microprocessor

Reconfigurable

ProcessorDedicated

Digital Signal

ProcessingReconfigurable

Logic

Analog RF, GPS

receiver and

sensor interface

Embedded

Microprocessor

Reconfigurable

Processor

Figure 1-1PicoNode systempartitioning with dedicatedand configurable buildingblocks. A standardsensorinterface will be partof the PicoNode core as well[4].

Much like the design of the BEE (Big-scale Emulation Environment) – a high-endradio test bed for system-level experimentation [57], a test bed to allow for net-working, processing, radio, etc. exploration and integration for the PicoNode will bedeveloped as well [5]. In order to verify the envisioned goals for PicoRadio a suitableenvironment and applications need to be specified and implemented.

1.2 Applications for PicoRadioPossible applications for PicoRadio range from sensors to communications and re-mote control applications. Although solutions for these type of applications existsand/or are under development today, the approach of a network of PicoNodes de-scribed here is quite different. Moreover it enables novel applications and services.

A first broad brainstorming resulted in the following proposals [35]. They are listedwithout specific order and might not even fit into the scheme of the PicoNodes an-ticipated, but show quite clearly where wireless communication of some sort wouldbe desirable:

• Wireless home network (much like HomeRF [20])

• Virtual keyboard or notepad

• Smart environment: Fridge, cabinet, etc.

• Wireless backplanes: Phone, computer, etc. (similar to Bluetooth [19])

• BodyLAN, human environment: Identification, personalization, etc.

• Auto/industrial sensors

• Home alarm

• Heart-rate monitor

• Wireless intercom or cordless phone: Baby monitor, walkie-talkie, etc.

2

1.2. Applications for PicoRadio

• Garage door opener

• TV/stereo remote control

• Remote-controlled car

• School/classroom applications

• Smart tags: Luggage/price tags

• Location of objects and people: GPS/LPS [56, 28]

• Inventory managment [58]

• Toll collection

• Smart indicator

• Ubiquitous repeater

• Distributed Appliances [10]

• Agricultural control/optimization

A narrowing in on the actual goals of PicoRadio showed a clearer view of the scenar-ios to be considered, and also on the requirements for a specific PicoNode. A numberof questions were used as a guideline:

• Is it an attractive or motivating application?

• What additional infrastructure is required? (i.e. Hospital)

• What is the value-added by a PicoNode for that scenario compared to existingsolutions?

• What is new/different?

• Who is our "customer”, partner?

• How can it be demonstrated?

• What’s been done before in this area?

• What is wrong with current solutions?

• What could be built today?

• Can we use Bluetooth for the test bed?

• How are we better?

• What expertise/resources do we have?

• What is the main enabling technology?

• Do we want to be known for it?

• What domain needed for demonstration?

• Any social relevancy?

• What quality of service is required?

3


1.2.1 Scenarios for PicoRadioThe following presents a list of possible scenarios, all with different applications andrequirements for a radio communication environment:

1. Indoor smart home environment: Temperature-, light sensor and control,smart consumer gadgets, intercom, paging, connection to existing devices, ap-pliances like a smart refrigerator or smart microwave oven.

2. Conferencing meeting: Voting and paging mechanisms, location of people,social networking, spontaneous information gathering.

3. PC-enhanced toy: Interactive, daily changing, updating, networked to chil-dren, networked to other toys, energy scavenging, paging and positioning mech-anism [1, 11].

4. Classroom K-6: Assess students, interactive and group collaboration.

5. Exploratorium or Science Museum: Heterogeneous environment compri-ses smart home and classroom environment. The user is not familiar with theequipment. Location awareness is important for the applications.

6. Inventory in a store or factory: Smart tags. Unidirectional sensing appli-cation.

7. Distributed appliances: Mobile phone made up of four pieces, distributedkeyboard, automobiles [10].

8. Hospital environment: Sensor application, distributed databases, entertain-ment.

9. Industrial Sensors: Image grabbing, inventory and paging, lights, tempera-ture but a more static network topology.

It turns out that some of these scenarios are quite similar in respect to the techni-cal specification of the system. Three main groups of scenarios can be identified: Asmart environment, group collaboration and sensor tags.

Scenario 2 and 4 differ mainly in the type of users interacting with the system.Scenario 6 is thought to be more or less a one way communication issue, with ul-tracheap electronic tags broadcasting their identification and position [58]. 1, 5, 8and 9 are also a group of scenarios with similar demands. Although the latter twowould impose much more stringent specifications as a home ennvironment mainlydue to reliability issues. The scenario 5 Exploratorium and Science Museum showedto be the most diverse environment to demonstrate a successfull implementation.It combines the attributes of both the Smart Home 1 and Smart Toy 3 scenariosin addition to providing the possibility for future research and collaboration on theimpact of technology on society. Conferencing and voting mechanisms such as envi-sioned for 2 and 4 can be implemented as well.

The main applications and design possibilites for this specific scenario are:

• Interactive exhibits: Exhibits that can serve/source data/web pages to nearbyclients.

4


• Updateable exhibits: Data stream to download new versions of program/datato exhibit.

• Collaboration: Allowing conferencing, collaboration among clients on the net-work (i.e. voting, sharing information, working on a problem together, testing,etc.).

• Paging: For navigation and collaboration purposes.

• Intercom: Exhibit explainer could broadcast to the nearest nodes on the net-work (no forwarding of packets), users could communicate with each other.

• Sensor: Velocity, EM fields, fish tank temperature, or other sensors specific tothe exhibit. May be used to get user statistics or profiles.

• Energy scavenging: People will be walking around with the mobile PicoNodesor interacting with the exhibits carrying other PicoNodes.

• Positioning: Configuration of network and navigation for users.

1.2.2 Exploratorium EnvironmentThe San Francisco Exploratorium [15] is a science museum for all ages. It is lo-cated in a large hall of the landmark Palace of Fine Arts building in the GoldenGate/Presidio area of San Francisco.

Figure 1-2The San FranciscoExploratorium Building: Alarge open hall with a fewpartitions and a raisedupper section, shown incolor in the middle of thisfigure.

The individual exhibits are often hands-on applications, that require user interac-tion. They are set up in an open in- and outdoor environment and are often changedor rearranged according to topical interests.

Additionally to the interactive exhibits the Exploratorium hosts teacher institutes,school field trips, webcasts and other special events. The facilities include a multi-media learning studio, a tactile dome, museum store, cafe, staff offices and a largeworkshop.

The indoor section is a large, open room with a few subdivisions and walls. Themain walls are made of steel reinforced concrete and steel pillars to support thehigh metal ceiling that is used to hang down lighting and sound equipment. Theenvironment is considered to be a quite hostile one in respect to radio transmissionsdue to the topology and many electrically powered exhibits (emissions and possiblemultipath reflections).

The outdoor area is more like a park with dedicated pathways, vegetation and waterbut also large building structures that obstruct the line of sight over large distances.

5


Figure 1-3The San Francisco Exploratorium features an environment with a large industrial type hallas indoor space as well as a small park and lagoon outdoors.

At times, there may be up to several thousand visitors present in the whole area atonce. Peak attendance on holidays and weekends can be up to 7000 visitors per day.Visitors can roam around freely in the whole exhibit area. To some extent, guidedtour paths are available to visitors in certain sections or theme exhibits. Often clus-ters of visitors would form since there are single distinct attractions or demonstra-tions that many people are interested in. Especially school classes visiting wouldexplore the Exploratorium space in groups of up to 60 persons. However there is noguarantee for a certain minimum amount of people in any given area at any giventime. At times, especially the open outdoor environment might be quite unpopulatedleading to extensive voids in the networked space.

Today a network linking workstations in the staff areas as well as in the classroomsis in place at the Exploratorium. A powerfull backbone network extends along thetwo long sides of the building with distributed access points along the wall. Someof the exhibits already have a computer integrated into them, but they are not net-worked. All electrically powered exhibits are connected to external power that isdistributed from the walls and along the floor.

1.2.3 Exploratorium ApplicationsSeveral different networking application needs exist at the Exploratorium. They areintroduced and outlined in this section.

• The single exhibits of the Exploratorium are not networked.

• Exhibits are usually made up of a single item only.

• The interactive experience is limited (to the extents of the exhibit).

• Visitors can not be identified and tracked when using exhibits.

A key issue is to give each roaming visitors a networked electronic device while atthe Exploratorium that would allow him to communicate, identify and interact withtheir environment and the exhibits. Suitable scenarios of applications were outlined

6


Figure 1-4A typical ExploratoriumExhibit: An InformationKiosk that requires userinteraction for selectionand control of functions.These kiosks are notnetworked toinfrastructure. Otherexhibits would involvesensors or actuators inaddition to a userinterface.

in [23]. Primary applications include analytical experimenting, personalized docu-mentation, self-guided touring, representational model building via a distributedinfrastructure, environmental control and monitoring.

Figure 1-5Userinterfaces for theExploratorium: TheExploratorium visitor canchoose among threedifferent types of portableuserinterfaces withintegrated PicoNodes: Awristband communicator, asmall scale palmtop and afully equipped multimediatablet computer. [23]

1.2.3.1 Networked Exhibits for Monitoring and Control

It is desirable to have an infrastructure network in place for monitoring and con-trol of exhibits and other units of the Exploratorium. Feedback on the status of adevice and on the user-interaction or -acceptance plays a major role in planning andmaintenance of exhibits. This greatly enhances the operations of such facilities. Inaddition to this management aspect, networked exhibits can then be configured andupdated remotely.

This may go as far as to a vision of a constantly morphing and learning exhibitenvironment.

1.2.3.2 Distributed Infrastructure

Exhibits could be made up of distant components and/or people, with communicationand remote access to each other. Either distant components could interact with eachother, or based on the amount and position of components, certain entities mightform and change.

7


Implementing experiments based on social behavior models or disease spread is oneof the many options that can be realized using PicoRadio. Remote sensing would alsobenefit greatly from a wireless and portable sensor infrastructure. Not only sensorsas part of an exhibit but the analysis of a visitors behavior in the museum is a keyto successful operations.

1.2.3.3 Interactive World

Based on the behavior of the visitors and the grouping of exhibits or parts of ex-hibits, the Exploratorium world might change it’s representation and it’s way ofinteraction.

Cafe

Offices

Entrance

Exhibits

Figure 1-6Exploratorium Tour:PicoRadio enables adistributed but continuousnetwork infrastructure forsensor applications. Atypical visitor to theExploratorium carryingportable PicoNodesenhances his visit byinteracting with exhibitsand self guiding his tour.[23]

With people as an integral part of the networking world a host of new applicationsand behaviors have to be taken into account. The user can individually control hisinteraction and thus account for very specific needs, say a foreign language or moredetailed information on a specific interest. Based on data gathered about a visitorstrip, a dynamic database can be created, that can be explored at home via the worldwide web or be reused on a followup visit.

People roaming about the Exploratorium would be part of the whole, forming clus-ters as they group around exhibits or streams as the walk on designated paths.

Communication and paging services would greatly assist groups visiting the sciencemuseum together.

1.2.4 Navigation DriversPositioning capabilities are the key enabling technology for many of the applicationsfor PicoRadio networks. Certain needs for the navigation capabilities of a PicoRadiosystem can be identified from this specific application scenario:

• Behavior of networking nodes

8

1.3. PicoRadio Architecture

• Integration of positioning into the communication framework

• Collaboration of nodes

• Scalable Service

It is important to keep the different aspects of navigation and positioning seperatedaccordingly when considering applications for PicoRadio. Many PicoRadio applica-tion will not need an absolute knowledge of latitude and longitude information buta mere value of proximity to an other node will be sufficient. A main seperationcan be made for absolute versus relative in combination with position versus rangeinformation. This issue is elaborated in chapter 2.

Since several different applications exist for the geolocation data it is importantto specify the quality of service, availability and cost (hardware, compute cycles,latency and consumed energy) from a system level perspective.

1.3 PicoRadio ArchitectureThe PicoNodes that make up the PicoRadio network essentially all have the samebase capabilities. There is no distinction by origin if a PicoNode is a basestationor a network client node. Only the application and usage of the single PicoNodedistinguishes it from other nodes.

1.3.1 Network TopologyAs stated at the beginning of this chapter a PicoRadio network is comprised ofmany PicoNodes (see figure 1-7) that are dispersed over an area. PicoNodes aredesigned for highly dynamic environments that allow forming high density clustersof PicoNodes, moving PicoNodes and stationary PicoNodes. The configuration of thenetwork (i.e. participating nodes, connections available to the next visible nodes) isestablished on the fly and the network topology is highly dynamic over time as nodesmay not be attached to a fixed location or even appear and disappear within the con-text of the network. There is no master controller by default in this self configuringnetwork environment although there might be locally controlled areas.

For each node the mean distance to the next nodes may vary substantially. Thereforeno constant or even minimum number of networking partners for each node can bespecified. Parts of the network might even be obstructed by physical obstacles suchas depicted by the wall in the upper right of figures 1-7 to 1-9. At certain times partsof the network might even be cut off, forming islands of their own.

Certain nodes can be used as a basestation with a communication link to the outsideworld, such as shown in figure 1-8. Another benefit of these basestations is, thatwhenever isolated islands of nodes have a basestation node as a networking partnerthey will not be cut out of the network context if these basestation nodes have asecondary (i.e. backbone) connection.

9


2

1

3

4

5

6

7

8

9

10

1112

13

14

15

16

17

1819

20

Obstacle/Wall

Figure 1-7PicoRadio NetworkTopology: Interconnectionof PicoNodes in open andobstructed space. A greywall is shown as a sampleobstruction in the upperright corner. A lineindicates a possiblecommunication link in themultihop network. Clusterscan form, move anddissolve freely in the openspace and the networkconfiguration will followaccordingly.

2

1

3

4

5

6

7

8

9

10

1112

13

14

15

16

17

1819

20

Obstacle/Wall

Figure 1-8PicoRadio NetworkTopology: PicoNodes can beemployed as fixedbasestations that establishconnections to the outsideworld or act as a backbonenetwork to relieve thenetwork from too muchexcess traffic over longmultihop distances.

1.3.2 PicoNode SubsystemsEach PicoNode consists of a standard hardware setup that can be configured to servemultiple functions. A generic standard interface can add sensor/actor or userinter-face capabilities to every node. A node might serve as a sensor, i.e. for temperature,light or sound, actor, i.e. remote control terminal, display or even combined sensorand actor, i.e. as a userinterface. This sensor interface is targeted to support datarates up to a few kbyte/sec.

Some key target features of a single chip implementation of a PicoNode core systemare:

10

1.3. PicoRadio Architecture

6

7

10

1112

13

14

15

16

17

1819

20

Obstacle/Wall

Figure 1-9PicoRadio NetworkTopology: The signal rangeof a PicoNode can spanmultiple network hops. Apower controlled radiointerface can thus assistthe ranging mechanismand adjust it’scommunication linkaccording to thetransmitted data type andamount (see [13]).

• System power consumption 5 1 mW

• Radio transmit power 5 0.5 mW

• System overall size 5 1 cm3

• Integrated antenna

• Temporary data storage for sensor data

• Remote update capability for system software

• Ruggedized packaging

• Maintenance free

• Standard I/O interface

1.3.3 Required System SpecificationsDifferent requirements on the networking protocol and the architecture of the PicoNodescan be identified. Some of them vary in their importance or feasibility. To date mostof them are still without quantitative figures. They are given here without a specificrank or order:

• Latency in communication

• Adaptable data types with different bit rates and types of error correction

• Power scalable radio frontend

• Multiple users/streams per node

• Quality of service for applications

• Scalable positioning mechanism

• Unique identifier for every node

• Dynamic scaling of network/nodes

11


• Dynamic (re-)configuration of the network

• Redundant communication links

1.4 Advantages of PicoRadioIssues of mobile communications and ubiquitous computing have been extensivelyinvestigated in [3, 17, 24] and are increasing in importance in public life as well inresearch issues. The idea of everpresent electronic devices is not new, but not widelyavailable today either. By large systems developed in the past can be grouped ineither link based voice communication systems or packet based data systems. All ofthem are either available in point to point or downlink/uplink style or as a broadcastsystem.

PicoRadio is targeted at the extreme of minaturisation and also availability. Com-munication nodes will be present everywhere and at a very small cost of resourcesper node: infrastructure, size, capacity, coverage, cost and especially power are alltargeted to be magnitudes smaller than those of comparable systems. The key ideabehind this is the flexible ad-hoc networking protocols that operate the PicoRadiosystem. Positioning and location information will be very important not only toemerging applications but also to these protocols.

The distributed infrastructure developed through the deployment of very manyPicoNodes makes up the system. Large quantities of PicoNodes will be availablewith often more than one node per entity or application. A single node by itself willnot be very useful. Through this scheme PicoRadio systems can be deployed every-where and instantly.

12

2Fundamentals of Navigation

Navigation can be defined as means by which we can travel from one known locationto another, determining the position and path on the way [28]. Traditionally it canbe divided into 5 methods:

• Piloting - along fixed waypoints

• Dead Reckoning - determining a series of measured velocity increments

• Celestial Navigation - by usage of precisely timed sightings of stars, planetsand the moon

• Inertial Navigation - by usage of integrating accelerometers mounted on gy-roscopically stabilized platforms

• Radionavigation - employing (the time of flight of) electromagnetic waves

The focus of this document is on radionavigation.

2.1 RadionavigationRadionavigation techniques can be based on measuring received signal strength(RSSI), angle of arrival (AOA), the time of arrival (TOA) or time distance of arrival(TDOA) of a signal [38]. Three or more independant signal measurements may beused to solve for a triangulation solution.

While calculating direction using AOA requires additional antenna hardware thatneeds to be precisely calibrated, the systems based on the time of flight of electro-magnetical waves require very acurate timing measurements and thus high syn-chronicity.

In a networked environment of many navigating nodes, cell-site density, the distri-bution of nodes, the ability to share information, terrain and physical obstructionplay a major role in the accuracy derived by the system.

Radionavigation can additionally be divided into two classes, active and passiveradionavigation [28].

