Localization of Orphan Utility Meters Basedon Spatio-Temporal Signature Information

Yimin Zhang*, Xin Li*, Emmanuel Monnerie~, Glenn A. Pritchard#, and Ruben Salazar Cardozo~

*Wireless Communications and Positioning LabCenter for Advanced Communications

Villanova University, Villanova, PA 19085, USA

~ Cellnet+Hunt, Alpharetta, GA 30022, USA

#PECO, Meter Reading Technologies, Philadelphia, PA 19101, USA

Abstract - As utilities convert from manual meter reading toautomated systems, a new set ofoperation challenges arise in theday-to-day operation of the metering system. One of the mostsignificant problems occurs when a meter is installed, but thephysical location of the meter has not been properly recorded bythe utilities' system of record. This condition is commonlyreferred to as an "orphan" meter. Current methods of locatingorphan meters are manual and time intensive. This paperpresents a new method for electronically locating orphanmeters. The technique leverages spatio-temporal correlationsbetween the reference meters and the orphan meter. Additionalinformation, such as the received signal power, is also used toprovide more robust location estimation performance.Ray-tracing simulation and field measurement results haveshown promising localization results of the orphan meters.

Index Terms - Localization, spatio-temporal signature,utility metering network, orphan meter, field test.

I. INTRODUCTION

Many utility metering network systems, such asCellnet+Hunt's Advanced Metering Infrastructure (AMI)system, use low-power radio transmitters, integrated into thecustomers' utility meters, to send out customer energyconsumption data. This meter transmitter is programmed toperiodically send out a short burst of data, which is capturedby at least one of several access points distributed within acertain radius of the meter.

The "orphan" meter problem arises when the ID andlocation information of a meter are not associated in thecustomer record system. The AMI network receives the datatransmissions from an orphan meter whose physical locationis not registered in the database. Until the meter is linked to anaddress, the service provider does not know who is using thepower and, as a result, cannot collect the associated revenue.The current practices to identify and locate orphan metersinvolve manual meter investigation, local area audits andvisual inspections. Such practices are often highly timeconsuming and ineffective. Our objectives are to develop

This work is supported by Cellnet+Hunt.

techniques that can effectively localize orphan utility meters.

Localization of transmitters has been an attractive topic inthe past decade. One of the main motivations stems from thefact that the U.s. Federal Communications Commission(FCC) requires automatic determination of the preciselocation of all enhanced 911 (E911) callers. When asynchronized network is available, time-delay-of-arrival(TDOA) based approaches are considered promising innon-GPS based outdoor applications to achieve multilateralpositioning. Direction-of-arrival (DOA) estimation methodscan also be used in multiple base stations for multi­angulation. Such methods do not require accurate timing, butthe performance is sensitive to multipath signals. Patternmatching based positioning is another approach that workswell even in severe multipath environments [1-4]. The designinvolves a location server with a database that includesmeasured and predicted signal characteristics, and thelocation of the unknown mobile phone can be computed bycomparing signals received by the mobile with the signalvalues stored in the database.

For the underlying application, it is highly desirable thatthe equipment to be developed can be operated in a singleposition as well as non-coherently in multiple positions.Thus, the technique we develop in this paper is similar topattern matching method. Signals transmitted fromneighboring meters whose locations are recorded in the data­base are used as references. Due to the specific nature of theproblem, however, existing techniques may be foundineffective. The fact that both orphan and known meters arestationary highly limits the information that we can use forthe localization. The utility meters may be separated by tensof meters, thus the reference signatures are spatially deci­mated. The calibrated database does not have uniquesignatures corresponding to each exact meter locations.Another important challenge is that the transmitted signal isbursty and, therefore, we cannot rely on receiver motion tosynthesize an aperture. Additionally, as the AMI receiver ismounted on a vehicle which is typically of a low altitude,severe multipath may occur and makes the localization

978-1-4244-2241-8/08/$25.00 ©2008 IEEE 170

where

IV. SIMULATION RESULTS

In the simulation study, we focus on urban environmentpropagation. A modified ray-tracing model [8, 9] is deve­loped and applied to a dense residential downtown area insouthern Philadelphia, where orphan meters are actuallyobserved. The operating carrier frequency is 915 MHz. The

(5)

(4)

(3)Pi =--;======~=====

and Po and Pi denote the received power of the orphan meterand the i-th reference meter, respectively, averaged over allthe array antennas. kp takes a positive value representing thesensitivity of the cost variable to the power difference.

