WPI Precision Personnel Location System:Rapid Deployment Antenna System and Sensor

Fusion for 3D Precision LocationA. Cavanaugh, M. Lowe, D. Cyganski, R. J. Duckworth

Worcester Polytechnic Institute, Worcester, Mass.

BIOGRAPHY

Mr. Andrew Cavanaugh is a M.S. candidate in Electricaland Computer Engineering at WPI. Since completing his B.S.EE degree at The University of Rhode Island in 2008, he hasserved as a research assistant in the WPI Precision PersonnelLocation Laboratory. His research is focused on improvingthe accuracy of the WPI Precision Personnel Location system,using Bayesian methods to fuse diverse sources of information.

Mr. Matthew Lowe has been attending WPI since 2005,and is currently working towards his M.S. degree in Electricaland Computer Engineering in the Precision Personnel LocationLaboratory. Mr. Lowe has done funded research in the areasof applied mathematics, signal processing, and is currentlyfocused on developing the tools necessary to efficiently fuseinformation from pressure, inertial, magnetic, and other sen-sors for use in location solutions.

Dr. David Cyganski is Professor of Electrical and ComputerEngineering at WPI where he performs research and teacheslinear and non-linear multidimensional signal processing, com-munications and computer networks. He is an active researcherin the areas of radar imaging, automatic target recognition,machine vision, and protocols for computer networks. Heis coauthor of the book Information Technology: Inside andOutside. Prior to joining the faculty at WPI, he was an MTSat Bell Laboratories and has since held the administrativepositions of Vice President of Information Systems and ViceProvost at WPI. He is a member of IEEE and a member ofION.

Dr. R. James Duckworth is an Associate Professor in theElectrical and Computer Engineering department at WPI. Heobtained his Ph.D. in parallel processing from the Universityof Nottingham in England. He joined WPI in 1987. Duck-worth teaches undergraduate and graduate courses in computerengineering focusing on microprocessor and digital systemdesign, including using VHDL and Verilog for synthesis andmodeling. His main research area is embedded system design.He is a fellow of the BCS, a senior member of the IEEE, amember of ION and the IEE.

ABSTRACT

An RF-based system is being developed for trackingof first responders and other personnel in indoor environ-ments. The system assumes no existing infrastructure, nopre-characterization of the area of operation and is designed

for spectral compliance and rapid deployment. The RF 3Dlocation system, based on a recently developed multicarriersignal fusion algorithm, has previously demonstrated sub-meter positioning accuracy of a transmitter, even in difficultindoor environments with high multi-path, with all receiversplaced outside the building. However, the current version ofthe system requires a set of 12 to 16 antennas be distributedoutside three to four sides of the building in order to obtainthe necessary diversity of information necessary to preciselyresolve 3D location. This is undesirable as a practical applica-tion of a Precision Personnel Location (PPL) system requiresrapid deployment of any antenna nodes at a time when almostall manpower at the fire scene is dedicated to rapid and safeentry into the building and fire suppression activities. Thus,the act of antenna deployment must be extremely fast, easyand forgiving. This paper reports on the development andevaluation outcomes of an approach that permits 3D locationbased upon deployment of two antenna arrays on any one faceof the building.

The positioning performance of the PPL system with thenew antenna system will be compared to performance from theprevious incarnation of the PPL system with widely distributedantennas. In the case of the new antenna system, outcomeswill be shown for both the cases of Bayesian inference basedupon additional sensor information and without this sensorinformation. The previously described[1], [2], [3], [4], [5] WPIPPL system has demonstrated 3D accuracies of better than 1m in indoor tests. We will show that the new version of thesystem does not perform at the level of precision as that witha diverse antenna distribution, however, at a level sufficient tostill significantly aid in search and rescue missions.

INTRODUCTION:WPI PRECISION PERSONNEL LOCATOR SYSTEM

The Worcester Polytechnic Institute(WPI) Precision Person-nel Location(PPL) system, being developed at WPI, is an RFbased system for locating first responders inside of a building.More specifically, the goal is to have a system that is accuratewithin +/- one meter, portable, rugged, and rapidly deployablewith no site preparation or on-site calibration procedures[2].In recent years, the system has been expanded to consider fire,police, military, and even robotic vehicles in both urban andwilderness settings.

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Figure 1 is an artist’s rendition of the PPL concept. Thelocator units worn by personnel emit a Multi-Carrier Wide-Band(MC-WB) signal that is received by multiple antennaslocated outdoors, around the building at known positions.The receiving antennas are connected to our transceiver units,which send the spectrum of the received signal to a centralcomputer through a wireless network. The computer processesthe data from all of the received antennas, and calculatesthe location of the firefighter based on the time-difference-of-arrival(TDOA) like algorithm involving a signal fusionapproach.

Fig. 1. PPL concept illustration.