13

Chapter 2: Fundamentals of Navigation

2.1.1 Active RadionavigationIn active radionavigation the navigation receiver broadcasts a signal f1 to a distanttransmitter or basestation that bounces the signal back at the receiver, possibly ata different frequency f2, as seen in figure 2-1. For example, the range between thereceiver and distant transmitter is computed by half the time of flight multiplied bythe speed of light c = 3 · 108 m/sec.

s = c∆t

2(2.1)

Local

Remote

f1

f2

Figure 2-1Active radionavigationuses a bidirectional linkbetween the distant andlocal navigation node. Theup- and downlink signalshown by f1 and f2 mustnot be the same.

2.1.2 Passive RadionavigationIn passive radionavigation a distant transmitter sends out a series of preciselytimed pulses f1. The navigation receiver pics up these pulses, measures the timeof flight and multiplies by the speed of light analogous to the previous solution.

s = c∆t (2.2)

Local

Remote

f1

Figure 2-2Passive radionavigationsystems rely on thecontinuous availability oftimed pulses from fixedbeacons.

Similar methods are being used for measurements of received signal strength.

2.1.3 Communication Links and RadionavigationA radionavigation system may or may not be able to communicate and thus sharedata used to solve for the position. Radionavigation systems that cannot communi-cate have to rely solely on their ability to detect an electromagnetic signal. They canemploy methods based on signal strength, doppler shift and AOA measurements.

If communication is available, data can be modulated onto the navigation signal orbe available on a secondary communication link. Combinations of modulated data

14

2.2. Some Geometric Principles

and secondary links, for example differential GPS, are possible too. The data sourceof the modulated signal assists in deriving the GPS solution, the secondary commu-nication link in correcting and refining the solution.

TDOA and TOA measurements require a minimum of transmitted data consisting ofat least a pulseshape, used for synchronization and thus measuring the time of flightof a signal, to be exchanged. Depending on the signal and the range anticipated,larger amounts of data can be transmitted.

Type of Communication Type of NavigationNo Communication Ranging Radiosignals, Transit [28]

Unidirectional BidirectionalModulated CommunicationSignal

Passive Radionaviga-tion; Global PositioningSystem

Active Radionavigation

Secondary Link Communi-cation

Identification Uplinks;Tag Systems

Cell Based Navigation

Modulated Signal and Sec-ondary Link

Differential GPS Network Assisted GPS[18]

Table 2-1: Communication Links Assist Radionavigation

For a host of applications, what is really desired is location within a building or anarea, or location relative to other people or objects, whether moving or stationary.Most positioning systems allow autonomous determination of position, yet this in-formation must be communicated by a seperate mechanism in order to be shared.Knowing one’s latitude and longitude is useless without additional information suchas a map.

Measures of quality of service (QoS) in location services for communication systemsinclude accuracy, periodicity and reponse time [43] as well as coverage area, avail-ability and deployment cost (see [32, 38]).

2.1.4 Applications and Services for PositioningPositioning may be initiated by a node, application or the network and is subjectto various restrictions based on capability, security and service profiles that lead todifferent classifications of location services such as the ones shown for GSM in table2-2 [43].

2.2 Some Geometric PrinciplesCommonly navigation data is given as latitude, longitude and height. Speed, accel-eration and bearing can account for the dynamics in the position of moving objects.But the navigation data is only useful in the context of a map system, i.e. a reference.

It is thus important to differentiate further between absolute and relative naviga-tion data as well as position, orientation and distance in the stationary state.

15


Location Service Function ApplicationMobile-originated re-quest for positioning

Mobile station requestsits location from the net-work

Aid for driver navigation

Positioning allowed on aper service instance

Mobile station grants anexternal application atemporary allowance toposition the mobile forthe sake of delivering aspecific service

Mobile yellow pages, i.e.find the nearest hotel tomy location

Network-originatedrequest for positioning

Network requests the po-sition

E-911 location serviceor location-dependentbilling

Positioning without mo-bile station identification

Positions the mobilewithout mobile stationidentification

Traffic incident detectionof cellular hot spot activ-ity locator

Position with mobile sta-tion identification

Provides the position ofthe mobile in a definedarea with mobile stationidentification

Used to detect mobileunits in a high securityregion

Positioning within aclosed group

Allows for special rightsin determining position

Fleet and asset tracking,authentification

Table 2-2: Classification of Location Services for GSM

ñy

x

z

P0

r0

rp

Figure 2-3Position and Orientation: Sixdegrees of freedom are attributedto a single position for each objectin stationary state: three positioncoordinates and three orientationcoordinates often given in polarcoordinates.

A position or point is defined by it’s coordinates, i.e. (x, y, z). A distance is the vectorbetween two points P1 and P2 given by:

|~r| =√

(x2 − x1)2 + (y2 − y1)2 + (z2 − z1)2. (2.3)

Initially each distinct position attributed to a real object has six degrees of freedom:Three position coordinates (x0, y0, z0) for every point and the orientation coordinatesat this position given by (xP , yP , zP ). (see figure 2-3) Many navigation application

16

2.2. Some Geometric Principles

treat the orientation coordinates as an invariant or derive them via the system dy-namics given over time. The correct geometric solution would be to have two or morepositions defined per object or establish exact angular measurements.

Every set of geometric data is only of use when applied within the context of aninertial system. Inertial systems exist for numerous applications and can be inter-changed using standard transformations. The standard transformation from carte-sian coordinates (x, y, z) to cylindrical coordinates (three dimensional (θ, ρ, z) givenin equations 2.4 and 2.5) or polar coordinates (two dimensional (θ, ρ)) and sphericalcoordinates (three dimensional (θ, φ, ρ) given in equations 2.6 and 2.7) is illustratedin figure 2-4:

θ = arctany

xρ =

√x2 + y2 z = z (2.4)

x = ρ · cos(θ) y = ρ · sin(θ) z = z (2.5)

θ = arctany

xφ = arctan

z√x2 + y2

ρ =√

x2 + y2 + z2 (2.6)

x = ρ · cos(φ)cos(θ) y = ρ · cos(φ)sin(θ) z = ρ · sin(φ) (2.7)

θ

ρ

y

x

Py

x

zP

P*φ

ρ

θ

y

x

zP

P*ρ

z

θ

Figure 2-4Cartesian and PolarCoordinate Systems: Twodimensional polar andthree dimensionalcylindrical coordinates areshown on the left, threedimensional sphericalcoordinates on the right.

2.2.1 Relative PositionVectors linking two points give information on their position relative to each other.When no reference points are given, the solution can be rotated and mirrored throughan arbitrary axis. Each position added to a system solution reduces the problem onedegree of freedom at a time.

A system of many known positions that has no reference location and/or orienta-tion is only fixed in itself, not in it’s position in space: If only the position of onepoint and no orientation is given, the system can be mirrored and rotated throughany axis leading through this point. The translatory movement is prohibited by thisfirst known position. When a second position is introduced into the system the rota-tion of the system is further restricted to the axis through these two points. A twodimensional problem would thus still have two possible solutions. A third knownposition finally fixes the system in three and two dimensional space.

17


2.2.2 Absolute PositionAn absolute position is given in respect to an inertial system and a reference pointin this inertial system. It allows to determine positioning information of disjunctsystems independently, in reference to the same point in the inertial system.

Dynamic navigation systems can surpass this problem partially by adding motionin form of a history of past positions and/or velocities to the problems solution.

y

x

z

P1*

φP1

ρP1

θP2

ρP2

rP

1

P2

Figure 2-5Vector Geometry forPositions: The vectors −→r ,−−→ρP1 and −−→ρP2 shown in thisfigure allow for absolutepositioning of points P1

and P2 relative to thereference coordiante system(x, y, z).

The absolute position of the distant points P1(xP1, yP1, zP1) and P2(xP2, yP2, zP2) infigure 2-5 is given by the vectors −→ρP1 = ((x0 + xP1), (y0 + yP1), (z0 + zP1)) and −→ρP2 =((x0 + xP2), (y0 + yP2), (z0 + zP2)) to the reference point (x0, y0, z0) respectively. Theposition of point P2 can additionally be derived by −→ρP2 = ~r+−→ρP1 The relative distancebetween the points P1 and P2 is

|~r| = |−→ρP1 −−→ρP1| = (2.8)

=√

((x0 + xP2) − (x0 + xP1))2 + ((y0 + yP2) − (y0 + yP1))2 + ((z0 + zP2) − (z0 + zP1))2

which can be reduced to

|~r| =√

(xP2 − xP1)2 + (yP2 − yP1)2 + (zP2 − zP1)2

and so does not include the reference point (x0, y0, z0) anymore.

In addition every observer of a geometric system can act as an absolute referencepoint in an inertial system, namely their own inertial system such as it is commonon inertial platforms. The GPS system references the earthcentered geoid, LORANfixed beacons on the earths surface.

2.2.3 TriangulationAny arbitrary triangle with the sides a, b, c and the opposing angles α, β, γ can bedescribed with of a combination of three sides and/or angles when applying the lawof sines:

18

2.3. Triangulation with Range Measurements

a

sin(α)=

b

sin(β)=

c

sin(γ)(2.9)

and cosines:

c2 = a2 + b2 − 2ab · cos(γ) (2.10)b2 = a2 + c2 − 2ac · cos(β) (2.11)a2 = b2 + c2 − 2bc · cos(α) (2.12)

Adjoining or overlapping sets of triangles can then be used to calculate the positionand range of disjoint locations.

2.2.4 Geometric ErrorsIt is important to keep in mind that to some extent, all measurements are associatedwith a certain improbability. These might be propagated through computation to beaccumulated or eliminated.

When each observation x0 of an actual value x is associated with an absolute error∆x = x0 − x the sum or difference of several observations x0,1..n will be associatedwith an absolute error less or equal than the sum of the single absolute errors:∆x ≤ ∑n

i=1 ∆xi.

The relative error δx is given by: δx = ∆xx = x0−x

x

For N samples with sample mean x the variance is defined by:

σ2 = var(x) =1

N − 1

N∑i=1

(xi − x)2 (2.13)

For the anticipated range of PicoRadio (3-10 m) an absolute error of i.e. 5% resultsin ∆x(5%) = ±0.15 . . . ± 0.5 m on a single range measurement.

2.3 Triangulation with Range MeasurementsTriangulation methods can be used in conjunction with angle and range data tosolve for remote positions. In focus of the application of radionavigation to PicoRadioonly range vector data from range measurements such as TDOA, TOA and RSSI orsimilar is considered as available input data in the scope of this document.

A range measurement between two nodes reduces the navigation problem one de-gree of freedom at a time. When there is no reference point available only positionsrelative to each other can be computed.

19


2.3.1 Triangulation for Relative PositionsWhen no fixed reference positions are known, a triangulation can only resolve theposition of nodes relative to each other. They can then still float and be rotated ar-bitrarily in free space. The geometric navigation solution derived here is always therelative distance beween points. Figures 2-6, 2-7 and 2-8 illustrate this procedurefor relative positioning of nodes.

2

1

3

4

5

12

2

1

3

4

5

12

Figure 2-6With just the two ranges measured from node 5 to node 2 and 4 (5-2 and 5-4 shown on theleft) no position can be ascertained for points 2 and 4 relative to node 5. Valid positions lieon circles around any of the nodes. After the range 2-4 has been introduced to the system, thesolution can be rotated and mirrored on any or a combination of the three points depicted ingray (right).

2

1

3

4

5

12

2

1

3

5

12

4

Figure 2-7With an observer in the position of node 5, the system shown on the left will still be free torotate about this relative reference point or be mirrored on an axis leading through thispoint as long as only this one reference is part of the system (left). When two absolutereference positions (node 3 and 5)are known, the relative position of node 2 shown on theright, is limited to two possible positions, denoted by the red dots. Node 4 can still bemirrored on the axis 5-3, resulting in 4 possible positions for the whole configuration (right).

20


2

1

3

4

5

12

2

3

4

5

12

6

10

1

Figure 2-8Another vector 3-4 introduced again reduces the possible positions for node 2 and 4. Nowonly the two mirrored positions along the axis 5-3 are possible (left). Only large amounts ofoverlapping range vectors will allow to distinguish the exact absolute position (right). Threereferences are necessary to gain absolute position information. Depending on the distancefrom these reference positions, the error increases in the net of vectors involved.

The system of range vectors can be described using a weighted graph G(v, e), withvertices v and edges e, where the weights denote the distance associated with eachedge. Such a system can easily be expressed using matrixes.

2.3.1.1 Errors in Relative Positions

When three range measurements for vectors −→a , −→b , −→c are given with a relative error(i.e. δx = 5%), then the observed pseudorange values are given as (a0 ± 0.05 · a0),(b0 ± 0.05 · b0), (c0 ± 0.05 · c0). Using equations 2.9 and 2.10 the angles of the triangleABC will be given by:

γ = cos−1

((a0 ± 0.05 · a0)2 + (b0 ± 0.05 · b0)2 − (c0 ± 0.05 · c0)2

2(a0 ± 0.05 · a0)(b0 ± 0.05 · b0)

)(2.14)

approximated by:

γ ≈ cos−1

(a2

0 + b20 − c2

0 ± 0.1(a20 ± b2

0 ∓ c20)

2(·a0b0 ± 0.1 · a0b0)

). (2.15)

The other angles are derived accordingly by substitution.

From position C position B (see figure 2-9) containing a 5% range error would bedescribed as:

xB = xC + (a0 ± 0.05 · a0)cos(γ) yB = yC + (a0 ± 0.05 · a0)sin(γ) (2.16)

If the initial position C is offset by a similar error (i.e. δx = 5%) the final positionB will be maximally offset by the sum of the two errors (i.e. δxtot = 10%). Multi-ple range vectors can reduce the geometric error if the geometric constellation is

21


C

B

y

x

zRangeError

Angular Error

Figure 2-9Errors in Range Measurements:The range and angular errors arepropagated through a network ofvectors. The error of the startingpoint adds to the error of theendpoint of a vector. A singlepseudorange vector onlyintroduces a range error, but thetriangulation solution will resultin erroneous angle values as well.

favorable. (A practical method based on the geometric constellation is explained inchapter 3)

0 500 1000 1500 2000 2500 30000

0.5

1

1.5

2

2.5

angl

e

alphabeta gamma

0 500 1000 1500 2000 2500 30001

2

3

4

5

6

leng

th

samples

abc

Figure 2-10Errors in Triangulation:The angles of a triangleABC given by the sidesa = b = c with a randomerror of δa,b,cx = 5% areshown in the figure on theleft. The first partcorresponds toa = b = c = 5, then a = 2,b = c = 5 and on the righta = 2, b = 5, c = 4. Theimpact of the shape of thetriangle shows that theideal shape is anequilateral triangle.

When it is assumed that the error of a pseudorange estimate is somewhat correlatedto the length of the pseudorange the single pseudorange error will contribute to theerror of the solution more when the specific pseudorange is much larger or smallerthan the mean pseudorange used in the solution. This can easily be understoodwhen considering that on a longer path a signal will be deteriorated more than on ashorter path.

2.3.2 Triangulation for Absolute PositionsThe absolute position of a remote user position can be computed using range mea-surements to known positions.

Multiple range measurements (TOA, TDOA or RSSI) or angles (AOA) are necessaryto determine the exact 3D position on a remote navigation receiver. To solve for the

22


a b c var(a) var(b) var(c) var(α) var(β) var(γ)5 5 5 0.0209 0.0207 0.0202 0.0016 0.0017 0.00162 5 5 0.0034 0.0207 0.0202 0.0002 0.0101 0.01012 4 5 0.0034 0.0207 0.0130 0.0010 0.0149 0.0089

Table 2-3: Geometric Error in Triangulation of a Triangle ABC

three position coordinates Ux, Uy, Uz, of the users absolute position, at least threerange measurements are necessary.

y2

x2

z2

y3

x3

z3

y1

x1

z1

(Ux,Uy,Uz)

R1R2

R3

Figure 2-11Three Dimensional AbsolutePosition: The three dimensionalabsolute position is determined bypseudorange estimates to at leastthree known positions.

A set of equations based on range measurements and three dimensional cartesiancoordinates is given here. It can be easily adapted to other coordinate systems (seesection 2.2) or AOA observation data. This example assumes that the transmitterof the pseudorange measurement a priori knows it’s position and can transmit thisdata to the remote receiver (derived from the GPS system described in chapter 3[28, 8]).

The navigation equations for n given positions are given by:

(X1 − Ux)2 + (Y1 − Uy)2 + (Z1 − Uz)2

(X2 − Ux)2 + (Y2 − Uy)2 + (Z2 − Uz)2

(X3 − Ux)2 + (Y3 − Uy)2 + (Z3 − Uz)2...

(Xn − Ux)2 + (Yn − Uy)2 + (Zn − Uz)2

=

R21

R22

R23

...R2

n

(2.17)

Xi, Yi, Zi denote the current position of transmitter i, Ri the pseudorange informa-tion measured for transmitter i. For more than four available pseudorange mea-surements, the solution is overdetermined and can be solved using minimum meansquare error (MMSE) methods.

By subtracting the last line, this can be linearized to a system of

2X∗u = r∗ (2.18)

where

23


X∗ =

(X1 − Xn) (Y1 − Xn) (Z1 − Xn)(X2 − Xn) (Y2 − Xn) (Z2 − Xn)(X3 − Xn) (Y3 − Xn) (Z3 − Xn)

......

...(Xn−1 − Xn) (Yn−1 − Xn) (Zn−1 − Xn)

,

u =

Ux

Uy

Uz

and

r∗ =

R21 − R2

n − X21 + X2

n − Y 21 + Y 2

n

R22 − R2

n − X22 + X2

n − Y 22 + Y 2

n

R23 − R2

n − X23 + X2

n − Y 23 + Y 2

n...

R2n−1 − R2

n − X2n−1 + X2

n − Y 2n−1 + Y 2

n

that can be solved using a MMSE for overdetermined systems such as the QR factor-ization algorithm. Other methods that are mostly geared towards an efficient com-putation of coordinates use hyperbolic equations, dynamically offsetting the triangu-lation points and reference planes [16] and supplemental information (see chapter3).