In most situations, it is expected that a reference meter thatis closely spaced to the orphan meter renders a high value ofAi. Further improvement of performance can be achieved byusing probabilistic localization techniques [7].

4. Localization

Moreover, the location of an orphan meter can be estimatedas the weighted average ("gravity center") of the locations ofthe known reference meters whose cost variables are higherthan a pre-specified threshold. The accuracy of suchlocalization can be further improved by recording data atmultiple receiver locations.

where superscript H denotes conjugate transpose of a vector.In practice, the spatio-temporal correlation coefficient isdirectly estimated from the received signals corresponding tothe orphan meter and the reference meters without explicitlyperforming channel estimation.

The received signal power may also be used to enhance theorphan meter localization performance. It should be noticedthat, when the localization of an orphan meter is performedusing a single array, the signal power level is highlysymmetric to the receiver and thus can only be used as asecondary reference for localization purpose. When datarecording is performed at multiple receiver locations,nevertheless, this information can be used for multilaterationas widely discussed in literature [4, 5].

3. Data Fusion

The spatio-temporal correlation coefficient and the receivedsignal power can be fused into a single cost variable as

2. Feature Extraction

The spatio-temporal correlation coefficient between the delayprofiles of the orphan meter, ho(t), and the i-th referencemeter, hlt), is defined as [6]

f h~ (t)h; (t)dt

f h~ (t)h o(t)dt f h~ (t)h i (t)dt

problem more formidable.

In this paper, we pursue meter localization by using amulti-antenna receiver and comparing the spatio-temporalsignature of an orphan meter with those of known meters.After examining various variables available for orphan meterlocalization, the spatio-temporal correlation coefficient hasbeen found to be key in meter location characterizations.Additionally, the received signal power can also be used toimprove the localization accuracy. Simulation results usingray-tracing channel modeling techniques show that theproposed method is promising. Further, a general-purposetest platform was established and field measurements werecarried out to validate the localization performance.

III. LOCALIZATION TECHNIQUES

The localization of an orphan meter can be achieved throughthe following steps:

1. Data Recording and Alignment

The key parameter used for orphan meter localization is thespatio-temporal correlation coefficient. Because each datapacket is transmitted in a different time to avoid collision, thereceived data corresponding to different meters have to betime aligned. This necessitates storing the data received fromthe orphan meter as well as reference meters so that corre­lation and comparison can be made. While the estimation ofthe exact time delay (or equivalently the transmitted timing)is impractical, the spread spectrum signal and the bursty datanature allow a coarse estimation of the symbol and chippositions for the evaluation of spatio-temporal correlation.An accuracy of a fraction ofthe chip can usually be achieved.

II. SYSTEM STRUCTURE AND PROPAGATION MODEL

Consider a spread-spectrum signal where the spreading codeand the preamble data bits are known to the receiver. Thereceiver consists of an N-element array. The received signalvector corresponding to signal transmitted from a distanttransmitter is expressed as

L-l

x(t) =La,s(t-T,)+n(t), (1)'=0

where L is the number of paths, set) is the spread signalwaveform, a, is the complex-valued spatial signature vectorcorresponding to the l-th path, and tl is the time delay of thel-th path. In addition, net) is the noise vector, which isconsidered to follow independent and identically distributedGaussian distributions with a zero mean. Note that, in ourapplications, the channels are considered quasi-stationary.The corresponding spatio-temporal channel signature,

L-l

h(t) =La,8(t-T,), (2)'=0

can be estimated from the received signal vector and theknown transmitted signal, up to the resolution determined bythe signal bandwidth [5,6].

