Effects ofAntenna Polarization on RSSI BasedLocation Identification

Mark Barralet, Xu Huang, and Dharmendra SharmaFaculty of Information Sciences and Engineering

University of CanberraCanberra, ACT 2601, Australia

Location Identification

Determination of location can be done in a number ofways including: triangulation, trilateration andmultilateration.

Triangulation requires knowing the angle between nodes.However this is generally not an option for this type ofwireless network as isotropic antennas tend to be used forboth practical and cost reasons. For the 2D case the locationof an unknown node can be determined by trilateration.This requires 3 nodes of known location. The 4th node ofunknown location can be determined if the distance to thereference nodes is known. This is shown graphically in Fig1. However we can only determine the distance within acertain degree of certainty. This affects our ability toaccurately determine correct location. By using morereference nodes we can partially average out thisuncertainty.

Range measurement

Two methods of determining distance (refereed to aspseudorange in GPS systems) are based on: propagationtime and Radio Signal Strength (RSS).

Electro magnetic radiation (radio waves) travels at thespeed of light, which is approximately 3 x 108 m/s in freespace. So we can determine distance by observing the timeit took a transmitted signal to reach the receiver. Oralternatively the time difference between several receiversat known locations. Due to the very high speed ofpropagation (1 meter takes just 3.3ns), this requires fastprecision timing and as such is not well suited to the costand power requirements of low power radio frequency (RF)applications.

RSSI based distance calculations add no extra cost to thedevice. But do require additional power but much lowerthan that of GPS. However RSSI measurements give loweraccuracy range results due to variable Attenuation (path

3

2

4(b) Multi1ateration

n

(a) Trilateration

Fig 1. Distance (range) based location techniques [5]

INTRODUCTION

Determining the location of a node in a wireless networkhas real commercial uses. An example is determining thelocation of mobile assets like equipment and people in abuilding. Such systems have found there way into a numberofhospitals to track the location ofmedical equipment, staffand patients [10]. These systems have also been used toprevent loss of equipment or restrict access to areas.

This type of location tracking system requires reliablelow cost, low maintenance solutions. It is not acceptable tobe changing or charging batteries at regular intervals likeyou would have to with a GPS based system.

Abstract-Real-time position localization of moving objectsin an indoor environment is an encouraging technology forrealizing the vision of creating numerous novel location-awareservices and applications in various market segments.Increasing the accuracy of these location tracking systems willincrease their usefulness. An off the shelf developmentplatform that uses Radio Signal Strength Indication (RSSI)based location tracking technique is studied. In this paper weinvestigate the affects of polarization on the accuracy of anindoor location tracking system. The accuracy of the locationcalculation is mainly dependent on accuracy of the rangemeasurements. We present an approach to increase systemaccuracy based on this investigation.

We established a model for determining range from RSSIand showed that the model fits our own experimental data.The model includes parameters used to account ofenvironmental effects and we use the least squares method ofdetermining the parameter values. Antenna polarizationangle will affect RSSI and thus range accuracy. Weempirically show that the model is still valid for polarizationmismatch but with different environmental parameter values.We then analyze the affects that these parameters andpolarization have on our location system. A method based onsemi-automated trail and error is proposed as a better methodfor selecting the environmental parameters. Usingexperimental data we show that if we adjust the modelparameters to account for polarization angle then we canincrease location accuracy. Adjusting parameters forpolarization is fairly trivial to implement when thepolarization angle is known. A practical solution fordetermining the polarization angle is with an accelerometer.The addition of an accelerometer could also be used to increasethe battery life of the node.

Index Terms-Location, RSSI, RFID, Sensor Networking,Zigbee

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loss) and fading effects with high variance.In the case where there is a direct path between a

Transmitter and receiver. The receiver signal power, Pr isrelated to distance, d, by the inverse square law.

Pr oc ti2 (1)

However this is an ideal case for a point source. In the realworld the signal power often decays at a faster or slower rate,and we can express this as:

Pr oc tin (2)

Where n is the loss exponent. An excepted form of therelation between distance and receive power simplified forthe case of a one meter reference distance is [:

Pr(d) (dBm) = A - 10n·loglO(d) (3)

Where A = Received power in dBm at one meter, and n isthe loss parameter (or loss exponent), d is the distancebetween the transmitter and receiver. In real life the valuesof n and A can only be determined empirically [7].

Multipath signals arrive at a nodes antenna via thereflection of the direct signal off of nearby objects. Theresultant or total signal available to the receiver will be thevector summation of the direct signal and all of themultipath signals.