2.3.2.1 Errors in Absolute Triangulation

The overlapping vectors used to solve for an unknown position (see figures 2-11,2-12 and 2-14) each contribute an error to the solution as it was described in theprevious sections. Depending on the geometry and amount of vectors used for thesolution of the MMSE equations a total error for the position results.

y'

x'

z'

y

x

z

Range

Error

AngularError

A

B

C

Figure 2-12Absolute Positioning Errors: Thegeometry can assist in reducingthe error in positions derived frommultiple vectors. When solving theposition for B from the knownpositions A and C throughtriangulation the possiblepositions lie in the red field only.In this case the resulting error forB will be much smaller than thesingle error of the vectors −−→

AB and−−→CB.

24


When a known position with a position error is used as local reference for a newposition computed through triangulation as shown in figures 2-9 and 2-12 from po-sitions A and C to the new position B the position error is propagated additively butis limited to the possible solutions of the triangulation (shaded area in figure 2-12).

1 2 3 4 5 6 7 8 90

0.02

0.04

0.06

0.08

0.1

side c with an error of 5%

rang

e er

ror

BAxBAyCBxCBy

1 2 3 4 5 6 7 8 90

0.2

0.4

0.6

0.8

1

side c with an error of 20%

rang

e er

ror

BAxBAyCBxCBy

Figure 2-13Absolute TriangulationError Estimation: The twosets of error curves shownhere are the error for asolution to figure 2-12 for atwo dimensional trianglewith the given lengthsa = 4, b = 5 and variablelength c = 1.2 . . . 8 forerrors of δx = 5% (top) andδx = 20% (bottom). Itshows analog to the resultsin figure 2-10 that thehighest accuracy is derivedfor angles between 60◦ and90◦.

A simulation of triangulation from very many known positions according to the algo-rithm outlined in the previous section shows considerable improvement in accuracywhen more than 5 nodes are used. When the errors of the single ranges are corre-lated, the resulting solution for equations 2.17 is associated with the same error.This correlated error can only be resolved through iterations, assuming that at dif-ferent times different errors occur.

A sample simulation for 12 randomly placed nodes is shown in figure 2-14. Here5 iterations using 5 sets of uncorrelated errors to model every pseudorange to thenode in the middle (x = 0.5, y = 0.5) results in five position estimates denoted by thered crosses. The mean position averaged over these 5 position estimates is shown asthe pink star.

More simulated constellations using 3, 5, 12 and 35 nodes as well as 50% and 5%pseudorange errors and different iteration intervals can be seen in appendix A.

The simulation shown in figure 2-15 is based on 100 iterations and shows the in-crease in accuracy, especially when 7 and more nodes are used in the solution andvery high errors occur. In average an increase from 3 to 10 nodes used in the solutionresults in a decrease of the position estimate error of by a factor of 4. The occasionaloutliers seen in the simulation result from very poor geometric configurations dueto a random placement of the nodes and their errors. In the simulations the actualposition has no influence on the error of the pseudorange, which is not the case in areal environment, where obstruction of LOS and noise sources are the main sourcesof impairment.

25


0 0.5 10

0.2

0.4

0.6

0.8

1Delaunay Mesh of 12 Remote Nodes

x

y

0 0.5 10

0.2

0.4

0.6

0.8

1Solution on 12 Ranges and 0.5 Error

x

y

0 0.5 10

0.2

0.4

0.6

0.8

15 Solutions and Mean

x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y

dx 0.032dy −0.04

Figure 2-14Absolute Positioning ErrorEstimate: Thepseudoranges from 12randomly placed stationarynodes have been used hereto simulate the positionderived using a MMSE topoint (x = 0.5, y = 0.5).Each pseudorange wasgiven an uncorrelated errorof 50%. The red crossesdenote the single solutions,the pink star the meansolution over 5 iterationswith different errrors each.The lower right hand plotshows a zoom onto thetarget position.

0 5 10 15 20 25 30 350

0.02

0.04

0.06

var(

dx,d

y) 50% range error

0 5 10 15 20 25 30 350

0.01

0.02

0.03

25% range error

var(

dx,d

y)

0 5 10 15 20 25 30 350

1

2

3

4x 10

−4

Nodes

var(

dx,d

y) 5% range error

Figure 2-15Geometric Error on ManyNodes: The three sets ofvariances shown here showthe continuous decrease inthe position estimate errorsdx and dy when multiplenodes are used for thesolution. The occasionalspikes still seen result fromvery bad geometry/errorconstellation. The threeplots are based on seperaterandom placements ofnodes and errors.

26

2.4. Issues on Radio Propagation

2.4 Issues on Radio PropagationThe wireless environment is characterized by a variety of factors impeding reliablecommunication. Propagation of radio transmission is influenced by many factors.Walls and floors tend to decrease and reflect the signal. Background noise makesthe signal more difficult to extract.The channel quality might vary quite a lot overtime (fading).

The problem of radio propagation and communication is best divided into the radiochannel and the surrounding noise environment [51].

2.4.1 Radio ChannelsTypical non-line-of-sight (NLOS) land mobile radio channels have a characteristicrandom multiple path radio propagation with the principal parameters of multipathfading, shadowing and path loss [17]. Traditionally the transmission link of such asystem is modelled by an elevated base-station antenna, a relatively short line-of-sight (LOS) propagation path followed by many long NLOS reflected propagationpaths and an antenna on a mobile transceiver. Many of these channel models aretargeted towards long propagation paths (several hundred meters to several kilo-meters), high transmit power and motion of the user, i.e. receiver. Not a great dealof work has been achieved in environments with many interacting moving users, i.e.moving transceivers and on very low radio ranges.

Variations in the channel occur due to changes in the static environment, movinginterferers and motion of the user/transceiver [54]. When natural and constructedobstacles and especially movement in the environment or a transmitter result inmultiple signal paths the situation is referred to as multipath propagation, influ-enced by the following factors: The rate of variations of a signal between movingobjects is referred to as doppler spread. This is then a time selective or time variablesignal phase on the received signal. Envelope fading is the non frequency selectiveamplitude distribution, time delay spread a time variation due to reflections.

Obstacles in the way of line-of-sight will shadow certain regions. Large surfacesreflect and sharp edges diffract signals. The scattered signal depends strongly onthe dimension of the obstacles and the materials characteristic surface absorbtion.

2.4.2 Noise EnvironmentThe noise of the environment, stationary and moving noise sources, has to be ac-counted for in the channel model. The noise sources are randomly distributed andusually time variant. One effect of this is, that the channel will not be symmetricdue to the noise sources.

The noise present on a channel significantly reduces the raw bandwidth available forcommunication. Different coding techniques can improve the achieved bandwidthefficiency. For distance measurement it remains a problem though.

2.4.3 Computational ModelA constant-envelope phase-modulated signal sT (t) is given by

27


sT (t) = Aej[ωt+Ψs(t)] (2.19)

where A is a constant amplitude, ω the angular radio frequency, Ψs(t) the phase orfrequency modulated baseband signal [17]. For dynamic environments or movingtransceivers, the time variable random propagation medium p(t) is given by

p(t) = r(t)ejΨr(t) = m(t)r0(t)ejΨr(t) (2.20)

with r(t) containing an average fading term m(t) and a short or fast multipath fad-ing term r0(t) and Ψr(t) the time variable random phase of the medium. These equa-tions can be easily adapted to a position variable propagation medium by substitut-ing t by the motion equation x = v · t(m).

In a multiplicative fade model such as most applicable for mobile channels, the re-ceived signal sR(t) is given by

sR(t) = sT (t) · p(t) = Aej[wt+Ψs(t)] · m(t)r0(t)ejΨr(t). (2.21)

The received signal strength sR(x) at the distance x will thus follow the averagefading m(x) with strong impact of the fast multipath term r0(t). Even a movementin the order of a few hundredths of the signal wavelength λ will influence the signalenvelope. For example a signal of f = 2.5 GHz has a wavelength of λ = 12 cm. Amovement of only x = 1 cm will thus result in a rate of 1/12 ≈ 8/100 · λ leading tosignal envelope fluctuations [17].

The random phase variation Ψr(t) is the cause of the frequency or doppler spreadsince phase and frequency are correlated.

The free space propagation loss of a transmitted power PT versus a received powerPR is given by

PR

PT= GT GR

(c

4πrf

)2

(2.22)

with GT and GR the transmit and receive antenna gain and r the distance betweenthe two antennas.

The propagation loss LF in [dB] for the LOS component is derived by

LF [dB] = 10logPR

PT(2.23)

resulting in a typical 1/r2 decrease. Commonly this is extended to a combined LOSand NLOS path loss of

LTOT (r) = LLOS(r0) · LNLOS(r − r0) = GT GR

(c

4πr0f

)2

·(r0

r

)n(2.24)

28

2.5. Accuracy and Reliability of Radionavigation

with the exponent n characterising the NLOS environment. Typical values for n are3.5 ≤ n ≤ 5 for outdoor and 2 ≤ n ≤ 4 for indoor channels. Furthermore it is commonto set the antenna gain to the unity gain G = 1 for omnidirectional antennas [17].

The direct influence of increased frequency on the signal propagation can be seen inthese equations as well.

The main problem with this model is, that it is primarily used for and based on em-pirical data of long signal propagation distances. Actually many magnitudes longerthan anticipated for PicoRadio.

2.4.4 Ultra Wideband Pulse RadioThe idea of ultra wideband impulse radios is not a totally new one, but it is bylarge unused for communication systems today. Similar to a radar system, codedsequences of gaussian impulses [41] are transmitted and received.

These nonsinusoidal impulses are the actual baseband signals, which means, thereis no carrier frequency associated. A single very short pulse of duration Tm is trans-formed to the frequency domain, its energy spans the frequency band from dc up to2/Tm Hz [44]. A 1 ns pulse is spread at frequencies below 1 GHz, modulated sinewaves need at least 30-60 GHz for this bandwidth.

The transmitted signal is generated by applying current steps through a Large-Current Radiator (LCR) [21, 22] antenna. This launches an impulse when the cur-rent is turned on or off. The radiation power launched is proportional to the squareof the derivative of the current flow.

In order to be able to operate in the highly populated frequency bands below a fewGHz spread spectrum techniques must be applied to cope with interference anddistortion.

Channels can be distinguished through pseudo random codes such as used in DSSSsystems. A time integrating correlator is proposed by Aetherwire[41]. Doublets of apositive and a negative pulse (much like a DSSS chip) allow for easy detection of thecorrelation sequences.

Benefits of this technology are a very low power spectral density, good penetrationof obstacles and superior multipath resolution due to the inherent precision timingcapability. Positioning mechanisms clearly benefit from the high precision synchro-nisation of the system as a centimeter accuracy in theory requires at least 1 GHz ofbandwidth.

An exact TOA or TDOA measurement with this technique seems to be quite feasiblealthough it would have to be evaluated if it can achieve the same level of accuracyon very short distances (1-10 m) as on long distances (∼km).

2.5 Accuracy and Reliability of RadionavigationThe electromagnetic waves used for these range measurements can be easily de-teriorated by athmospheric disturbances, other electromagnetic signals as well asphysical obstacles.

29


Very low frequency transmission waves reflect off of the ionosphere, an effect calledwave form ducting. This can be employed to broaden the area of coverage but in-creases the variation in the received signal. Moreover only very little data can bemodulated onto these carrier waves.

Older groundbased radionavigation systems like the Transit system that were run-ning on wavelenghts of multiple kilometers [28] measured the phase difference ofsignals only, resulting in lane ambiguities that would allow an accuracy of up to±1

2wavelength only. By using different frequencies for each range measurement in-troduced to the triangulation solution these lane ambiguities can be extracted sinceevery one of them is characteristic to a specific frequency.

When the carrier frequency is increased the higher frequencies will no longer bereflected by the ionosphere. This is beneficial in terms of the carried data bandwidthbut also means beeing in line of sight with the transmitter. Groundbased navigationsystems are thus restricted to a very small region per transmitter only.

On spacebound navigation systems the signal is bent and slowed down while passingthrough the earths ionosphere. These ionospheric delays depend on the amount ofloaded particles along the line-of-sight vecor of the signal but is related to ∆t ∼ 1

f2 ,thus can be eliminated by using two different transmission frequencies.

When passing from one horizon overhead to the next horizon the amount of athmo-sphere passed by the signal varies greatly. These tropospheric delays can be limitedby imposing a minimum elevation angle (mask angle) of 5-10◦ for the signals incor-porated in the navigation solution.

On mobile communication systems the limited bandwidth in the AMPS standardand its loosely defined signal structure create a challenge to accurate location cal-culation while the use of power control in CDMA communication systems creates aheadache for every triangulation scheme.

In a strong multipath environment the strongest signal might not be the first firstsignal to arrive. This makes detecting the direct line path quite difficult.

30

3The GPS System

The Global Positioning System (GPS) was set up by the U.S. Department of Defense(DoD) to enable global 3D-positioning and timing services. It has been operationalsince 1993. Today, two positioning services are available: Standard Positioning Ser-vice (SPS), which provides civilian users a global 100 m accuracy and Precise Posi-tioning Service (PPS) providing 20 m accuracy to military users.

Figure 3-1GPS System Segements:The GPS system is dividedinto a user, space andcontrol segment. It wasinitially designed in the1960’s and is constantlybeing updated.

3.1 Fundamental Properties of GPSThe GPS system uses 24 satellites (Space Vehicles) on six orbital planes inclinedat 55◦ relative to the equatorial plane (see figure 3-2). The orbit is approximately26560 km above the center of the earth, with an orbital period of 12 h sidereal timeon asymmetrically repeating ground tracks. The sidereal time is the earths rotationrelative to the fixed stars and is approximately four minutes shorter than a solarday. Each satellite has a covererage of approximately 42% of the globe.

31

Chapter 3: The GPS System

Figure 3-2Global Configuration of theGPS Satellites

3.1.1 GPS SignalsEach GPS satellite continously broadcasts direct-sequence, spread-spectrum (DSSS)signals on two right-hand circular polarized L-band frequencies [14, 28]: fL1 =1575.42 Mhz and fL2 = 1227.60 Mhz.

Figure 3-3The GPS L1 and L2

downlink signals carryingthe SPS and PPSinformation.

sL1(t) =√

2CXD(t)X(t)sin(2πfL1t + θL1) +√

2CP D(t)P (t)cos(2πfL1t + θL1) (3.1)

sL2(t) =√

2CL2D(t)P (t)sin(2πfL2t + θL2) (3.2)

The L1 signal as given in equation 3.1 contains an in-phase component with thepower CX and a quadrature component with the power CP . The L2 signal as given in

32

3.1. Fundamental Properties of GPS

equation 3.2 contains an in-phase component with the power CL2 only. They are re-ceived on earth at a power level of -160 dBW for CX , -163 dBW for CP and -166 dBWfor CL2.

Each component of the two signals are modulated bith the 50 b/s navigation dataD(t). The navigation data is developed by the GPS ground control segment andcontains the satellite’s location and clock offset and some status information data.

A pair of spread-spectrum codes X(t) and P (t) is modulated onto the carrier. Itmodulates the amplitude of the carrier wave with a pseudorandom sequence of ±1at a certain modulation rate. This enables to distinguish the service (SPS or PPS)available. The part of the L1 code modulated by X(t) is known as clear or coarseacquisition (C/A code) code and is modulated at 1.023 MHz, each chip with 1/1.023 µsof duration, spreading the bandwidth of the L1 component by a factor of 20 000. Theother spread-spectrum code P (t) is referred to as precision code (P(Y) code) anduses a chipping rate of 10.23 MHz, spreading the bandwidth of the quadrature L1

component L2 signal by a factor of 200 000. This P-code can be switched to a securecode known only to authorized users [28, 8, 7].

Spread-spectrum is vital to the function of GPS. It enables precise ranging measure-ments in the presence of noise, reflected signals and interfering signals. Additionallyit allows each satellite to broadcast continously on the same two frequencies. Thedifferent satellites are distinguished from each other, using code division multipleaccess (CDMA) based on each satellites unique C/A and P-codes.

3.1.2 GPS MeasurementsAn introduction on how to compute a 3D position based on range measurements hasbeen given in section 2.3. A commonly applied algorithm [14] is demonstrated in thefollowing.

For passive range measurements based on the time of flight of an electromagneti-cal signals, precisely synchronized clocks are vital in both the transmitter and thereceiver. Since this is seldom practical, the receivers clock bias error cB can be ex-tracted by introducing a fourth range measurement into a minimum mean squareestimate (MMSE) solution. The measured arrival time (pseudorange) correlated bythe GPS receiver Ri and the space vehicle’s (SV) location in space Xi, Yi, Zi is intro-duced to a system of linear equations derived from equation 3.3:

(X1 − Ux)2 + (Y1 − Uy)2 + (Z1 − Uz)2

(X2 − Ux)2 + (Y2 − Uy)2 + (Z2 − Uz)2

(X3 − Ux)2 + (Y3 − Uy)2 + (Z3 − Uz)2

(X4 − Ux)2 + (Y4 − Uy)2 + (Z4 − Uz)2

=

(R1 − cB)2

(R2 − cB)2

(R3 − cB)2

(R4 − cB)2

(3.3)

The range and location of at least four SV’s are necessary to eliminate the clockbias error cB given by cB = Ub − Bi, Ub denoting the receivers clock and Bi the GPSsystem time of SV i. A variety of other measurements errors are combined in ∆Ri

and added to the set of equations given in equation 3.3:

Ri =√

(Xi − Ux)2 + (Yi − Uy)2 + (Zi − Uz)2 + cB + ∆Ri (3.4)

33


The resulting set of equations is linearized to calculate the navigation solution. LetδUx, δUy, δUz , δUb be departures from the best a priori estimate of Ux, Uy, Uz, Ub andlet δρi be the corresponding perturbation of the pseudorange. The position coordi-nates are ordered in a right hand set: East, North and Up. The SV azimuth azi ismeasured clockwise from true North and the elevation eli is measured up from localhorizontal. For small position and time errors the linearized equations for i SV’s canbe written as:

cos(el1) sin(az1) cos(el1) cos(az1) sin(el1) 1cos(el2) sin(az2) cos(el2) cos(az2) sin(el2) 1

......