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v. FIELD TEST RESULTS

1. Experiment Platform and Field Tests

To carry out field tests, an experiment platform is established,as shown in Fig. 5. This platform supports offlineprocessing of recorded data, where the Agilent Infiniium54855A oscilloscope simultaneously and coherently acquiresfour-channel data, and the Cellnet+Hunt BLT receiverprovides the trigger signal to the oscilloscope. A Cellnet+

chip rate is assumed to be approximately 1 Mcps, andunfiltered chip waveforms are used to modulate the signalwaveform. The receiver is made up of four-element circulararray with a radius of 0.23 m, yielding approximately awavelength of spacing between two closest antennas.

1. Single-Site Receiver Performance

As shown in Fig. 1, the area being examined consists ofparallel structures representing street block, each with alength of 120 m and a width of 40 m. A meter is typicallylocated at a height of 1.5 m from the ground. The buildings'height is set to 10m. All streets are assumed to be 10m wide.Four reference meters are assumed to be available with anequal spacing of 23 m on each side of a building. Thespatio-temporal channel signatures of the orphan meter aswell as 480 reference meters located with an area ofapproximately 770m x 490m are computed for analysis.

Fig. 2 shows the distribution of the cost variable mappedinto the geographical locations. The orphan meter is markedwith a red ~. The three meters with the highest cost variableare marked with red circles. The cost variable correspondingto each reference meter is depicted in square mark with itscolor representing the value. The results indicate that theclosely spaced neighboring meters are likely to have highvalues ofcost variable whereas low values are observed at thedistant meters. Depending on the locations and channelenvironment, however, there could also be some distantmeters that also show high value of the cost variable.

The same localization procedure can be performed at otherreceiver locations. Fig. 3 shows the cost variable when thereceiver is moved to a different place. In this specific example,some symmetry is observed which yields location ambiguity.

2. Multi-Site Receiver Performance

Results obtained from the multiple receiver locations can befused to yield improved localization performance. Note thatthis is a noncoherent operation and does not require thereceivers to be operated simultaneously or synchronized. Forexample, we can simply multiply the above two sets of costvariables, shown in Figs. 2 and 3, to obtain the fused resultsas shown in Fig. 4. It is obvious that the fused resultsrepresent improved localization performance in the sense thatall reference meters with the highest cost variable values areclosely located in the same street with the orphan meter.

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Hunt transmit module used in an electrical meter issequentially moved and placed outside each house unit alongthe streets to emulate actual working meters. The oscilloscoperecords signals received at the four antennas.Elevated-feed-point mobile antenna made by AntennaSpecialists Co. was used. The antenna has a 3 dB gain at itsoperation frequency band of 902-960 MHz. The receiversystem and the array configuration are illustrated in Fig. 6. Ineach channel, a Utilinet 6DFI2-915/X22-5 band-pass filter(BPF), with a passband centered at the 915 MHz band, and aMini Circuit ZRL-1150LN+ low noise amplifier (LNA), withan operation band at 650-1400 MHz, are used. The former isused to reduce the out-of-band interference and noise, and thelatter amplifies the in-band signal with a typical gain of 32 dBwhen the operating frequency is around 915 MHz.

PC

Fig. 5. Block diagram of the measurement platform.

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2. Experimental Results

Field tests were performed in downtown Philadelphia areaand the the meter under test was moved and measured at 100locations. Using the loc~lization techniques proposed inSection III, the spatio-temporal correlation coefficient and thecost variable of the orphan meter corresponding to Kp=0.04

were obtained for each test point. Part of the experimentalresults are illustrated in Figs. 7 and 8. For clarity andconvenience, we also mapped each test point onto itsgeographic location.

Fig. 7 shows that, in a line-of-sight (LOS) environment, thereference meters that are close to the orphan meter are likelyto have high values of the cost variable, whereas the distantreference meters correspond to low values. As a result, theestimated orphan meter location, marked with a circle, ishighly accurate.