Another factor that affects received signal strength isantenna polarization. Small simple Omni-directionalantenna's produce linearly polarized radiation. The electricand magnetic fields components of electrical magnetic(EM) radiation are perpendicular to the direction ofpropagation. As the magnetic field is perpendicular to theelectric field we normally describe the wave polarizationwith respect to the electric field only. For the linearpolarized case, the EM field remains in the same plane as theaxis of the antenna. When the antenna produces an electricfiled that remains in a plane parallel with respect to earth thepolarization is said to be horizontal. Likewise when theelectric field remains in a plane normal to earth thepolarization is vertical. To receive maximum power thereceiving antenna polarization must be in the same as thetransmitting antenna. The loss due to misaligned antennapolarization is:

Polarization Mismatch Loss (dB) = 20 log (cos 8) (4)

Where 8 is the polarization angle between the antennas[3].

This gives an infinite loss when the mismatch is 90° (orperpendicular). However in practice most antennas used inlow cost short range applications do produce a field withpolarization in more than one direction [4]. Additionally inthe case of multipath; if the objects that reflect the signalsare not aligned or parallel with the polarization of theincident signal, the reflected signal will undergo apolarization shift [3]. In general, there will be a number ofsignals arriving at the receive node that are not aligned withthe polarization of the receiver antenna. Rotation of theantenna from vertical to horizontal, will affect the receiveenergy from these multiple signals.

DEFINITIONS

The following is a list terms used in this paper:Node: An RF transceiver in the network.

I-hop: Direct communication between source anddestination nodes in the network (i.e. the data not routedthough other nodes.)

Blind Node: A node that calculates its own position.Reference Node: A node at a known fixed location

(sometimes refereed to as a beacon)Dongle: A node that is connected to a computer and

configures and monitors the Blind and Reference nodes.RSSI: Received Signal Strength Indicator, an

approximation of RF signal power as measured by theRF transceiver.

CC243I: Texas instruments RF transceiver. [2][16][17]

CC2431

The CC2431 is an inexpensive 2.4 GHz IEEE 802.15.4compliant RF transceiver from Texas Instruments. Itcombines a 8051 microcontroller and a RF transceiver withDSSS (Direct Sequence Spread Spectrum) modem into onepackage. The CC2431 also includes a hardware 'LocationEngine' that can calculate the nodes position given the RSSIand position data ofreference nodes. The main inputs to thelocation engine are the; (x,y) location and RSSI of referencenodes, and the parameters A and N used internally to convertRSSI values into ranges. The output ofthe 'location engine'is the calculated (x,y) position. The 'location engine' usesan unspecified technique optimized for minimum resourceuse and speed. The precision for the (x,y) locationcoordinates is 0.25meters. The precision of A is 0.5dBm,while n uses an index into a lookup table that limits itsprecision (see Table 1). The range of valid RSSI to thelocation engine is 40 to 81 (in -dBm), so RSSI values higherthan -40dBm must be set to -40dBm. This means that theminimum measurable range is around 1 meter (assuming anRSSI of -40dBm at 1 meter). Thus affecting the accuracywhen the blind node is closer than 1 meter to a referencenode.

Table I [7]

RSSI

The CC2431 has a built in RSSI register. The RSSI valueis always averaged over 8 symbol periods (128uS). TheRSSI value can be used to determine the RF signal Powerwith reasonable accuracy as shown in Fig 2.

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One problem with the RSSI is that a narrow bandinterferer inside the channel bandwidth can affect the RSSIvalue [15]. Thus it is important to only use the RSSI valueon valid error free packets as bad packets have a higherprobability of being subject to due to this type ofinterference.

Fig 2. Typical RSSI value vs.

RF Levet

power [12]

RSSI vs Distance

70

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20

10-10 -5 0

10 Log( Distance)

Fig 3. Environmental effects are not just caused by obstacles. Greaterreflections in the shielded room cause higher multipath effects. In this casethe small size of the room means the primary multipath signals arrive withnet constructive superposition.

+VH I

+

l1V45I <>VV

70

65

60

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!(ij 50UJD::

45

40

350

Observations indicate that for small distances the VV(parallel) case gives the highest RSSI, followed by V45,then VH (perpendicular). However in our observations therate ofRSSI decay over distance was higher in the VV case.This indicates that in general VH gives a higher value A(lower signal strength at one meter) and a lower n (loss

5 10 1510 Log( Distance )

Fig 4. RSSI vs. distance (range). Measurements were taken in a longhallway of varying width. The transmitter is fixed at a location and theantenna is always vertically polarized. The RSSI is recorded for receiveantenna polarization ofvertical, horizontal and 45 degrees (Le. Parallel (0°),orthogonal (90°) and 45 degrees)

Polarization

Antenna polarization does affect the RSSI value and thuscan affect the outcome of location identification. Ameasured example ofhow polarization affects average RSSIover a range ofdistances is shown in Fig 4. The polarizationof the system is identified in an abbreviated form where thefirst letter identifies the polarization of the transmit antennaand the second letter (or number) is the polarization of thereceive antenna. The abbreviations used are V (vertical), H(horizontal), 45 (45 degrees).