......

cos(eli) sin(azi) cos(eli) cos(azi) sin(eli) 1

δUx

δUy

δUz

δUb

=

δρ1

δρ2...

δρi

(3.5)

If xU denotes the pertubation [δUx, δUy , δUz , δUb]T and ρ = [δρ1, δρ2, . . . , δρi]T , thenwe can simply write

GxU = ρ (3.6)

where G is determined by the user-satellite geometry and is thus called geometrymatrix.

az4

Local

North

az1

az2

az3

el4

el3el2

el1

Figure 3-4Satellite Geometry:Depending on the volumespanned by the satellitesand the ground transceiver,the geometric dillution ofprecision (GDOP) variessubstantially

For i >= 4 these navigation equations can usually be solved to provide an optimalleast square estimate of the users psoition and clock bias offset. If the covariancematrix describing the pseudorange measurement errors is Pρ, then the maximumlikelyhood estimate of the perturbation in the user position and clock is

xU = AUρ (3.7)

where

AU = [GT P−1ρ G]−1GT P−1

ρ . (3.8)

The covariance error in the estimate of xu has a covariance matrix given by

34


Px = [GT P−1ρ G]−1. (3.9)

3.1.3 GPS AccuracyThe basic properties and limitations of GPS accurracy are outlined here in respectto their severity for the positioning mechanism of a PicoNode. Some issues that areof more importance to space based systems are not outlined in great detail.

3.1.3.1 Selective Availability

The C/A code is deteriorated by a slowly varying clock bias called selective availabil-ity (SA) that enables the DoD to introduce a ranging error of approximately 30 m ondemand [14]. It has been activated most of the time to safeguard national interests.SA has a correlation time of approximately 180 s and is thus not a large problem forstationary GPS equipment.

If the GPS measurements are not corrected, then the individual pseudorange er-rors are dominated by SA. All of the pseudorange errors have approximately equalstandard deviation σi

ρ ≈ σρ, where σ2ρ is the common variance of the pseudorange

measurement errors. In this case, Pρ ≈ σ2ρI where I is the identity matrix, and the

maximum likelyhood estimate is given by

xU = [GT G]−1GT ρ (3.10)

and the errror in the estimate has the following covariance:

Px = [GT G]−1σ2ρ. (3.11)

3.1.3.2 Geometry

As shown in (3.11), the position error suffered by a GPS user is proportional to thepseudorange measurement error σ2

rho, but it also depends on the geometry of theuser and the satellites through the matrix GT G

−1 shown in figure 3-4 [14]. If thesatellites are clustered from the user’s point of view, the geometry is poor, and pooraccuracies result. An ideal geometry would be three low-elevation satellites 120◦

apart in azimuth, with a fourth satellite at zenith. The measures which describe thequality of the geometry are known as dilution of precision (DOP) values. These aredefined as follows.

When the position coordinates are the ordered right hand set (East, North and ver-tical), then

Px =

E2DOP

N2DOP

V 2DOP

T 2DOP

σ2

ρ. (3.12)

As shown, East DOP (EDOP ) is the estimate in the upper left of the [GT G]−1 matrixand so forth for North DOP, vertical DOP and time DOP. An estimate of any position

35


or time error can now be obtained by multiplying the appropriate DOP value by thevariance of the pseudorange measurement error [32, 8].

The most commonly used measures of accuracy for vertical, horizontal, and 3-Dpositioning are

2σV = 2VDOP σρ (3.13)

2√

σ2E + σ2

N = 2√

N2DOP + E2

DOP σρ = 2HDOP σρ (3.14)

2√

σ2E + σ2

N + σ2V = 2

√N2

DOP + E2DOP σρ + V 2

DOP = 2PDOP σρ. (3.15)

The specified accuracies are achieved approximately 95% of the time.

The geometric dilution of precision values defined above depend on user locationand on time, where the latter dependence arises because GPS satellites are mov-ing relative to the surface of the earth. The GPS constellation has been designed togive low dillution of precision when averaged over all locations worldwide and overthe day. The worldwide median daylong values of PDOP and HDOP are 2.0 and 1.2,respectiveley. As mentioned earlier, the standard deviation of the pseudorange mea-surement errors are approximately 30 m when the C/A code is used. At any time,if PDOP and HDOP are equal to their median values, then the 3-D and horizontalaccuracies are 120 m and 70 m, respectively.

It is clear however from the geometry matrix (see figure 3-4), that the vertical ac-curacy suffers much more from the geometry of the satellites, especially those withvery low elevation, when compared to the horizontal accuracy. The average time forsatellites in the low elevation positions is much higher than in the zenith.

3.1.3.3 Ionospheric and Tropospheric Delays

The inospheric and tropospheric refraction delays are significantly smaller than SAerrors. Ionospheric delays can result in varying errors of 40-60 m 95% by day to6-12 m 95% at night. Tropospheric delays are caused by moisture in the lower ath-mosphere. They may be up to ±6 m 95%. They can mostly be compensated by differ-ential GPS operation [8].

3.1.3.4 Multipath Signals

In a GPS receiver, multipath causes an error in the measurement of the pseudorangebecause it affects the delay-lock tracking loop. If the path length of the indirectsignal is more than a chip longer than the direct signal, the correlator will not beable to correlate the indirect signal anymore. For stationary or slow moving usersthe multipath error is on the order of a few meters for a period from a few minutesto an hour [8].

36


3.1.3.5 Receiver Phase Noise

Oscillator phase noise in the receiver is a basic limitation on narrowing the carriertracking loop bandwidth and, therefore, on the achievable carrier-track signal tonoise ratios (C/N0) for GPS receivers [27, 14, 52, 42]. However, when receiving andtracking several satellite signals the phase noise is common to all tracking loopsand, in principle can be removed by a common-mode rejection scheme. The phasenoise contributed by the satellites is negligible in comparison with the phase noisecontributed by the receiver’s oscillator.

The solution for a two-channel system is given here and an extension to n chan-nels is straightforward. The optimal linear estimator for a two-channel system isformulated using the composite state vector x(t) = [ϕ, θ1, θ1, θ2, θ2]T for second orderdynamics, where ϕ is the oscillator phase noise and θi is the phase of the ith signal.The linear model of a two-channel system, with the oscillator phase noise modeledas a white gaussian noise (WGN), is therefore

x(t) =

0 0 0

0 F 00 0 F

x(t) +

1 0 0

0 G 00 0 G

w0(t)

w1(t)w2(t)

(3.16)

with

E[w0(t)w0(τ)] =f2

L1h0

2δ(t − τ), (3.17)

w0(t) beeing the frequency WGN and

E[wi(t)wi(τ)] = Qδ(t − τ), (3.18)

wi(t) the WGN of the ith signal. F and G are defined to produce a n-th-order Wienernoise process. The parameter h0 is the intensity of the normalized phase-noise spec-trum as defined by Barnes [2].

The signal phases are observed by phase detection

z(t) =[

e1

e2

]≈ H[x(t) − x(t)] +

[v1(t)v2(t)

](3.19)

which is linearized as

H =[

1 1 . . . 0 . . .1 1 . . . 0 . . .

], x(t) = E[x(t)] (3.20)

where E[v(t)v(τ)] = Rδ(t − τ) and R = N0/2C for phase-locked carrier-phase de-tection and R = (1 + N0/2CTb)N0/2C for Costas suppressed-carrier phase detectionwith a coherent integration interval of Tb.

The optimal linear estimation of this filter using the Kalman-Bucy formulation hasa steady-state covariance solution given by

37


P = PF + PFT − PHT R−1HP + GQGT = 0 (3.21)

where

P = E[(x − x)(x − x)T ]. (3.22)

The resulting problem complexity is (nm)2 for n channels and m-order signal track-ing algorithms, where complexity is the number of multiplications in the filter. Asub-optimal solution of substantially lower complexity can be derived by the as-sumption that the estimator of oscillator phase noise and the signal phase dynamicsare seperable, such as given in [27].

The implementation of specialized hardware [46, 47] has shown that these problemsare not very straightforward to overcome.

3.2 Enhancing the Performance of GPSThere are numerous ways to enhance the navigation solution provided by a GPSreceiver. Some of them can be integrated into the GPS system itself, and some wouldhave to be implemented externally, using postprocessing.

3.2.1 Estimates for Underdetermined Navigation SolutionsIf less than four signal measurements are available at a given time, the system ofequations given in 2.17 and 3.5 is under-determined and the unknowns cannot besolved for. It is common to make some assumptions about the receiver dynamics,system components and observation environment for a limited timespan in order toovercome this obstacle.

It is vital though, that a full navigation solution with at least four independantmeasurements can be achieved within a given timespan. The reacquisition timeneeded for the receiver to lock back on the satellites signal after it was unavailablefor a certain time is an important indicator of the quality of service provided by theGPS receiver. All three estimates given below rely on a fast reacquisition time of theGPS receiver.

3 Satellite Measurements:

On earthbound navigation there is usually not a very rapid change in altitude overtime. Using a Modified Kalman Filter (MKF) to model this unknown, the LSA can besupplemented with data from the filter and a navigation solution can be determined.Certainly the solution is only valid if the altitude of the receiver is not changingsignificantly. Major altitude deviations during 3 satellite solution will accumulateerror and propagate into the horizontal position solution [55].

38

3.2. Enhancing the Performance of GPS


If only two satellite measurements are available then the signal assumption of mod-eled altitude for the vehicle is no longer sufficient. An additional assumption needsto be implemented to solve for the remaining 2 observation equations. If it is as-sumed that the clock behavior of a running GPS receiver can be modeled for shortterms using the MKF approach the LSA can be solved using only two available mea-surements. The error growth over time is modeled using the expected fluctuations inthe clock characteristics of the crystal. This model calls for a minimum acquisitiontime prior to utilizing this estimate in the navigation solution to give the receiverhardware time to stabilize and a very high quality quarz oszillator setup. In receiverarchitectures available today, such as the SiRFstar GPS [48], this clock hold mode(CLH) is bounded to about 60 seconds (typical).


With only one GPS satellite visible, giving rise to only one measurement, furtherassumptions need to be made to estimate the variables from the unknowns. Em-ploying a position history and assuming a constant direction of movement resultsin another MKF estimate. This is mainly usefull in an urban environments, wheretravel is confined to the streets. The assumption of constant direction when usingonly one satellite measurement is valid if the initial estimated direction is correct.If the receiver is stationary (or has a velocity of less than 30 cm per second) thenthe direction estimation will be unreliable due to the dominant effects of selectiveavailability (SA). This estimate is thus confined by a minimum speed and a shortterm use of 30 seconds (typical) (such as for the SiRFstar GPS curb hold mode (CBH)[55]).

3.2.2 Carrier Phase NoiseEvaluations of commercial low cost GPS receiver systems such as [45] have shownsignificant limitations on the real time extraction and processing of signal phasedata from the receiver. The sequential architecture of the channels and large clockdrifts are the main problems.

3.2.3 Differential GPSA stationary GPS receiver calculates a deviation from the actual position and broad-casts this to a remote GPS receiver. Many different services offering differentialGPS (DGPS) operation exist today. Real time services over communication links arecommonly used for surveying applications and vehicle navigation. Postprocessing ofreceived DGPS data can be applied as well, especially for geographic high precisionapplications. The accuracy derived depends strongly on the distance between thetwo receivers.

Additionally tasks such as data snooping, residual determination and other types ofstatistical testing might be applied for higher accuracy.

39


3.3 Future Drivers in GPSThe reception of indoor GPS is limited due to the very weak received signal strengthavailable. The E-911 requirements for mobile communication systems in the UnitedStates are a primary driver for increased accuracy and indoor applications. It seemsat the moment, that most systems will rely on triangulation between basestationsand only employ GPS as a secondary system or for specialized devices.

Emerging high mobility and location aware applications have strong impact on thedevelopment of GPS systems. With the availability of more infrastructure the dif-ferential services and thus accuracy will increase for the user. Emerging chipsetsolutions show higher complexity algorithms and integration of digital hardware asa system on a chip [49].

The very weak received signal strength is also the main problem when single chipsolutions for GPS are being evaluated. The analog hardware has to be preciselytuned and it is today not yet possible to integrate this with digital CMOS structureson one device [12, 46, 47].

The GPS system is constantly being improved. The development of new SV’s withmore downlink data capacity has been started. A general availability of the PPSservice has been anounced within a couple years.

40

4Positioning for Networked PicoNodes

A networked environment of PicoNodes has essentially different characteristics anddemands on positioning capabilities than the Global Positioning System, guidancesystems such as VOR or TACAN in avionics or cellular telephone systems [38, 43].

The possibilities and limitations of a distributed positioning algorithm for PicoNodesis explored in this chapter. An off the shelf wireless local area network based on theIEEE 802.11 standard was used to verify behavior and the basic concepts of thealgorithm. A future PicoRadio test bed [5] that is currently being developed willassist in further refining the methods outlined. This test bed will allow softwareroutines, hard- and software partitioning and different radio types to be tested in areal world environment.

4.1 Lucent WaveLAN IEEE 802.11The Lucent WaveLAN IEEE 802.11 [50, 36, 53] is the next generation of the popu-lar Lucent WaveLAN or former DEC RoamAbout DS. It is compatible to the IEEE802.11 standard [26] and thus features a full MAC protocol, including MAC levelacknowledgements, optional RTS/CTS, fragmentation, automatic rate selection androaming capabilities.

It is operating at the 2.4 GHz ISM band as a Direct Sequence Spread Spectrum(DSSS) signal with BPSK/QPSK modulation and multiple channels at a signallingrate of 1 Mbps or 2 Mbps respectively. An extension to 5.5 and 11 Mbps is currentlybeing standardized as IEEE 802.11b High Rate [25]. The DSSS signal uses 11 chipsfor encoding resulting in 22 Mhz effectively used bandwidth for each channel, eachspaced 5 MHz apart. The number of channels available vary depending on regula-tions (see table 4-2) of every county. The deployable channels have to be seperatedby at least the bandwidth of 22 MHz, limiting the usable channels considerably.

The roaming client station automatically switches to the frequency channel of theWavePOINT, which gives the best link quality. The advantage is that you can nowexploit the capacity of up to 3 independent channels, and segment your network,

41

Chapter 4: Positioning for Networked PicoNodes

Figure 4-1Lucent WaveLAN IEEE 802.11 PC Card and WavePOINT Access Point

1 Mbit/sec 2 Mbit/sec 5.5-11 Mbit/secBit Pattern BPSK QPSK DQPSK even odd symbol

0 01 π

00 0 0 π

01 π/2 π/2 3π/211 π π 010 3π/2 3π/2 π/2

Table 4-1: BPSK/QPSK encoding table for different datarates on IEEE 802.11

instead of sharing one frequency channel with multiple cells. This allows more usersto use the wireless network, without jeopardizing response times or throughput.The roaming capability can even keep data links such as UDP traffic alive whenswitching access points.

The WaveLAN IEEE switches from high datarate, i.e at 2 Mbps back to 1 Mbps whenthe quality of the link cannot sustain the 2 Mbps. With 1 Mbps larger distances canbe covered as well as more users can be supported on the aggregate bandwidthavailable (see table 4-1 and 4-3).

Two networking modes are supported: A connection of wireless clients to other net-works via basestations/gateways and an ad-hoc networking mode to deploy infras-tructureless networks between nodes only, on the fly.

In order to account for the so called hidden station problem, the basestation can beconfigured to act as a master in the network, allocating and distributing resources.The hidden station problem occurs when node A and B can hear the basestation andA can hear B but B cannot hear A.

The actual function of the Access Point is more like a bridge to external networks,than like a basestation in the sense of mobile communication.

42

4.1. Lucent WaveLAN IEEE 802.11

Regulatory DomainCh Frequency US (FCC) EU (ETSI) France JP (MKK)

ISM Band 2.4 to 2.4835 GHz 2.4465 to 2.4835 GHz 2.417 to 2.497 GHz

Max. Output Power 1000 mW 100 mW 10 mW/MHz

1 2.412 GHz X X - -2 2.417 GHz X X - -3 2.422 GHz X X - -4 2.427 GHz X X - -5 2.432 GHz X X - -6 2.437 GHz X X - -7 2.442 GHz X X - -8 2.447 GHz X X - -9 2.452 GHz X X - -

10 2.457 GHz X X X -11 2.462 GHz X X X -12 2.467 GHz - X X -13 2.472 GHz - X X -14 2.484 GHz - - - X

Table 4-2: IEEE 802.11 Frequencies and Output Power Levels According to Regulatory Do-mains

4.1.1 WaveLAN HardwareWaveLAN IEEE PC Card Transceiver

The WaveLAN IEEE PC Card Transceiver [29] is a fully integrated PC card andantenna. The nominal output power is given at Ptx = 15 dBm equivalent to Ptx ≈30 mW . There are several data rates available. An internal powermanagement func-tion can put the transceiver in sleep mode and then wake up periodically to checkfor network requests. This can be configured by the user.

The WaveLAN cards support received signal strength and noise level measure-ments.

Specification WaveLAN CardSpeed options [Mbit/s] 11 5.5 2 1Range in Open Office [m] 160 270 400 550Range in Semi Open Office [m] 50 70 90 115Range in Closed Office [m] 25 35 40 50Receiver Sensitivity [dBm] -82 -87 -91 -94Delay Spread (at FER of <1%) [ns] 65 225 400 500

Table 4-3: WaveLAN IEEE PC Transceiver Card Specifications

43


WavePOINT IEEE Access Point

The WavePOINT IEEE Access Point [31] is a transparent bridge from the IEEE802.3 ethernet to IEEE 802.11 wireless LAN protocol. It can be used with two in-dividually configured WaveLAN PC Cards. It uses the spanning tree algorithm andsupports selective protocol filtering, roaming and encryption.