In non-LOS environments, the experimental resultspresented in Fig. 8 show that the reference meters that areclose to the orphan meter are likely to maintain high values ofthe cost variable. In this situation, however, there could besome distant reference meters that also have high values ofthe cost variable, yielding an ambiguity problem of theorphan meter locations. Such results resemble to thatobtained in the ray-tracing simulations depicted in Fig. 3. Forexample, in Fig. 8(a), one of the estimated locations of theorphan meter is accurate, but there are three other ambiguouspoints. These experimental results also validate theperformance obtained in the simulations. The ambiguityproblem ofthe estimated locations is expected to be solved byrecording data at multiple receiver locations and data fusionis performed for enhanced orphan meter localization.

VI. CONCLUSION

The problem of localizing orphan meters in an urbanenvironment has been examined in this paper. An arrayantenna based localization approach that fuses thespatio-temporal correlation coefficient and the receivedsignal power was proposed. The effectiveness of theproposed approach was first demonstrated using ray-tracingmodel based simulation results. It was also demonstrated thatthe orphan meter localization performance can further beimproved when data collected at multiple sites areincorporated. An experimental platform was established andfield tests were carried out in downtown Philadelphia area.The field measurements have demonstrated promisingperformance of the proposed orphan meter localizationapproach in an urban environment. The measurement resultsfurther show that, when there is a line-of-sight (LOS)between the orphan meter and the receiver antennas, theproposed techniques provide reliable orphan meterlocalization performance. When the LOS does not exist, theproposed techniques still provide reasonable localizationperformance, but the results are suffered from low signalstrength as well as stronger presence ofmultipath propagationeffects. In this case, an orphan meter may also show highcorrelation with its neighboring meters as well as someremote meters in other streets. It is considered that the fusionof measurement data collected at multiple receiver locationsmay mitigate such ambiguity and improve the localizationperformance. Additionally, during the field experiments,

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significant variation in the signal strength was observed ineach antenna. This is a natural result of time-varying fadingdue to reflection, diffraction, and scattering from variousstationary and moving objectives. Thus, when multiplemeasurements are available for each meter location,improved orphan meter localization performance would beachieved.

[8] G. Liang and H. Bertoni, "A new approach to 3-D ray tracingfor propagation prediction in cities," IEEE Trans. Antennasand Propagation, vol. 46, no. 6, pp. 853-863, June 1998.

[9] H. M. Ei-Sallabi, "Modeling and characterization of urbanradio channels for mobile communications," Report S261,Ph.D. thesis, July 2003, Helsinki University of Technology.

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REFERENCES

[1] G. Sun, 1. Chen, W. Guo, and K. 1. R. Liu, "Signal processingtechniques in network-aid positioning," IEEE SignalProcessing Mag., vol. 22, no. 4, pp. 12-23, July 2005.

[2] O. Hilsenrath, M. Wax, S. Jayaraman, A. Bar, M. Malkin, R.Rajapakse, V. Radionov, and Y. Meng, "Wireless locationdetennination using spatial signature infonnation," WorldIntellectual Property Organization Publication NumberW0/20001034799, June 15, 2000.

[3] S. Jayaraman, M. Wax, and O. Hilsenrath, "Calibration tablegeneration for wireless location detennination," United StatesPatent 6,101,390, Aug. 8,2000.

[4] D. J. Dupray, "Wireless location using multiple locationestimators," United States Patent 6,249,252, June 19, 2001.

[5] G. D. Durgin, Space-Time Wireless Channels. Upper SaddleRiver, NJ: Prentice-Hall, 2003.

[6] K. Yang, Y. Zhang and T. Ohira, "Array configuration designfor space-time adaptive processing systems", IEEE SensorArray and Multichannel Signal Processing Workshop, Rosslyn,VA, Aug. 2002.

[7] P. Kontkanen, P. Myllymaki, T. Roos, H. Tirri, K. Valtonen,and H.Wettig, "Probabilistic methods for location estimation inwireless networks," Chapter 11 in: Emerging Location AwareBroadband Wireless Ad Hoc Networks, edited by R. Ganesh, S.Kota, K. Pahlavan and R. Agusti. Kluwer, 2004.

ACKNOWLEDGEMENT

The authors would like to thank Dr. Moeness Amin for hisvaluable inputs and discussions.

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