RANGE MEASUREMENTS

Determining distances between nodes is key part oflocation determination by multilateration. The 'locationengine' uses the following model for the relation betweendistance and RSSI:

RSSI = -( IOn LoglO(d) + A) (5)

Where RSSI is in dBm, n is the path loss exponent, d isthe distance in meters and A is the RSSI in -dBm at onemeter.

By measuring the RSSI at different distances the valuesof n and A can be determined. This can most easily be doneand understood by letting D1og=1 Olog(d). Then we canrewrite this as a more simple and more obviously linearequation:

-RSSI = n·D1og + A (6)

(Which is clearly in the form y=mx+b) We can then use alinear least squares approximation to solve for n and A. Ifwe plot the least squares line, A is the value at zero crossing( 10·LOG(lmeter) = 0), and n is the slope of the line.

Unfortunately effects like multipath fading often result inthe average RSSI value at a particular distance notconverging on the least squares line ofbest fit as the numberof samples increases. (Remembering that received signal isthe vector sum of the direct wave plus all of the reflected,scattered, diffracted, or refracted waves, and is highlyposition and time dependent). However there is asignificant variation between each sample so averaging isrequired to reduce the worst case error. Note that fadingeffects apply not only to distance (or location) but are alsotime dependent. Meaning that at a particular location theaverage RSSI will also fluctuate with time. The values ofAand n are both affected by environmental factors and varysignificantly between different locations as shown in Fig 3.

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polarization. This is because real isotropic antennasproduce little power in the direction of the antenna axis.The blind nodes on the other hand are mobile and thus thepolarization is likely to change dynamically. The testfollows these assumptions and keeps the reference nodesfixed for vertical polarization and the blind nodes arechanged between vertical and horizontal polarization.

Range measurement test

As an example of range measurement accuracy, the blindnode was placed at the centre location and the RSSI datawas collected by monitoring the Zigbee network packets.The result of this test is shown in Fig 6. The nodes were allrunning the Zigbee Z-location reference firmware v1.4.2provided by Texas Instruments as part of the Z-Locationsoftware for the CC2431DK. Source code v1.4.3 isprovided as part of the Z-Stack (Zigbee protocol stack).

Fig 6. Graphical representation of location identification problem with realdata. The small filled squares represent the location of the reference nodes.In this case the blind node is in the center of all the reference nodes at(13,12.5) and is marked by a star. The circles represent the distance basedon averaged RSSI measurements using (5), with parameters A=44, n=3.5.Ideally all circles should intercept at the point (13, 12.5).

It was found that the A and n parameters calculated in theprevious section gave very poor location accuracy.Increasing the value ofn tended to give much better results.This can be explained by the high variance of the RSSImeasurements and the logarithmic relationship betweenRSSI and distance. Fig 7 shows the relation between nandrange (distance) error that helps makes this concept clearer.

86

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I!:.I!:.

0 VVI!:.

I!:. V45

+ HV

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35 +---...,.----,..-----.,-------r----I

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exponent) for best range measurement results.For our experimentation on location accuracy we study

only a single room. The RSSI vs distance measurementswere observed along a diagonal of the room and the resultsare shown in Fig 5. For this particular short distance singleroom case, we note that there is a fairly significantdifference in the slope and zero crossing of the least squaresline between perpendicular and parallel antennapolarization.

60

~ 55!~ 50D::

10 Log( Distance)

65

Fig 5. Measured RSSI vs Distance with 3 polarization setups. Note that inall cases there is a high variance between the least squares line of best fitand the 32 sample averaged data.

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LOCATION IDENTIFICATION

The testing of polarization effects on locationidentification were performed with the CC2431 DKDevelopment Kit from Texas Instruments. Location engineresults were observed using the PC application 'Z-locationEngine' downloaded from the Texas Instruments website.The Z-location software was used to configure the locationsof the reference nodes and configure the parameters of theblind nodes (most importantly the A and N parameters)

The important thing to note about the reference system isthat during RSSI measurements, the blind node is thetransmitter and the reference nodes are the receivers. Theblind node calculates its own position, reducing the networktraffic that needs to be sent to the Dongle. It also allowsblind nodes to know where they are without the presence ofthe Dongle.