WaveLAN IEEE Range Extender Antenna

The Range Extender [50] is a 5 dBi indoor omni directional antenna that providesextra coverage (see table below) over the existing coverage of standard WaveLANIEEE products. The Range Extender comes standard with a 1.5 meter cable, whichgives you the ability to mount the antenna at a more favorable position (i.e. outsidea wiring closet).

Specification ValueDimension H x W x D 230 x 25 x 8 mmRange Open Office 50 % increase over standard PC CardRange Semi-Open Office 15 % increase over standard PC CardAntenna pattern Omni-directional (pan cake)Antenna diversity YesGain total antenna 2.5 dBi(incl. cable with 2 dB loss)Frequency 2400 to 2500 MHz

Table 4-4: WaveLAN IEEE Range Extender Antenna Specifications

4.1.2 WaveLAN Software EnvironmentThe Lucent WaveLAN is supported on several different software platforms. Driversfor MacOS, Windows 95, Windows NT, Windows CE, and Linux are currently avail-able. The network management and configuration software supplied is available onWindows systems only.

The software system consists of the transceiver card driver, a site survey or networkclient monitoring tool, the WaveMANAGER/Client (see figure 4-2), an Access Pointconfiguration and monitoring tool, the WaveMANAGER/AP (see figure 4-3) as wellas transceiver card firmware update tools [30].

Furthermore, the driver source code is available under license agreement for OEMmanufacturers.

4.2 Propagation Measurements for NavigationIn order to determine physical range information for a network of nodes, signalpropagation measurements based on the received signal strength indicator (RSSI)were undertaken. Many studies of radio propagation are targeted towards commu-nication systems and/or much longer signal ranges than anticipated for PicoRadio

44

4.2. Propagation Measurements for Navigation

Figure 4-2The WaveMANAGER/Client tool allows monitoring and logging of WaveLAN card data.The left picture shows the monitoring of all Access Points in range, the right picture themonitoring of a point-to-point connection.

Figure 4-3The WaveMANAGER/AP softwaretool allows configuration andmonitoring functions for aWavePOINT Access Point.Extensive configuration of thewireless network parameters andprotocols as well as monitoringfunctions for the data andthroughput are available.

[37, 17]. The radio environment for PicoNodes transmitting at Ptx ≤ 1 mW and an-ticipating a range of 1-10 m is very similar to the human scale, i.e. single rooms, in-and outdoor transmition and it is expected that the weak signal will not penetratethrough obstacles like walls very well.

The study of existing wireless local area networks in a heterogeneous in- and out-door environment would allow to determine the navigation capabilities and con-straints of a network of PicoNodes more clearly. The fact that a wireless LAN servesmuch higher data rates at many multiples of the transmit power of a PicoNode istaken into account.

4.2.1 System SetupThe test system consisted of a Lucent IEEE 802.11 WaveLAN set up in the facilitiesof the Berkeley Wireless Research Center (see figures 4-7 and 4-8). In a first experi-ment, multiple basestations and clients were used to determine the signal coveragein this mixed in- and outdoor environment. A second experiment was set up with

45


clients only to measure a net of range vectors inbetween every one of these nodes,primarily for navigation purposes. Several experiments to characterize the channeland the influence of external noise sources are described subsequently.

The WaveLAN transceiver cards used are the 2 Mbit/sec versions. Each LucentWaveLAN Access Point (see figure 4-4) was set up with two WaveLAN transceivercards configured to two different frequency channels. The transceiver card in slotA was set to channel 2 (2.417 GHz) and the card in slot B was set to channel 10(2.457 GHz). The transceiver card in slot A was additionally equipped with an ex-ternal extender antenna. With this configuration, two independant signal samplescould be taken simultaneously. The only difference in the two systems was the ex-tender antenna and the orientation of the two antennas.

Figure 4-4An Access Point with twotransceiver cards and extenderantenna for one of the transceivers(channel 2) is set up on top of thewall of cubicle 91. The extenderantenna needs to be located as faraway form the second transceiveras possible to minimizeinterference.

The Access Points were integrated into the local TCP/IP network and configured totheir default values: High transmit rate, standard multicast rate and medium Ac-cess Point density. These parameters mostly affect the protocol handoff for roamingclients.

Standard laptop computers (see figure 4-5) were used as network clients with oneWaveLAN transceiver card each. The power management on the transceiver cardswas disabled to reduce error from the transceiver card switching between sleep andoperating modes. No medium reservation by the clients was configured.

Network Card WaveLAN-II PC Card Type-II Extended NIC 2.00PC Card Firmware WaveLAN/IEEE Station Functions Firmware 4.08Driver WaveLAN/IEEE NDIS Miniport Driver 4.00Utility WaveMANAGER/CLIENT-II 4.02Access Point WavePOINT-II 3.38Utility WaveMANAGER/AP 2.06

Table 4-5: WaveLAN Software Versions

4.2.2 Test EnvironmentThe test environment for the experiments were the facilities of the Berkeley WirelessResearch Center (see figure 4-7). The main area is a large semi-open office space

46


Figure 4-5Labtop computers with WaveLANPC Card transceivers were used asnetwork clients. TheWaveMANAGER/CLIENTsoftware running on every clientallows logging and visualizationof network status data.

Figure 4-6The laptop client used for takingsamples throughout the whole areaof the Berkeley Wireless ResearchCenter was placed on a cart or ona desk to maintain a uniformheight for every measurement.

with low walled cubicles. Some offices, labs, meeting- and classrooms are situatedon the perimeter areas. The overall area is about 1000 m2.

The cubicle walls are 135 cm high and only seperate the adjoining desks. The ceilingheight is 477 cm and the lighting system and certain installations are hung off anintermediate metal grid system at 304 cm from the floor. This intermediate gridsystem is only present in some areas leaving large areas of the ceiling uncovered tothe full height. Several steel pillars are distributed in regular distances.

A large forum area in the middle is not divided by cubicles. It is seperated from themain cubicle area by a glass wall. The two outdoor patio decks are seperated fromthe inside either by concrete or glass walls. All other surfaces are either plastercovered or steel enforced concrete wall surfaces.

There are many electronic appliances in operation at the Berkeley Wireless Research

47


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

0 2 4 8 12

Figure 4-7The test environment in theBerkeley Wireless ResearchCenter comprises the wholefirst floor of the building.There is an open mainsection that houses thework cubicles and meetingarea as well as offices andclassrooms along the sidesof the building. Twooutside patio areas areavailable too.Cubicle walls are only halfheight. There are manytransparent glass wallsand a high, open ceiling.The area was divided usinga 2 × 2 m grid in an arrayof 21 × 21.

Center. Every desk is staffed with a workstation and monitor, the laboratory areahouses many test and measurement systems as well as the server and networkingsystems. Several different electric lighting systems are present too.

The environment is not regarded as a specially hazardous area in respect to electro-magnetic interferences, but not as a carefully shielded area either. In average thereare about 80 workstations/servers with a display each and about 30 appliances inthe lab in operation at the same time. A very rough estimate on the amount of in-terferers per area results in:

area

computer + display + lab ± variation≈ 1000 m2

80 + 80 + 30 ± 30≈ 5.2 ± 0.9 m2/system

(4.1)

Naturally there would be clusters in some areas and almost empty areas in otherspots, like the classroom. Moreover the environment is changing due to roamingusers and changes in the configuration. For the analysis presented here, it is as-sumed as static, i.e. positions are fixed for the duration of all experiments.

4.2.3 Signal Coverage MapA signal coverage map shows the fundamental signal propagation and related ef-fects like multipath and shading for a specific environment. It is mainly useful for

48


network planning and the analysis of signal attenuation by physical obstruction.Moreover it serves as a database for the empirical verification of the signal propa-gation model.

The area assigned for evaluation (see figure 4-7) was partitioned using a 2 × 2 mgrid. This means that based on the estimate given in equation 4.1 there is a littleless than one electronic interferer per grid element of 4 m2 each when distributedevenly.

Three Access Points [31] were set up on the top of the cubicle walls, 135 cm highwhen measured off the ground. This height is sufficiently high to allow for goodsignal propagation and less deflection and scattering on edges such as the cubiclewalls top edge. This setup would result in an assessment of three independant signalcoverage maps referring to the three different locations of the Access Points.

Antenna for transceiver channel 10

Extender antenna for transceiver channel 2

Access Point 1: BWRC classroom

Access Point 2: BWRC cubicle 53

Access Point 3: BWRC cubicle 91

Figure 4-8Basestation setup in theBerkeley Wireless ResearchCenter: Three locationswere chosen to establishgood coverage of the wholearea and to be able assessdifferent types ofenvironments: 1 - a closedroom, 2 - an open officearea and 3 - a semiopenenvironment with somewalls and windows.

The three positions all have a special surrounding to them: Access Point one is sit-uated in the BWRC classroom, close to the corner of a closed room, bordered by aglass wall to the patio (to the left as seen in figure 4-8) and a concrete wall to thestaircase (to the top as seen in figure 4-8). Access Point two is situated on top of thewall of cubicle 53 in a wide open space of the main area. Access Point 3 is situatedon top of the wall of cubicle 91, right on a concrete wall (to the bottom as seen infigure 4-4 and 4-8) that seperates the main area from the lab. There is a small lineof glass windows on the top of this wall, so it is more transparent to radio signalsthan a full wall.

All samples were taken in sets of 6 (3 Access Point locations and 2 transceivers perAccess Point) at every location from a mobile client laptop computer that was eitherput on a cart (height 80 cm) or on the desktop (as can be seen in figures 4-6 and 4-5).

49


The data was extracted from the logfiles and analized using matlab.

4.2.3.1 Signal Coverage Map Data

Three sets of plots shown in this section were measured to Access Points withone transceiver card configured to channel 10 (without the extender antenna onthe Access Point) and three sets of plots were measured to Access Points with thetransceiver cards configured to channel 2 (with the extender antenna on the AccessPoint).

Each set of three plots shows signal level, noise level and signal to noise ratio (SNR)over the whole area. All the data presented here was 2D interpolated to smoothe thedata.

−80

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x

Signal Level to BWRC Classroom − Chan 10

y

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dbm

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Figure 4-9Signal Level, Noise Leveland SNR Map to AccessPoint 1, Channel 10:The location in theBerkeley Wireless ResearchCenter Classroom showsstrong signal loss on thesurrounding walls. Thestrong decrease in signallevel on the left and bottomedge of the plots is due tothe fact, that these sampleswere taken on the sidewalkone floor lower and outsidethe building.

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Figure 4-10Signal Level, Noise Leveland SNR Map to AccessPoint 1, Channel 2:In comparison to figure 4-9the peak signal levelreceived from the externalextender antenna on theAccess Point is about 10dBm higher in this setup(see table 4-6). Also, thenoise level seems to bemuch more sensitive. Thesignal to noise ratio is notvisibly affected by theseeffects.

50


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ydb

m

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Figure 4-11Signal Level, Noise Leveland SNR Map to AccessPoint 2, Channel 10:This location in the opencubicle section shows anearly circular signalpropagation that protrudesthrough the glass wallsthat divide the cubicle fromthe forum section. Thehighest noise level peaksexist in areas of lowreception quality, behindwalls, corners of thebuilding or in seperatedrooms.

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Figure 4-12Signal Level, Noise Leveland SNR Map to AccessPoint 2, Channel 2:Similar to the previouspair of measurements infigures 4-9 and 4-10 thenoise level shows a highersensitivity when theextender antenna is used.The average noise levelvalues differ by only-1.2 dBm compared tofigures 4-11.

51


−80

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Figure 4-13Signal Level, Noise Leveland SNR Map to AccessPoint 3, Channel 10:This series ofmeasurements shows theinterference of structureslike cubicle walls andtables. Mainly a strongerattenuation is visible insome spots here. Theattenuation of the wall tothe laboratory area(concrete bottom and glasstop) on the lower rightcorner shows a meanattenuation of the signal of-10 dBm, characteristic forglass windows.

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Figure 4-14Signal Level, Noise Leveland SNR Map to AccessPoint 3, Channel 2:Very similar to the previoussets of measurements theexternal extender antennaincreases sensitivity of thesignal and noise level. Herethe attenuation effects ofthe signal described infigures 4-13 can be seenmore in detail. The signalto noise ratio is thus moreinfluenced by the extenderantenna.

52


Signal Source Signal [dbm] Noise [dbm]

Max Min Max Min Mean Std DevAccess Point 1, Channel 10 -42 -89 -69 -96 -90.5 2.28Access Point 1, Channel 2 -32 -85 -82 -96 -89.2 2.54Access Point 2, Channel 10 -38 -85 -69 -96 -90.3 2.30Access Point 2, Channel 2 -36 -83 -82 -96 -89.1 2.65Access Point 3, Channel 10 -31 -86 -69 -96 -90.5 1.92Access Point 3, Channel 2 -38 -85 -82 -96 -89.1 2.56Average Channel 10 -37.0 -86.7 -69.0 -96 -90.4 2.17Average Channel 2 -35.3 -84.3 -82.0 -96 -89.1 2.58Average Channel 2 and 10 -36.2 -85.5 -75.6 -96 -89.8 2.38

Table 4-6: Signal and Noise Map Characterisic Parameters

The characteristic parameters of the signal and noise data in table 4-6 show verysimilar values for all 6 sets of measurements. An enhanced sensitivity to noisecan be ascertained for the use of an extender antenna. Because the antenna of thetransceiver on channel 10 and the extender antenna on channel 2 were not in thesame position, the maximal signal level varies for the different locations. The aver-age however is almost the same for these two systems.

More detailed signal level maps are available in the appendix B. The plots were gen-erated from the same data as the plots in this section. They show signal strengthpropagation and characteristics in more details. Attenuation, shading and the effectof different materials in buildings can be identified. The commonly specified atten-uation of -10 dB for windows can be clearly seen on the right hand side towards thelab at coordinates x = 35. Most significant are the shading effects of some objects,such as the column at coordinates (x = 15, y = 23) and the reflections seen on fig-ures B-1 and B-2 on the wall on the upper left hand corner (x = 1, y = 30 . . . 40) thatreflect the signal into the staircase at (x = 10, y = 35).

4.2.4 Signal Propagation ModelIn order to convert received signal strength indicator (RSSI) values to a range esti-mate to be used with triangulation methods as explained earlier it is necessary touse an appropriate signal propagation model. The experiments outlined here onlygive rough guidelines for the signal propagation behavior of future PicoNodes sincethe physical radio frontend and thus also the methods of deriving a range estimatehas not been defined yet.

Individual measurements of the path loss between two transceivers are shown infigures 4-15 and 4-16. These samples were taken without the range extender an-tenna in ad-hoc network mode. The noise level is in the range of -85 to -95 dBm,similar to the data presented in table 4-6.

A modelling according to equation 2.22 with a path loss of 1/r2 results in a relation-ship of received signal strength and distance of

53

Chapter 4: Positioning for Networked PicoNodesSignalandNoiseLevel[dBm]

-120

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23 24 25

Figure 4-15WaveLAN Long RangePath Loss: Two WaveLANPC Card Transceiversshow a characteristic pathloss behavior of 1/r2 on arange from 0-30 m. Thiscorresponds to a direct lineof sight (LOS) pathaccording to the modelpresented in section 2.4.3and derived from [17].

Pr[dBm] = 15 + 10 · log(

c

4πrf

)2

(4.2)

with f = 2.422 GHz corresponding to channel 3 of the WaveLAN in ad-hoc networkmode.

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Figure 4-16WaveLAN Short RangePath Loss: The samplesshown here were takenfrom full contact of tworeceivers up to a wall at adistance of 8 m. Thecharacteristic is the sameas in the previous figure,but the reflections from thewall can be seen from250 cm on. The muchhigher signal level at thebeginning shows thedifficult estimation of thefirst meters of path loss.

In order to establish an empirical signal propagation model for the semi open envi-ronment charted in the previous section, crossections of the signal coverage mapswere taken and fitted with regression curves (see figures 4-17 and B-7).

The evaluation of all 6 signal coverage maps shows that an 1/r1.5 signal path lossbehavior models the situation in the environment of the Berkeley Wireless ResearchCenter best. This behavior that is usually not found indoors is attributed to thelargely open space and the strong transmit power of the WaveLAN at Ptx ≈ 30 mW .

54


0 5 10 15 20 25 30 35 4080

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samples

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Figure 4-17Signal Propagation OverDistance: The signalsamples shown here wastaken from the signalpropagation map 4.2.3.1and is a horizontal cutthrough the maximum. The1/r1.5 curves turns out to betoo low on average. A 1/r1.5

path loss shown here andin figure B-7 fits thesamples from theWaveLAN better, especiallyon the distances concerned.

4.2.5 Signal Propagation in the Presence of NoiseA bidirectional radio channel essentially has an asymmetric characteristic due tothe fact, that external influences such as noise or obstructions will not be alignedsymmetrically. The influence of strong noise sources is explored in figures 4-18, 4-19and 4-20.

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Distance [cm] (Local moving away from Remote and Noise Source)

LSLRSLLNLRNLLSNRRSNRLinear (LSL )Linear (RSL )Linear (LNL )Linear (RNL )


Figure 4-18Signal Propagationwithout Noise: Thepresence of nearby wallscan be seen by thesaturation of the receivedsignal level of bothtransceivers. The almostlinear decreasing signallevel is quite different fromthe one shown in figure4-15. Especially in therange of r = 0 . . . 2 m theRSSI of local (LSL) andremote (RSL) is quite thesame.

A strong FM signal was generated at the channel frequency used by the WaveLANand two transceivers were positioned about 0.4 m apart and 1 m to the side of thenoise source. One of the transceivers (local at (x, y) = (−0.6, 0)) was then movedaway from the stationary transceiver (remote at (−1, 0)) always keeping the noisesource (noise at (0,−1)) 1 m to the side. Both transceivers were logged simultane-ously with the WaveMANAGER/Client software to show the asymmetric noise leveland thus the signal to noise ratio.

55


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Figure 4-19Signal Propagation with0 dBm Noise: The meannoise level for thestationary node (RNL)remains the same as themean noise level for themobile node (LNL) is seento decrease over distance.The first increase in thenoise levels results from themobile first moving to thenoise source and then awayfrom the noise source.

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Figure 4-20Signal Propagation with+15 dBm Noise: With aneven stronger noise sourcethe saturation of bothreceivers in the first half ofthe samples can be seen.The noise level increasesfor both signals, as thedistance between thetransmiters increases. Thiseffect is best explained bythe spread spectrumtechnology used for theWaveLAN.

56

4.3. Networked Ranging Vectors

4.3 Networked Ranging VectorsIdeally the PicoNodes would be able to establish a range measurement to all partnernodes and all partner nodes would be able to do the same, resulting in a completegraph of all nodes and ranges. In reality, due to the limited radio range and themultihop nature of the network of PicoNodes and the obstruction in the environ-ment, only certain range estimates will be available for calculating the unknown orrecalculating changing positions.

Node xpos1 ypos1 xpos2 ypos2

A 17 19 24 18B 22 23 38 12C 41 16 41 16D 38 15 38 15E 41 12 41 12F 40 11 40 11G 38 9 38 9H 14 11 30 13I 7 9 32 14J 8 20 26 7

Table 4-7: Position of Nodes for Networked Ranging Vector Experiment

The second experiment was set up to generate range vector data between 10 inde-pendent network nodes. This data would then be used to do a position calculation forthese nodes. Since networks of PicoNodes consist of many nodes and it is assumedthat many of them overlap their radio range in a certain region this high number ofnodes was used for this set of experiments.

A network node was set up using a laptop computer and a WaveLAN PC CardTransceiver for each node. The network cards were configured in ad-hoc networkmode (channel 3, f = 2.422 GHz) to enable all to communicate on the same medium.The WaveMANAGER/Client software then allows to measure links to and from allnetworking nodes within range.

4.3.1 Test EnvironmentTwo sets of position data were taken from locations given in table 4-7. The locationsare shown in figures 4-21 and 4-22. The first set shows a group of spread out nodes(A, B, H, I, J) and a cluster of nodes (C, D, E, F, G) on the right that is shaded by thewalls of the laboratory. The coordinate system is the same as was used earlier forthe signal level maps.

4.3.2 Data Model for TriangulationThe analysis model is kept relatively simple, especially for the generation of the sin-gle pseudorange values. Since the physical communication of PicoNodes and thusthe radioranging mechanism is not yet defined this simplification is required. The

57


0 2 4 8 12

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Figure 4-21Networked RangingVectors Set 1: Some nodesare spread out over an areaand some nodes areclustered. The maximumdistance is at the limit ofthe signal reception of theWaveLAN. The meandistance is about 50%between the minimum andmaximum distancebetween two nodes. Thesingle distances vary quitea lot which will requirechoosing only similarlength range estimates fora single solution.

0 2 4 8 12

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Figure 4-22Networked RangingVectors Set 2: The nodesthat were spread out in setone are now closer to thecluster in the laboratoryarea. The mean distancebetween nodes is only a fewmeters and the individualdistances are much moresimilar than on the first setof positions.

58


RSSI estimates of the WaveLAN transceivers are a very inaccurate mean of deriv-ing pseudorange distance estimates. Since one of the main goals for the PicoRadionavigation is robustness of the system based on the amount of nodes interacting inone system this is less of a problem for this first analysis.

Of course it would be desirable to incorporate values and algorithms that evaluatelink stability, behavior over time, status and an exact model of the transceiver andthus the channel in a filter mechanism but this would go too far on this first analysisand for the data available.

4.3.2.1 Data Analysis

To solve for an unknown position the linerarized MMSE system described in section2.3.2 was applied using a simple path loss model according to equation 4.2 andn = 1.5. For the following evaluation only a unidiretional RSSI estimate was takeninto acount. The internal thresholds for SNR, maximum and minimum noise andsignal level of the WaveLAN were used here.

This model could be easily expanded to a differential estimate or incorporate thenoise level observed on a channel to weight each range vector. The computationalcomplexity would be increased if this extra information had to be processed.

Subsequent plots shown in figures 4-23, 4-24 and B-8 to B-19 were generated usingall possible permutations of subsets of the measured nodes. I.e. at first all possiblecombinations of 3 nodes of the remaining 9 nodes were used to solve for the tenthnode. To solve for the unknown position of node D the following permutations

(ABC)(BCE)(CEF )

...(HIJ)

of size 3 were computed and resulted in a distinct solution each. Then the same wasdone for permutations of size 4,5,. . . ,9 each. An average position estimate over allsolutions derived from permutations is then computed.

4.3.2.2 Complexity of the Solution

A linearized MMSE solution using n known positions as input uses a matrix X∗

of the size sX = 2 × (n − 1) and a pseudorange vector r∗ consisting of 6 additivesquared values of size sr = 1 × (n − 1). The QR factorization algorithm can be beeasily parallelized, solved recursively and use it’s own input matrix as output matrixas has been implemented by [9] and others, mainly in the area of parallel computing.

When permutations are used as described above, a QR factorization has to be ap-plied for each permutation and then averaged. For n nodes

59


(n

p

)=

n!(n − p)!p!

(4.3)

distinct subsets of the size p exist. A system with an n × n matrix requires O(N3)operations. An updating process shown in [39] shows that it can be improved toO(N2). An average would then exist of

(np

)components. In reality not all of these

permutations will be useful for a geolocation algorithm and averaging will take placeincrementally, reducing the required storage space for variables.

A tradeoff between computational complexity, the size of the datasets evaluated,the amount of iterations and thus the accuracy and real time requirements has tobe considered.

4.3.3 Triangulation EstimatesFigures 4-23 and 4-24 show the difference for the positions given in set 1 and set 2respectively when using seven nodes for a solution at a time. The solutions for thepermutations using 3 to 9 nodes are given in the appendix (see figures B-8 to B-19).

−10 0 10 20 30 40 50−10

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]

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Triangulation using Permutations of 7 Nodes Figure 4-23Triangulation EstimationSet 1 on 7 Nodes: Thesingle solutions for node D(green) from 7 unknownpositions (blue) are shownas red crosses. The meanvalue is shown as a pinkstar with it’s absolutedeviation (dx, dy). Thelinear geometry of thenodes and the pseudorangeerrors moves the meansolution to the bottom tocluster in one area.

A large error can be observed for the first set of positions but an almost 10x smallererror is observed for the second set of positions where the nodes are situated closertogether and more uniformly spread out. The clustering of solutions observed showsthe convergence according to the geometry and the influence of the pseudorangeerror described in section 2.2.

The most important observation is that the single length of the true ranges used forthe solution of a nodes position and the mean length of all these single ranges arerequired to be similar, i.e. not magnitudes apart. Moreover the longest range vectorsmust not result in more accurate triangulation solutions, because their pseudorangeerror is usually greater due to the greater impairment. The contribution of the er-ror of a pseudorange estimate is of course much more significant, if this range ismagnitudes larger than all the others incorporated in the solution. This was ear-lier introduced by the term geometric dillution of precision (GDOP), similar to the

60


15 20 25 30 35 40 45 50−5

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]

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Triangulation using Permutations of 7 Nodes Figure 4-24Triangulation EstimationSet 2 on 7 Nodes: With allranges much closer to themean of the range valuesused in the solution fornode I, the results showmuch less error than on theestimation on set 1. Againa clustering of singlesolutions can be observedin certain regions. Thisresults from certaingeometric and pseudorangeerror similarities. It hasshown in the sequentialanalysis of everypermutation, that the meandistance to the unknownposition has great impacton the accuracy derived.

3 4 5 6 7 8 90

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dx set 1dy set 1dr set 1dx set 2dy set 2dr set 2

Figure 4-25Errors in Triangulationusing Permutations: Theerrors shown here wereextracted from thetriangulations using thenodes in set 1 and set 2.According to figure 2-15 adecreased error can beobserved for more than 5nodes in the triangulation.The increase seen on theright here results from theinnacurate pseudorangeestimates and unevengeometry.

GDOP for GPS. A selection of the ranges used for a positioning solution is thereforenecessary to obtain better performance.

Several factors contributing to the error of the solution can be identified:

• Geometry

• Number of nodes/pseudoranges

61


• Error on pseudorange

• Error in initial positions

• Single length versus mean length of pseudoranges

Figure 4-25 shows the absolute errors of dx, dy and dr =√

dx2 + dy2 of all permu-tations computed of the positions in set 1 and 2. It shows the similar increase inaccuracy as has been shown in figure 2-15 when more than the required 3 nodes areused for a solution. The slight decrease in accuracy observed on solutions of 5 andmore nodes can be explained by the rudimentary RSSI estimation in the WaveLANand the lack of a dedicated filter algorithm in the computation of the single pseudor-ange values. It is expected that a selection of certain permutations that have similarlength pseudorange values only and a more selective RSSI measurement or othermeans of estimating a pseuorange will not show this behavior.

Since there is no means to synchronize the collection of RSSI data on different re-mote WaveLAN systems it was not possible to set up an experiment evaluating thegradual iteration over time of such a system. It would be desirable to be able to col-lect all datasets at the same time and to log this over a longer period, also includingmotion of selected nodes. This has to be delayed until a test bed for the PicoRadiosystem [5] can be deployed.

62

5Navigation Scheme for PicoRadio

The experiments and methods described in the previous chapters suggest a novelapproach to a geolocation mechanism for the PicoRadio environment. This approach,titled cooperative ranging is described in the following and compared with othersolutions like the GPS system subsequently.

The key idea behind cooperative ranging is to utilize the positioning capabilities ofnetwork nodes as a driver for the operations of the network itself. A multihop, self-configuring network as it is envisioned for PicoRadio is a highly dynamic system buthas limited resources in system functionality, power consumption, computationalperformance, efficiency and accuracy of the radio frontend. In order to optimize op-erations in runtime, many tradeoff parameters have to be considered to achieve therequired robustness.

5.1 Cooperative RangingThe PicoRadio navigation algorithm can be partitioned into three main tasks: Rang-ing, Updating and Positioning. These tasks depend on each other to some extent,but they can be performed sequentially and out of order once a general databasehas been established. Every node in the network is required to keep a databaseof it’s neighbors positions and the range to these neighbors. The size of this data-base depends on the requirements of the positioning service requested as well as onthe state of the network, i.e. amount and geometry of neighboring nodes. Addition-ally in the scope of the multihop network, connections may not be available on anearest neighbor basis all the time. A tradeoff between the number of hops and thetransmitted data can force connections to span over intermediate nodes. The limitedresources in PicoNodes pose even more limitations when the energy consumption ofa link is considered.

5.1.1 Cooperative Ranging AlgorithmRanging This means to establish an estimate of a pseudorange to secondary nodes.The process is very similar to the tracking loop of GPS. Different measures of

63

Chapter 5: Navigation Scheme for PicoRadio

RangingUpdatingPositioning

EveryNode

Figure 5-1Cooperative Ranging NodeFunctions: Every nodes runs threetasks: Ranging to it’s neighbors,updating positioning and remoterange data with it’s neighbors andcalculating it’s own position.

• Received signal and noise strength

• Signal to noise ratio

• Bit and packet error rates

• TOA estimates through synchronization

• TDOA estimates through correlation

can be used. They may be combined to form differential estimates. The importancehere lies on an exact adaptable model of the environment, i.e. the channel. The GPSsystem uses periodic updates of correctional data to overcome this problem.

Updating Nodes have to share their range data with neighboring nodes over thenetwork link. The depth of the propagation on the multihop media depends on thetype of service requested, the geometry and the mean distance to neighboring nodes.The links available in the multihop environment may not be between the samenodes as the nodes of the pseudorange estimates. Under certain circumstances itcan be necessary to span a network link over certain nodes.

Differential distance measurements are only possible when communication takesplace. So it is close at hand to employ the information exchange used for differentialestimates to spread the positioning data to the network to reduce overhead traffic.For a PicoNode communication will be likely to be less efficient on the resourcesthan computation.

A local master would then control the flow of information in a certain area. Thecapability to be a master should not be fixed in the network but generated andnegotiated by nodes on the fly.

Positioning The computational analysis of the pseudorange estimates to solve fora nodes position is only a part of this step. Extensive heuristics and the evaluationof factors that allow to apply a confidence level to every estimate is equally impor-tant. To assess for link quality and environmental parameters certain factors canbe included:

• Stability of signal level

64

5.1. Cooperative Ranging

• Stability of noise level

• Last absolute range value computed

• Dynamics of node and clusters (velocity)

• Access point to backbone network

• Known positions

• Amount of first, second, third . . . level neighbor nodes

• Mean distance to neighboring nodes

• Time of last position update

• Out of area positions

• Known obstacles

• Known noise sources

• . . .

Certainly this list is not exhaustive and can be extended. Once the physical layer ofPicoRadio is defined more clearly it will be necessary to select the appropriate itemsand integrate them into a set of parameters to be monitored.

It is proposed to implement the positioning computation as a scalable linearizedMMSE estimator. A weighting of input data to the positioning algorithm would befeasible to be implemented as a Kalman Filter [8] which is common practice in GPSsystems. Such a filter relates an error state vector of the system to the actual obser-vations. It is thus a linear, recursive estimator that produces a minimum varianceestimate in a least square sense under the assumption of white gaussian noise pro-cesses. Two basic processes are modeled: a system dynamics model describing theerror state vector in time and a measurement model that defines the relationship ofthe error state vector and the measurements.

Figure 5-2Kalman Filter forPositioning: A KalmanFilter algorithm is oftenused in GPS solutions as arobust and recursivealgorithm. Intuitively, theKalman Filter sorts outinformation and weightsthe relative contributions ofthe measurements and ofthe dynamic behavior ofthe state vector. [8]

Fixpoints to serve as references for absolute positioning can be either integratedinto the PicoRadio environment by PicoNodes that are mounted at a known locationor by attaching sensors like GPS, accelerometers, etc.

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5.1.2 Modes of OperationEssentially two different modes of operation will be necessary: Initialization of thesystem and continuous operation.

Initialization As it is envisioned for PicoNodes to resemble their own infrastruc-ture and these types of networks will not be turned off often the startup phase willonly happen seldom. Nevertheless the latency for startup has to be considered as anoverall system parameter.

Continuous Operation The inclusion and exclusion of nodes is part of the con-tinuous operation. Large changes in the network structure and the amount of nodescan be happening at real time, especially, when in the case of the Exploratoriumexample a group of users equipped with PicoNodes enters or exits an area.

5.1.3 Levels of ServiceIn addition to the modes of operation different levels of services will be most ap-plicable for the positioning scheme described here. Different levels of accuracy canbe achieved under different circumstances and at a different requirements. Mainlythese requirements are power consumption and complexity of the hardware andalgorithms involved.

A broadcasted signal used by all receiving nodes to determine pseudorange esti-mates can establish a basic service. A low complexity algorithm would be best suitedfor continuously operation. Upon request differential distance measurements ondedicated point to point links and a more complex iterated computation processcould deliver more accurate resolution for certain nodes.

A controllable transmit power of the RF section of the PicoNode would greatly ben-efit the ranging accuracy and reduce interference to other nodes and the link pow-erconsumption considerably. An investigation of the multiple access interference [6]has shown that the near far problem can be resolved by aggressive Tx power controlor a soft handoff between multiple partner nodes.

5.1.4 Example of Local Clusters of OperationThe following three figures show how triangulation solutions propagate in a mul-tihop network. Pseudorange estimates are generated by broadcasting to all neigh-boring nodes, shown as circles here. They are continuously communicated to theneighboring nodes.

The ability to compute positions is passed on as a token. Tokens are generated whena certain time has elapsed since a node has possessed a token. Every node can onlypossess one token at a time. As time advances a database of pseudorange estimatesis established in every node. This is color coded in the figures. First yellow, thenorange, then red, then purple and so on. When a node cannot acquire enough mea-surements to compute it’s position, the token is passed on, shown by the red arrowin figure 5-3.

66

5.1. Cooperative Ranging

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Figure 5-3PicoNode RangingMechanism: When aPiconode gathers ranginginformation about itsneighbors it will share thisinformation to it’sneighbors. Here Node 5, 11and 18 start to measure 3ranges. When a node (hereseen at node 5 in step 3 red)runs out of rangingpartners they pass theranging token to aneighbor (node 2).

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Figure 5-4PicoNode RangingMechanism Motion: Theneighboring node (here theranging capability waspassed from nodes 5 and18 on to nodes 2 and 17respectively) continues togather ranging informationof it’s neighbors. Theislands of ranginginformation created sharetheir data. The range fromnode 2 to node 4 enablesthree nodes (nodes 2, 4 and5) to compute relativelocation information toeach other.

67


Gradually more and more relationships will be built up to form localized clustersof positioning information. Every node has a personal view of it’s surrounding en-vironment and keeps a personal database. Updates of this personal database occureither through sharing of information on the network link or by generating a newtoken after a certain time limit has been reached.

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Figure 5-5PicoNode RangingMechanism Clusters: Whenthis mechanism continues,clusters of information willoverlap and sharepositioning information.This then enables theclusters to define theirposition relative to otherclusters or fixed PicoNodesgiving a reference position.The propagation ofpositioning information iscontrolled. For example theposition of node 20 will notbe stored in node 3, 4 or 5,but maybe necessary to beavailable in node 11 or 13.

5.2 Geolocation and Multihop Routing IntegrationCooperative ranging explicitly exploits the amount of nodes present in an area. Themultihop networking architecture essentially is quite different from other position-ing systems available today.

A GPS system is based on the availability of a few highly synchronized referencesto be resolved by a single remote unit for a global coverage. Cellular communica-tion networks would operate on much smaller distances to their references, but stillrely on the scheme of one remote node and few reference points. Here especiallywhen basestations are aligned along a road, triangulation is very inaccurate. Flightguidance systems usually only operate in a given area or a beam.