Test setup

For testing a room (measuring 5.5 x 8 meters) was clearedand eight reference nodes were arranged around theperimeter of the room. For ease all nodes were placed atground level. A 2-diementional Cartesian coordinatesystem with a negative y axis is used to define the positionof the nodes in positive space.

It is reasonable to assume that because reference nodesare at a fixed location that it is easy and possible to set themall to the same polarization. Assuming that the nodes arenormally located on walls, then it is best to use Vertical

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Fig 7. RSSI based range error vs. path loss parameter (n), based on thesame measured RSSI data set as Fig 6 and keeping A=44. The error iscalculated as the difference between calculated range and actual range. It isseen that a positive change in n from the point ofminium error gives a lowerrange error than the same change in the opersite direction.

n (path loss parameter)

21

N (path loss exponent index)

8

Average Displacement Error (meters) W

In short, the method is to calculate the location over asweep range of the A and n parameters at differentpolarizations, then pick the best result(s).

876

-Average--.- (10,10)~(12, 10)____ (14, 10)____ (16, 10)____ (10,15)~(12, 15)--e- (14, 15)--e- (16, 15)

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Location accuracyThe accuracy of the location system is defined here as the

displacement error in meters between the actual (xa,Ya)position and the measured (xnz,Ym) position of the blind node,and is calculated using the equation:

Displacement Error = J(xm - xa )2 + (Ym - ya )2 (7)

The location accuracy was observed for parallel and perpendicularpolarization at a number of different actual locations. The results in

Fig 8 were taken for various values of the parameters Aand N, and show the effect that these parameters have on theaccuracy ofthe system. As predicted in the previous sectionthe best average result for perpendicular polarization occursfor higher values ofA and lower values of N (A=48, N= 12)compared to that of Parallel (A=44, N=20). If the paralleloptimized values (A=44, N=20) were used to calculate theperpendicular case, then the average error would increasefrom approximately 1.5 meters to 1.7 meters, that equates toa 0.2 meter improvement. Selecting the best compromise(A=46, N=18), the advantage of adjusting the parametersbased on polarization is reduced to 0.1 meter accuracy gain.

During testing it was noted that the reference node atlocation (12, 10) was consistently reporting very low RSSI,thus it is likely that the node was faulty. This would havelead to a higher n to reduce the range error influence thisnode had on the error calculation (see Fig 7). Had this nodenot been faulty it is highly likely that the accuracyimprovement would have been higher. We will be doingfurther testing to prove that this is the case.

System Calibration

To mathematically determine the best parameters (A andn) to use we would need to have a model that accuratelypredicted the average RSSI variation in a particular area ofinterest (and know the routine used in the 'location engine').Given that the least squares method does not give adequateresults, we propose the following semi-automatic trial anderror technique for calibrating the system for optimumaccuracy:

Average Displacement Error (meters) HV

N (path loss exponent index)

Fig 8. Displacement error between actual location and measured location.The y-axis gives the displacement in meters that the calculated position isaway from the actual position, ideally this should be zero. The x-axis givesvalues of the path loss exponent index (N). This data shows the averageresult for two different blind nodes at the physical locations (15,11) and(14,13).

There is only a limited set of discrete parameter valuesusable in the 'location engine' (20 values ofA and 31 valuesofN) it is possible to try them all then process the results andpick the best set. The maximum time that the blind nodewould take to do a full sweep of all valid parameters is8seconds (20*31 *13ms). The results of which could bestreamed to the dongle as they were calculated. Calibrationsoftware could be written for the PC that uses the results todetermine the best parameters. The calibration consists ofplacing the blind node accurately at a number of userdefined locations. The data could be processed to return thebest values for whatever statistical outcome was desired.

For best results each room or area would need to betreated separately. The best parameters should bedetermined for the building as a whole. Then each room orarea would have its own set of parameters. The system

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would work such that the blind node first calculates whichroom it was in then only use reference nodes from that roomto calculate a more accurate position within that room orarea (additionally we could use nodes from different roomsby applying a wall correct factor [8]). For this system, thereference nodes should store a room 'room id' and theoptimal parameter sets for that room. This way we wouldn'tneed to carry a database in the blind node and we canmaintain the ability of the blind node to calculate locationindependently of the dongle.