For a PicoRadio environment essentially every node is a remote navigation receiverand every node can be a reference position in the system. Even if the actual positionis not known at a certain time, a node can participate in the ranging framework toresolve proximity and to supply this information to the routing algorithms. Whenmore samples of pseudoranges can be stored, perhaps with the help of mobile nodespassing by, a node will eventually be able to determine the absolute position.

68

5.3. Constraints of a PicoRadio System

5.2.1 Multihop Networking for PicoNodesA multihop network requires many different tradeoffs to optimize operations. Thegeneral problem lies in the fact, that to reach an arbitrary node a broadcast hasto take place to find a route to that node when no central database is available. Inaddition to finding a route, the amount of hops can be adapted to suit the data beingtransferred and the resources available.

A detailed evaluation of bit error rate, transmit power and packet size of an IEEE802.11 network protocol [13] has shown that considerable amounts of energy can besaved when the Medium Access Control (MAC) and physical layer are adapted tothe specific channel characteristics, amounts and types of data transmitted.

5.2.2 Location Based RoutingThe navigation drivers for PicoRadio proposed in section 1.2.4 will allow to estab-lish a fully scalable service based on the application requesting a navigation service.This application may be the user, an application running on a PicoNode or the net-work itself. The positioning data resembles a kind of sensor data collected by thePicoNode. It is available distributed throughout the network.

PHY/

Range Estimates

MAC/

Routing

RangingUpdatingPositioning

EveryNode

Input Output

Transport Range DataFigure 5-6Cooperative Ranging in theNetwork Context: Therange estimates generatedby the physical layer feedthe positioning mechanismof every single node.Positioning data is beingused for the routing of themultihop network andtransports range data to beused with other nodesposition calculations.

Positioning information will help to establish a notion of localization and thus aidespecially on the long haul routes. This will allow to keep routing tables small andto establish routes based on the target position. A packet will be sent off in thedirection of the destination and every node will transfer this packet further into thedirection of the destination.

The different types of positioning services will allow a continuous operation of thenetwork, mainly based on proximity information with rather large errors. Upon re-quest the network can allocate more resources in a certain area to acquire a highprecision position that will be required by the user or other applications.

5.3 Constraints of a PicoRadio SystemA general observation about any navigation service is, that it is only useful in thecontext of an application. Most of these applications today require a detailed mapor communication services and thus extensive data storage and update capabilities.

69


Positioning and communication systems are not only a question of system hardwareand signal propagation and processing but of intelligent system partitioning withsuitable applications and services.

Accurate range estimates have to be obtained at the cost of complex system fea-tures, computational complexity and power consumption. A designated analog hard-ware as well as algorithms that manipulate large datasets require dedicated systemresources. Often it will be beneficial in terms of power consumption to allow formore computational complexity than for communication. This is directly the oppo-site from the development of communication/computation systems state of the arttoday where system resources and especially power are of no concern (yet).

For extremely low energy consumption it is desirable that the analog receiver andtransmitter hardware be shut down most of the time. That means, that during thistime no range data can be collected when a combined multiuse RF frontend is beingused. A similar feature is available on GPS solutions [33] known as Trickle PowerMode. Here the receiver only wakes up periodically to generate a position updateand then returns to sleep mode to conserve energy.

The bandwidth available in the ISM bands at 900 MHz, 2.4 GHz, 5.8 GHz, 24 GHz. . . isavailable for use today, but is getting more and more utilized for different types ofapplications. The question is near at hand, how a coexistence of different systems inthe same part of the spectrum can be managed.

5.4 OutlookIn a first step a detailed scenario for an application of PicoRadio has been devel-oped. This scenario of the Exploratorium Science Museum has served as a driverfor the development of specifications and algorithms for a network of PicoNodes.The top down view of the applications and the environment has been very helpfulin evaluating the possibilities to integrate a geolocation service into the PicoRadioframework.

The results derived through simulation and measurement show, that it is feasible toimplement this service even on the limited capabilities of a PicoNode. A consequentexploitation of the amount of nodes available and the properties of a multihop net-work environment help to overcome the difficulties posed by the wireless medium.

A detailed tradeoff between the application, the data transfer, the origin and targetaddress, the resources available on the single nodes as well as in a defined area thathas a certain population density of nodes and the power supply for every node will bethe main obstacle to a successful implementation. To minimize the overhead trafficin the multihop network position information proves to be a valuable resource.

Various options on the side of the RF frontend for a PicoNode will have to be eval-uated in conjunction with networking and positioning requirements. However, thedemand for a highly configurable and controllable frontend is obvious.

The positioning capability can be implemented as a scalable service. This means,that at times, resources can be freed for other functions on every PicoNode. Themain factors to evaluate are:

70

5.4. Outlook

• Distance measuring capability of PicoNodes

• Accuracy and latency of results

• Hardware resources allocated

• Software complexity (cycles and memory)

• Power consumption per pseudorange estimate/position

The PicoRadio test bed will allow to refine and specify the algorithms proposed.Furthermore it will allow to test the behavior with several nodes in a multihopenvironment, which has so far not been possible.

71


72

APositioning Error Estimates

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0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y

dx 0.015dy −0.04

Figure A-1Position Error Estimates, 3Nodes, 50% Range Error, 5Iterations

73

Appendix A: Positioning Error Estimates

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y

dx −0.0037dy 0.011

Figure A-2Position Error Estimates, 3Nodes, 50% Range Error,5000 Iterations

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y dx −3e−05dy 0.00029


74

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


xy

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y dx −0.018dy −7e−06

Figure A-4Position Error Estimates, 5Nodes, 50% Range Error, 5Iterations

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y dx 0.015dy −0.0069


75


0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y dx 0.00044dy −0.00028


0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y

dx 0.032dy −0.04

Figure A-7Position Error Estimates,12 Nodes, 50% RangeError, 5 Iterations

76

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


xy

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y

dx −0.0055dy 0.0091


0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y dx 0.00054dy −0.00017

Figure A-9Position Error Estimates,12 Nodes, 5% Range Error,500 Iterations

77


0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y

dx −0.016dy 0.012


0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


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y

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0.2

0.4

0.6

0.8


x

y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y

dx 0.012dy 0.004


78

0 0.5 10

0.2

0.4

0.6

0.8


x

y

0 0.5 10

0.2

0.4

0.6

0.8


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y

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0.4

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0.8


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y

0.4 0.45 0.5 0.55 0.60.4

0.45

0.5

0.55

0.6Zoom on Error

x

y dx 0.00022dy 9.5e−05

Figure A-12Position Error Estimates,35 Nodes, 5% Range Error,500 Iterations

79


80

BSignal Propagation Data

B.1 Signal Level Map Data

-85

-80

-75

-70

-65

-60

-55

-50

-45

5 10 15 20 25 30 35 40

5

10

15

20

25

30

35

40

x

Signal Level to BWRC Classroom Chan 10 [dbm]

y

[dbm]

Signal Figure B-1Signal Level Map to AccessPoint 1, Channel 10

81

Appendix B: Signal Propagation Data

-85

-80

-75

-70

-65

-60

-55

-50

-45

-40

-35

5 10 15 20 25 30 35 40

5

10

15

20

25

30

35

40

x

Signal Level to BWRC Classroom Chan 2 [dbm]

y

[dbm]


-85

-80

-75

-70

-65

-60

-55

-50

-45

-40

5 10 15 20 25 30 35 40

5

10

15

20

25

30

35

40

x

Signal Level to BWRC 53 Chan 10 [dbm]

y

[dbm]


82

B.1. Signal Level Map Data

-80

-75

-70

-65

-60

-55

-50

-45

-40

5 10 15 20 25 30 35 40

5

10

15

20

25

30

35

40

x


y

[dbm]


-85

-80

-75

-70

-65

-60

-55

-50

-45

-40

-35

5 10 15 20 25 30 35 40

5

10

15

20

25

30

35

40

x


y

[dbm]


83


-85

-80

-75

-70

-65

-60

-55

-50

-45

-40

5 10 15 20 25 30 35 40

5

10

15

20

25

30

35

40

x

Signal Level to BWRC 91 Chan2 [dbm]

y

[dbm]


B.2 Empirical Signal Propagation Model

0 20 40−100

−80

−60

−40

−20

Sig

nal [

dbm

]

0 20 40−100

−80

−60

−40

−20

0 20 40−80

−60

−40

−20

0 20 40−100

−80

−60

−40

−20

0 20 40−80

−60

−40

−20

Sig

nal [

dbm

]

0 20 40−80

−60

−40

−20

0 20 40−80

−60

−40

−20

0 20 40−80

−60

−40

−20

0 20 40−80

−60

−40

−20

Sig

nal [

dbm

]

0 20 40−80

−60

−40

−20

0 20 40−80

−60

−40

−20

0 20 40−80

−60

−40

−20

Figure B-7Signal PropagationCrossections: Crossectionsthrough the maximum ofthe signal propagationmaps shown in section4.2.3.1 shows fairly similarpath loss for similarenvironments.A pair of plots refers to asingle signal propagationmap in the same order asthey are given earlier. Theleft of each paircorresponds to a horizontalcut, the right to a verticalcut.

84

B.3. Triangulation Estimates

B.3 Triangulation Estimates

−10 0 10 20 30 40 50−10

−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx 25dy −16

Triangulation using Permutations of 3 Nodes Figure B-8Triangulation Estimation Set 1 on3 Nodes

−10 0 10 20 30 40 50−10

−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx 13dy 11


−10 0 10 20 30 40 50−10

−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx 14dy 12


85


−10 0 10 20 30 40 50−10

−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx 14dy 13


−10 0 10 20 30 40 50−10

−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx 14dy 15


−10 0 10 20 30 40 50−10

−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx 15dy 15


86


15 20 25 30 35 40 45 50−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx −1.3dy 4.3


15 20 25 30 35 40 45 50−5

0

5

10

15

20

25

30

x [m]

y [m

] dx −0.46dy 1.5


87


15 20 25 30 35 40 45 50−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx −0.49dy 0.22


15 20 25 30 35 40 45 50−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx −0.75dy −0.79


88


15 20 25 30 35 40 45 50−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx −1.6dy −2.7


15 20 25 30 35 40 45 50−5

0

5

10

15

20

25

30

x [m]

y [m

]

dx −2dy −3.8


89


B.4 Networked Ranging Vector DataLocal to Remote Count Local Signal Level Local Noise Level Remote Signal Level Remote Noise Level

avg min max avg min max avg min max avg min max

A_C 155 -77.16 -83 -74 -88.86 -93 -82 -79.69 -82 -77 -96.91 -101 -92

J_I 120 -56.09 -59 -53 -87.96 -93 -83 -58.38 -62 -56 -89.65 -96 -84

J_F 118 -76.63 -79 -74 -89.14 -93 -84 -80.57 -84 -77 -91.09 -97 -86

J_B 110 -64.76 -67 -62 -89.19 -93 -83 -64.16 -69 -61 -90.80 -97 -85

J_E 17 -82.88 -85 -82 -90.11 -92 -84 -86.11 -87 -85 -96.47 -98 -94

J_D 120 -75.3 -78 -74 -87.1 -93 -84 -77.05 -81 -75 -96.23 -99 -92

J_C 54 -82.96 -85 -81 -88.59 -92 -84 -90.77 -93 -89 -97.72 -101 -93

J_G 120 -76.84 -79 -72 -86.25 -93 -83 -79.65 -83 -76 -95.6 -99 -92

H_I 119 -55.48 -58 -53 -89.07 -92 -87 -56.90 -61 -54 -89.91 -96 -84

H_F 119 -70.24 -74 -68 -87.67 -93 -86 -69.92 -73 -66 -91.14 -95 -85

H_E 121 -71 -76 -69 -89.52 -94 -87 -68.45 -72 -66 -96.10 -100 -91

H_D 123 -58.38 -60 -57 -87.86 -93 -85 -59.39 -61 -57 -96.23 -99 -88

H_C 120 -78.98 -81 -76 -89.10 -94 -86 -80.41 -84 -76 -96.91 -101 -93

H_G 119 -63.72 -67 -62 -91.91 -94 -90 -63.11 -65 -62 -95.66 -98 -89

B_H 26 -60.15 -68 -53 -90.38 -95 -83 -59.11 -70 -53 -88.84 -93 -82

B_H 109 -62.11 -68 -59 -92.28 -97 -85 -60.20 -63 -57 -90.44 -93 -88

B_I 108 -72.77 -78 -69 -93.25 -98 -84 -66.40 -71 -64 -90.04 -96 -84

B_F 110 -76.95 -81 -75 -90.69 -98 -85 -72.90 -78 -70 -91.21 -96 -77

B_E 110 -76.87 -81 -72 -92.83 -98 -85 -76.96 -81 -73 -96.24 -99 -93

B_D 108 -69.22 -71 -66 -91.25 -97 -84 -69.46 -72 -67 -96.20 -99 -88

B_C 108 -75.89 -79 -73 -93.39 -98 -85 -81.51 -85 -78 -96.60 -102 -93

B_G 108 -70.87 -74 -68 -90.14 -97 -85 -71 -76 -69 -95.42 -99 -93

E_F 109 -54.88 -58 -50 -94.82 -99 -86 -48.07 -53 -44 -90.93 -95 -85

E_C 110 -48.68 -52 -46 -94.74 -99 -86 -51.13 -54 -47 -96.44 -100 -92

D_F 110 -53.8 -57 -50 -94.45 -98 -73 -54.30 -57 -50 -90.98 -95 -86

D_E 110 -47.92 -50 -45 -95.16 -98 -91 -44.89 -46 -44 -95.98 -99 -93

D_C 111 -50.40 -55 -48 -94.16 -98 -91 -47.98 -51 -46 -96.61 -101 -92

D_G 110 -47.83 -53 -42 -94.10 -99 -89 -42.36 -48 -41 -95.41 -98 -90

G_F 108 -48.74 -51 -42 -93.02 -97 -86 -42.44 -45 -39 -91.04 -95 -86

A_J 106 -54.39 -57 -53 -88.94 -93 -82 -63.30 -65 -60 -87.39 -93 -82

A_H 110 -56.84 -61 -55 -88.10 -92 -82 -57.3 -60 -55 -90.34 -93 -88

A_I 108 -59.06 -61 -57 -83.59 -91 -82 -58.58 -60 -57 -90.16 -96 -84

A_F 107 -75.62 -79 -71 -86.22 -92 -82 -75.63 -81 -71 -90.81 -95 -85

A_B 109 -50.10 -54 -47 -85.28 -92 -81 -50.02 -53 -48 -90.72 -97 -85

A_E 110 -74.41 -77 -73 -86.72 -90 -82 -73.89 -76 -73 -96.21 -100 -93

A_D 82 -72.65 -75 -70 -87.96 -91 -82 -68.96 -72 -68 -94.73 -99 -70

J_H 119 -58.94 -64 -54 -89.00 -93 -83 -55.21 -58 -52 -90.60 -93 -89

A_G 112 -70.48 -75 -68 -84.89 -92 -82 -74.51 -80 -69 -94.91 -98 -69

E_G 95 -53.56 -56 -51 -96.32 -99 -87 -53.8 -57 -50 -94.98 -97 -92

F_C 198 -51.62 -64 -49 -88.05 -94 -74 -52.99 -67 -49 -90.59 -97 -72

G_C 184 -56.91 -61 -55 -85.90 -93 -79 -55.15 -59 -53 -94.16 -98 -82

90

B.4. Networked Ranging Vector Data

Local to Remote Count Local Signal Level Local Noise Level Remote Signal Level Remote Noise Level


C_I 191 -78.01 -81 -66 -88.40 -93 -83 -81.65 -86 -64 -89.14 -94 -85

D_I 124 -67.38 -82 -64 -86.62 -92 -83 -65.48 -78 -62 -89.22 -93 -86

E_I 114 -73.40 -79 -36 -88.82 -93 -83 -81.45 -85 -36 -89.98 -94 -86

F_I 122 -81.85 -84 -78 -89.18 -94 -84 -75.16 -78 -73 -89.22 -93 -86

G_I 109 -75.44 -79 -73 -88.93 -92 -82 -75.42 -79 -72 -89.63 -93 -85

Table B-1Datasets Derived from Networked Ranging Vectors Set 1



J_I 121 -57.61 -62 -55 -88.04 -93 -82 -60.31 -65 -57 -90.15 -96 -84

J_F 93 -59.89 -64 -58 -91.33 -93 -83 -62.56 -65 -59 -91.12 -95 -85

J_E 119 -70.70 -74 -68 -89.51 -94 -82 -80.71 -87 -76 -96.43 -101 -91

J_C 120 -69.98 -72 -67 -88.11 -94 -83 -75.35 -80 -72 -96.38 -102 -92

J_G 125 -66.32 -70 -62 -88.25 -94 -82 -66.94 -74 -63 -94.93 -98 -92

J_A 122 -63.24 -68 -61 -88.94 -94 -82 -66.72 -72 -63 -88.88 -94 -83

H_J 120 -38.8 -45 -36 -90.61 -94 -87 -39.92 -50 -32 -87.43 -94 -82

H_I 122 -43.21 -45 -40 -91.21 -93 -88 -41.88 -44 -39 -89.32 -96 -84

H_F 124 -61.75 -64 -58 -90.41 -93 -88 -58.81 -62 -56 -91.00 -96 -83

H_E 120 -60.11 -62 -58 -90.6 -94 -83 -60.52 -64 -58 -96.15 -100 -91

H_C 120 -67.52 -70 -65 -90.1 -94 -88 -61.61 -64 -60 -96.35 -101 -91

H_G 120 -60.19 -63 -57 -92.05 -94 -90 -58.82 -63 -56 -94.24 -98 -88

B_H 109 -55.35 -58 -51 -88.68 -96 -82 -54.22 -57 -51 -90.01 -93 -87

B_I 92 -41.21 -44 -40 -84.04 -93 -82 -49.65 -55 -47 -89.5 -96 -84

B_F 110 -55.74 -58 -51 -86.84 -96 -82 -55.26 -57 -49 -91.19 -96 -86

B_E 109 -48.52 -50 -46 -87.22 -96 -82 -46.01 -49 -44 -96.02 -100 -92

B_D 112 -43.04 -46 -40 -90.5 -96 -82 -44.00 -48 -38 -95.06 -100 -91

B_C 114 -51.42 -55 -50 -91.26 -96 -83 -53.82 -58 -51 -96.17 -100 -91

B_G 109 -49.90 -55 -44 -85.85 -95 -82 -51.09 -55 -46 -95.34 -99 -76

E_F 110 -55.88 -60 -51 -93.44 -99 -86 -48.68 -52 -46 -91.09 -96 -85

D_J 106 -63.49 -71 -59 -95.35 -98 -82 -58.50 -61 -56 -87.69 -94 -82

D_I 109 -44.72 -46 -41 -93.55 -97 -89 -49.53 -53 -47 -90.47 -96 -85

D_F 110 -51.52 -56 -50 -94.37 -100 -90 -51.64 -57 -49 -91.23 -96 -86

D_E 110 -46.56 -50 -45 -94.92 -98 -91 -45.77 -51 -44 -96.03 -99 -93

D_C 110 -49.27 -52 -46 -94.38 -98 -90 -49.26 -51 -47 -95.98 -102 -82

D_G 116 -47.04 -55 -44 -95.89 -98 -91 -43.62 -47 -41 -95.49 -99 -91

D_A 314 -62.46 -67 -60 -94.76 -99 -87 -63.43 -69 -62 -88.41 -94 -83

G_F 110 -43.13 -46 -38 -92.53 -98 -87 -39.45 -44 -35 -93.3 -97 -77

A_F 110 -74.48 -78 -71 -88.02 -94 -82 -69.78 -74 -67 -91.33 -95 -85

A_E 110 -75.30 -77 -74 -87.36 -92 -82 -70.85 -74 -68 -95.23 -99 -90

A_C 109 -72.42 -74 -70 -85.91 -93 -81 -72.94 -75 -70 -96.44 -102 -91

A_I 118 -54.94 -59 -51 -87.98 -94 -78 -57.23 -61 -52 -89.99 -93 -87

91




J_H 118 -54.94 -59 -51 -87.98 -94 -78 -57.23 -61 -52 -89.99 -93 -87

H_A 122 -54.24 -56 -52 -89.90 -95 -88 -53.36 -56 -51 -87.96 -93 -83

B_J 286 -68.16 -72 -63 -88.35 -96 -82 -66.93 -70 -64 -87.97 -95 -74

B_A 110 -67.38 -70 -65 -89.52 -95 -83 -71.43 -75 -67 -88.07 -94 -83

E_G 95 -53.56 -56 -51 -96.32 -99 -87 -53.8 -57 -50 -94.98 -97 -92

D_H 114 -53.30 -59 -51 -94.21 -98 -87 -53.69 -57 -51 -89.97 -93 -87

A_G 110 -67.38 -69 -66 -91.2 -93 -83 -65.72 -68 -64 -94.87 -98 -92

E_C 110 -48.68 -52 -46 -94.74 -99 -86 -51.13 -54 -47 -96.44 -100 -92

F_C 198 -51.62 -64 -49 -88.05 -94 -74 -52.99 -67 -49 -90.59 -97 -72

G_C 184 -56.91 -61 -55 -85.90 -93 -79 -55.15 -59 -53 -94.16 -98 -82

C_I 104 -48.91 -53 -46 -87.63 -91 -81 -53.72 -62 -49 -89.94 -95 -80

E_I 105 -51.23 -56 -50 -87.91 -91 -82 -61.63 -67 -55 -90.07 -94 -84

F_I 112 -55.92 -59 -52 -86.58 -93 -82 -55.41 -58 -51 -89.91 -94 -87

G_I 114 -55.70 -59 -53 -87.45 -92 -81 -54.84 -57 -52 -90.07 -94 -87

Table B-2Datasets Derived from Networked Ranging Vectors Set 2

92

CGlossary

GPS, geographic information systems (GIS) and location information systems (LIS)technologies, aerial and orbital remote sensing, have developed technical terms pe-culiar to their own usages, and for the uninitiated these terms can be confusing.Following is a glossary of the more common definitions/descriptions in use withinthese disciplines [40].

AAlmanac Set of parameters used by a GPS receiver to predict the approximate

locations of a GPS satellite and the expected satellite clock offset. Each GPSsatellite contains and transmits the almanac data for all GPS satellites. (Seeephemeris).

Ambiguity The initial bias in a carrier-phase observation of an arbitrary numberof cycles; the uncertainty of the number of cycles a receiver is attempting tocount. If wavelength is known, the distance to a satellite can be computedonce the number of cycles is established via carrier-phase processing.

Anti-Spoofing (AS) The process of encrypting the P-Code modulation sequence sothat the code cannot be replicated by hostile forces. When encrypted, the P-Code is referred to as the Y-Code (see Y-Code & Spoofing).

BBaseline The measured distance between two receivers or two antennas.

CCarrier Frequency The basic frequency of an unmodulated radio signal. GPS

satellite navigation signals are broadcast on two L-band frequencies, L1 andL2. L1 is at 1575.42 Mhz, and L2 is at 1227.6 Mhz.

93

Appendix C: Glossary

Carrier Phase The fraction of a cycle, often expressed in degrees, where 360 de-grees equals a complete cycle. Carrier phase can also mean the number of com-plete cycles plus a fractional cycle. In a survey-grade GPS receiver, the receivercan lock on to a satellite and, keeping track of the number of whole cycles ofthe carrier, creates a cumulative phase of the signal which is often referred toas integrated Doppler.

C/A The (clear acquisition) Code consists of a sequence of 1023 bits (0 or 1) that re-peats every millisecond. Each satellite broadcasts a unique 1023-bit sequencethat allows a receiver to distinguish between various satellites. The C/A-Codemodulates only the L1 carrier frequency on GPS satellites. The C/A-Code al-lows a receiver to quickly lock on to a satellite. carrier phase the cumulativephase of either the L1 or L2 carrier of a GPS signal, measured by a receiverwhile locked-on to the signal (also known as integrated Doppler).

Chip time The duration of a single bit in the pseudo-random code sequence usedto spread the spectrum of an information signal.

Chipping rate The inverse of chip time; the rate at which the coded informationsignal bits are transmitted as a pseudo-random sequence of chips.

Circular Error Probable (CEP) The radius of a circle, centered at the true loca-tion, within which 50% of position solutions fall. CEP is used for horizontalaccuracy (see SEP).

DDelayed Retransmission The delayed response of tags, typically implemented

using surface acoustic-wave devices with different delay settings, from whichthe interrogator can infer tag identity.

Differential GPS (DGPS) A technique whereby data from a receiver at a knownlocation is used to correct the data from a receiver at an unknown location. Dif-ferential corrections can be applied in either real-time or by post-processing.Since most of the errors in GPS are common to users in a wide area, the DGPS-corrected solution is significantly more accurate than a normal SPS solution.

Dilution of Precision (DOP) A measure of the receiver-satellite(s) geometry. DOPrelates the statistical accuracy of the GPS measurements to the statistical ac-curacy of the solution. Geometric Dilution of Precision (GDOP) is composed ofTime Dilution of Precision (TDOP) & Position Dilution of Precision (PDOP),which are composed of Horizontal Dilution of Precision (HDOP) & Vertical Di-lution of Precision (VDOP).

Doppler Shift A shift similar to that experienced by audio phenomena, except oc-curring in the electromagnetic spectrum, where an apparent change in signalfrequency occurs as the transmitter and receiver move toward or away fromone another.

Double Difference (see Single Difference) The arithmetic differencing of car-rier phases measured simultaneously by a pair of receivers tracking the samepair of satellites. Single differences are obtained by each receiver from each

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satellite; these differences are then differenced in turn, which essentially deletesall satellite and receiver clock errors.

EEarth Centered, Earth Fixed (ECEF) A Cartesian coordinate system centered

at the earth’s center of mass. The Z-axis is aligned with the earth’s mean spinaxis. The X-axis is aligned with the zero meridian. The Y-axis is 90 degreeswest of the X-axis, forming a right-handed coordinate system.

Elevation Mask An adjustable feature of GPS receivers that specifies that a satel-lite must be at least a specified number of degrees above the horizon before thesignals from the satellite are to be used. Satellites at low elevation angles (fivedegrees or less) have lower signal strengths and are more prone to loss of lockthus causing noisy solutions.

Ellipsoid of Revolution (often referred to simply as Ellipsoid) A mathemati-cal representation of the earth that is an ellipse that is rotated about its minoraxis. An ellipsoid is an equipotential surface of a rotating, homogeneous body.Various ellipsoid models have been determined to approximate the geoid inlocal areas and in a global sense. GPS uses the WGS84 earth model which isbased on the GRS80 ellipsoid.

Ephemeris (plural: ephemeredes) A set of parameters used by a GPS receiver topredict the location of a GPS satellite and its clock behavior. Each GPS satellitecontains and transmits ephemeris data its own orbit and clock. Ephemerisdata is more accurate than the almanac data but is applicable over a shorttime frame (four to six hours). Ephemeris data is transmitted b the satelliteevery 30 seconds. (See almanac).

FFrequency shifting The translation of a signal of one frequency into a signal of

another frequency.

GGeodetic Coordinates A coordinate system whose elements are latitude, longi-

tude and geodetic height. The latitude is an angle based on the perpendicularto the ellipsoid. Longitude is the angle measured in the XY plane (see ECEF).

Geodetic Datum (Horizontal Datum) A specifically oriented ellipsoid typicallydefined by eight parameters which establish its dimensions, define its centerwith respect to Earth’s center of mass and specify its orientation in relation tothe Earth’s average spin axis and Greenwich reference meridian.

Geodetic Height (Ellipsoidal Height) The height of a point above an ellipsoidalsurface. The difference between a point’s geodetic height and its orthomet-ric height equals the geoidal height. geoid the equipotential surface of theEarth’s gravity field which best fits mean sea level. Geoids currently in useare GEOID84 and GEOID90.

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Geoidal Height (Geoidal Separation Undulation) The height of a point on thegeoid above the ellipsoid measured along a perpendicular to the ellipsoid.

Global Orbiting Navigation Satellite System (GLONASS) The Russian versionof GPS.

GPS week GPS time started at Saturday/Sunday midnight, January 6, 1980. TheGPS week is the number of whole weeks since GPS time zero.

IIonosphere Refers to the layers of ionized air in the atmosphere extending from

70 kilometers to 700 kilometers and higher. Depending on frequency, the iono-sphere can either block radio signals completely or change the propagationspeed. GPS signals penetrate the ionosphere but are delayed. The ionosphericdelays can be either predicted using models, though with relatively poor accu-racy, or measured using two frequency receivers.

LL1 & L2 Designations of the two basic carrier frequencies transmitted by GPS satel-

lites that contain the navigation signals. L1 is 1,575.42 Mhz and L2 is 1,227.60Mhz. L-band a nominal portion of the microwave electromagnetic spectrumranging from 1 to 2 Ghz.

MModulated Backscatter The process whereby a tag responds to a reader or inter-

rogation signal or field by modulating the response signal and re-radiating, ortransmitting, it at the same carrier frequency.

Multipath The reception of a signal both along a direct path and along one or morereflected paths. The resulting signal results in an incorrect paseudorange mea-surement. The classical example of multipath is the "ghosting" that appears ontelevision when an airplane passes overhead.

Multiplexing A technique used in some GPS receivers to sequence the signals oftwo or more satellites through a single hardware channel. Multiplexing allowsa receiver to track more satellites than the number of hardware channels atthe cost of lower effective signal strength.

NNavigation Messages Data modulated onto the satellite’s signals. The navigation

data is transmitted at 50 bits per second and contains ephemeris and clockdata for that particular satellite, other data required by a receiver to computeposition velocity and time and almanac data for all NAVSTAR satellites. Thedata is transmitted in 1500 bit frames, each requiring 30 seconds to transmit.A complete set of data to include all almanacs, timing information, ionosphericinformation and other data requires 12-1/2 minutes to transmit.

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NAVigation Satellite for Timing And Ranging (NAVSTAR) Another term forGPS or sometimes used in conjunction with GPS as in "NAVSTAR GPS.".

OOn-the-Fly (OTF) A term used to describe the technique of resolving differential

carrier-phase integer ambiguities without requiring a GPS receiver to remainstationary.

Orthometric Height (Orthometric Elevation) The height of a point above thegeoid.

PP-Code "Precise" or "Protected" code which is bi-phase shift modulated on both

the L1 and L2 carrier frequencies. P-code has a 10.23MHz bit rate and, asimplemented in GPS, has a period of one week. Each satellite has a uniqueP-code that is used to distinguish the satellite from all other GPS satellites.

Postprocessing The reduction and processing of GPS data after the data wasactually collected in the field. Post-processing is usually accomplished on acomputer in an office environment where appropriate software is employed toachieve optimum position solutions.

Precise Positioning System (PPS) The more accurate GPS capability that is re-stricted to authorized, typically military, users.

Pseudorandom Noise (PRN) The P(Y) and C/A codes are pseudorandom noisesequences which modulate the navigation signals. The modulation appears tobe random noise but is, in fact, predictable hence the term "pseudo"random.Use of this technique allows the use of a single frequency by all GPS satellitesand also permits the satellites to broadcast a low power signal.

Pseudorange The measured distance between the GPS receiver antenna and theGPS satellite. The pseudorange is approximately the geometric range biasedby the offset of the receiver clock from the satellite clock. The receiver actuallymeasures a time difference which is related to distance (range) by the speed ofpropagation.

RReal Time Kinematic (RTK) A DGPS process where carrier-phase corrections are

transmitted in real-time from a reference receiver at a known location to oneor more remote "rover" receiver(s).

Reference Network A series of monuments or reference points with accuratelymeasured mutual vectors/distances that is used as a reference basis for cadas-tral and other types of survey.

Reference Station A point (site) where crustal stability, or tidal current constants,have been determined through accurate observations, and which is then usedas a standard for the comparison of simultaneous observations at one or more

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subordinate stations. Certain of these are known as Continuous OperatingReference Stations (CORS), and transmit reference data on a 24-hour basis.

SSelective Availability (SA) The process whereby DoD "dithers" the satellite clock

and/or broadcasts erroneous orbital ephemeris data to create a pseudorangeerror (see Standard Positioning System).

Spherical Error Probable (SEP) A navigational measure of accuracy equalingthe radius of a sphere, centered on the true location, inside which 50% of thecomputed solutions lie. (See CEP.)

Sidereal Time Iis defined by the hour angle of the vernal equinox. Taking themean equinox as the reference yields true or apparent Sidereal Time. Nei-ther Solar nor Sidereal Time are constant, since angular velocity vary due tofluctuations caused by the Earth’s polar moment of inertia as exerted throughtidal deformation and other mass transports.

Single Difference The arithmetic "differencing" of carrier phases simultaneouslymeasured by a pair of receivers tracking the same satellite (between-receiversand satellite), or by a single receiver tracking two satellites (between-satelliteand receivers); the former essentially deletes all satellite clock errors, whilethe latter essentially deletes all receiver errors.

Spoofing The process of replicating the GPS code in such a way that the user com-putes incorrect position solutions.

Standard Positioning System The less accurate GPS capability which is avail-able to all. (See Anti-Spoofing and Selective Availability).

Static Observations A GPS survey technique that requires roughly one hour ofobservation, with two or more receivers observing simultaneously, and resultsin high accuracy’s and vector measurements.

TTriple Difference The arithmetic difference of sequential, doubly-differenced carrier-

phase observations that are free of integer ambiguities, and therefore usefulfor determining initial, approximate coordinates of a site in relative GPS po-sitioning, and for detecting cycle slips in carrier-phase data. (See single differ-ence & double difference)

UUniversal Time Coordinated (UTC) Time as maintained by the U.S. Naval Ob-

servatory. Because of variations in the Earth’s rotation, UTC is sometimes ad-justed by an integer second. The accumulation of these adjustments comparedto GPS time, which runs continuously, has resulted in an 11 second offset be-tween GPS time and UTC at the start of 1996. After accounting for leap sec-onds and using adjustments contained in the navigation message, GPS timecan be related to UTC within 20 nanoseconds or better.

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WWorld Geodetic System 1984 (WGS 84) A set of U.S. Defense Mapping Agency

parameters for determining global geometric and physical geodetic relation-ships. Parameters include a geocentric reference ellipsoid; a coordinate system;and a gravity field model. GPS satellite orbital information in the navigationmessage is referenced to WGS 84.

YY-Code The designation for the end result of P-Code during Anti-Spoofing (AS) ac-

tivation by DoD. Y-Code tracking, civilian several methods of obtaining validdata from encrypted Y-code are available:

1. Signal squaring (now obsolete) multiplies the signal by itself, thus delet-ing the carrier’s code information and making distance measurement (rang-ing) impossible. Carrier phase measurements can still be accomplished,although doubling the carrier frequency halves the wavelength, furtherweakening an already weak signal. This method required collecting dataover a much longer period.

2. Cross correlation, where no local (receiver) code is generated to match theL1 & L2 encrypted Y-codes. The ionosphere "slows" the L2 Y-code slightlyin respect to the L1 Y-code, hence the difference between these distancescan be measured and, once known, matched and multiplied to remove thecodes and leave pure carrier frequencies for measurement. This does awaywith the half-wavelength problem, but again results in a weakened signalthat necessitates longer observation periods.

3. Code correlation & squaring. Here, the L1 & L2 Y-Codes are comparedagainst a locally generated P-Code; the difference (the encrypting Y-codesignal) is thus revealed, measured and squared so that pure carrier fre-quencies can be measured. Squaring once again weakens the resultinghalf-wavelengths of both carrier frequencies, and once again requires longerobservation periods.

ZZ count A 29-bit binary number consisting of the fundamental GPS time unit. The

(10) most significant bits carry the GPS week number, and the (19) least sig-nificant bits give the time of week (TOW) count in units of 1.5 seconds.

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