Antenna Polarization Determination

The antenna polarization can be determined easily by theaddition of a 2-axis accelerometer. An accelerometer is adevice that converts the mechanical energy of accelerationinto an electric signal. Gravity is a fixed accelerationtowards the center of earth. When the device is aligned sothat its sensing axes are normally parallel to earth, the anglecan be calculated as ARCSIN(Ax/ Ig), where Ax is the valuefrom the accelerometer.

The addition of a 3-axis accelerometer would also allowfor other benefits. It could be used to reduce the powerconsumption by only performing location measurementswhile moving and powering down the node when stationary.Where the node is used as an active RFID tag tracking aperson it could double as a pedometer counting the numberofsteps taken. On fragile equipment it can be used to detectwhen equipment been dropped or incorrectly orientated. Bymathematical integration of the acceleration data thevelocity and displacement can also be calculated.

CONCLUSION

In order to obtain optimal design for a RSSI basedlocation system, we have shown experimentally thatlocation accuracy of our system is mostly influenced by theaccuracy of the range measurements. Although we have avalid model that can be used to convert RSSI into a range,the variance in the RSSI can produce large range errors.The RSSI measurement is affected by many factors such asmultipath fading, obstructions and antenna pattern andpolarization. Our model has two major parameters that areused to account for all these issues. The value of theseparameters is not fixed and can only be determinedempirically. There is little we can do about most of theseissues. However we can extract a little more accuracy fromthe location system by accounting for antenna polarization.To do this we can either take parameter values that are acompromise or we actively track the polarization mismatchand adjust the parameters to suit. Due to the high varianceand low increase in accuracy it is probably only worthconsidering storing two sets of parameters. One set forperpendicular (90°) mismatch and the other for parallel (0°)mismatch. A suitable point the make the change would be60° equating to a polarization mismatch loss of 6dB, seeequation (4). A suitable method for actively determiningantenna polarization is with an accelerometer. Althoughthis adds cost it does add other value. The most useful ofwhich, is to suspend location tracking activities when thenode is stationary in order to conserve battery power.

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REFERENCES

[I] P. M. Shankar, Introduction to Wireless systems, New York: JohnWiley & Sons, 2002, pp. 14.

[2] CC2431 Datasheet (Rev 2.01) SWRS034B[3] "Antenna Polarization Considerations in Wireless Communications

Systems", Cushcraft Corporation[4] Audun Andersen, "Antenna Selection Guide", SWRA161,

Application Note AN058, Texas Instruments.[5] SHI Qin-Qin, HUO Hong, FANG Tao, LI De-Ren, "Using Linear

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[6] K. Aamodt "CC2431 Location Engine", Application Note AN042(Rev. 1.0), SWRA095, Texas Instruments

[7] Hongbin Li, Xingfa Shen, Jun Zhao, Zhi Wang, and Youxian Sun,"INEMO: Distributed RF-Based Indoor Determination withConfidence Indicator"

[8] Paramvir Bahl and Venkata N. Padmanabhan, "RADAR: AnIn-Building RF-based User Location and Tracking System",Microsoft Research

[9] Jan Blumenthal, Ralf Grossmann, Frank Golatowski, DirkTimmermann "Weighted Centroid Localization in ZigBee-basedSensor Networks", IEEE International Symposium on IntelligentSignal Processing, (WISP 2007), Madrid, October3rd,2007

[10] Ron Weinstin, "RFID: A Technical Overview and Its Application tothe Enterprise", IEEE Computer Society, 2005, 1520-9202/05/

[II] Xiuzhen Cheng & Andrew Thaeler Guoliang Xue Dechang Chen,"TPS: A Time-Based Positioning Scheme for Outdoor WirelessSensor Networks", IEEE INFOCOM 2004,0-7803-8356-7/04/

[12] "CC2430 Data Sheet (rev. 2.1)", SWRS036F, Texas Instruments[13] Junhui Zhao, Yuqiang Zhang and Mengjie Ye, "Research on the

Received Signal Strength Indication Location Algorithm for RFIDSystem", ISCIT 2006, 0-7803-9740-X/06/

[14] Sergio Polito, Daniele Biondo, Antonio lera, Massimiliano Mattei,Antonella Molinaro, "Performance Evaluation Of Active RfidLocation Systems Based on RF Power Measures", The 18th AnnualIEEE International Symposium on Personal, Indoor and MobileRadio Communications (PIMRC'07), 1-4244-1144-0/07/

[15] Siri Namtvedt, "RSSI Interpretation and Timing", Texas InstrumentsDesign Note DN505, SWRAI14B

[16] "CC243IDK Development Kit User Manual Rev. 1.5", SWRU076D,Texas Instruments

[17] "CC2430DK Development Kit User Manual Rev. 1.0", SWRUI33,Texas Instruments

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