The goal of the WPI precision personnel location project isdevelopment of a system appropriate for use by first respondersincorporating no pre-installed infrastructure, rapid deployabil-ity, medium (not ultra-wide) bandwidth, flexible and spectrallycompliant signals, and low cost personnel tags[2]. Previouspapers have described: development and performance of newsignal processing and location techniques for the ameliorationof the extreme multi-path conditions such as found in typicalcommercial and industrial structures[1], [2], [3], [4], [5], whichusually frustrate all attempts to achieve precision location; in-corporation of physiological monitoring sensors and real timedisplay; automated solution of outdoor sensor positions forrapid deployment; synchronization technology for wirelesslyconnected transceiver nodes[6]. This paper describes a nextgeneration of the PPL fast-deployment antenna system andrequisite sensor and algorithmic support for solution of 3Dindoor location from antennas located on only a single side ofa building. An approach has been developed in which signalsare captured from antennas mounted on two extension laddersthat can be transported easily by fire truck and then quicklyextended and leaned against a building face. However, due

to the poor geometry this presents for 3D precision locationwithin the context of the present TDOA-like multicarriersolution technique and especially when challenged withinthe high multipath indoor environment, large errors in thedirection perpendicular to the antenna plane are experienced.To ameliorate this degradation of location precision, a new datafusion system has been developed that uses Bayesian inferenceto introduce additional, not necessarily precise or low varianceinformation, to obtain a refined global solution. The flexibleBayesian inference engine allows additional information to beobtained from sources as diverse as low-cost inertial sensorsand simple radio ranging sensors.

The following sections will describe the current WPI lo-cation algorithm(σART), pure RF signal based tests of boththe unrestricted and rapid deployments, additional sensor in-formation introduced in this paper, and the results of the rapiddeployment system information fused with these additionalsources of information.

THE WPI PPL SYSTEM

The Precision Personnel Location system currently employsthe σART algorithm to solve for the position of our locatorunit using multi carrier complex amplitude data from receivingantennas. Unlike traditional approaches, which use multi lat-eralization to determine a location solution. σART considersdata from all receiving antennas to solve for a position[7], [8].

In principal our Multi-Carrier Wide-Band(MC-WB) signaldivides our bandwidth of operation B Hz into a collectionof N discrete, unmodulated sub-carriers, spaced at B/N Hz[2]. In practice, hardware limitations keep us from placingsub-carriers near the upper and lower limits of our allottedbandwidth. Typically we employ approximately 100 sub-carriers spaced over a 150 MHz bandwidth. The carriers arespaced evenly, except where deletions have been made to avoidinterfering with existing services in the range from 608 to614 MHz, over our FCC allocated 550-700 MHz experimentalband.

At a particular antenna, the signal measured at the output ofa receiver may be written in the frequency domain, sampledat the transmitted sub-carrier frequencies ωk = 2πfk,

X(ωk) ·H(ωk) ·∑i

aie−jωkti (1)

where H(ωk) is the transfer function of the receiver andX(ωk) is the transmitted signal. Assuming these two termsare known, what remains is the channel response containinginformation about the direct and multipath signal components,each of which is a sinusoid:

V (ωk) =∑i

aie−jωkti (2)

where ai is the amplitude for the i-th signal and ti determinesthe periodicity of each signal with respect to ωk and is thepropagation delay of the i-th signal. The σART algorithmassembles the measured channel responses from all antennas

ION ITM 2010, Session A4: Urban Indoor Navigation Technology, January 25-27, 2010, San Diego, CA 2

into a signal matrix S whose columns are the channel response(2) measured at each antenna,

S =

V1(ω1) . . . Vn(ω1)...

. . ....

V1(ωk) . . . Vn(ωk)

= D +M. (3)

Considering direct-path signal energy, the elements of Dcorrespond to phase changes due to line-of-sight propagationat the speed of light:

D = ane−jωktn =

a1e−jω1t1 . . . ane

−jω1tn

.... . .

...a1e−jωkt1 . . . ane

−jωktn

(4)

Given knowledge of the antenna positions ~pn and frequenciesωk, the received signal may be re-phased for a hypotheticalsource location ~x. This is done by calculating the appropriatedelays from every point in the search space to each antenna;these pre-computed delays are then removed from the receiveddata when computing the metric on the corresponding scangrid point:

D(~x) = ane−jωktn · ejωk‖~pn−~x‖2/c (5)

where c is the speed of light. Thus the matrix of receivedsignals with propagation delays tn from a transmitter, re-phased to ~x can be written as

D(~x) = ane−jωk(tn−∆t̂n) (6)

illustrating how the re-phasing adjusts the time delay contentof the received signal. At the true transmitter location ~x?(subscripted with ?), the delays removed by re-phasing arethe same as the delays imparted by line-of-sight propagation,and the column vectors in D become linearly dependent,

S(~x?) = D(~x?) +M(~x?)

= ane−jθn +M(~x?)

(7)

and thus D(~x?) is rank one. As the re-phasing operationpreserves total signal energy (the Frobenius norm of S), therank structure, as described by the singular values (obtainedvia singular value decomposition [9]) of S(~x)

UHS(~x)V = Σ = diag(σ1, σ2, . . . , σn) (8)

reflects the distribution of received signal energy amongthe linearly independent basis vectors of S(~x). Assumingthat the receivers are not overwhelmed by multipath signalenergy, the first singular value σ1 measures the strength oflinear dependence between the re-phased direct path signalcomponents and is maximized at the transmitter’s location.This is the σART metric or likelihood function[7] an exampleof which is shown in Fig. 2 for the case of an unrestricteddeployment based test at the site used in the current work anddescribed below. The location of a firefighter is determinedby exhaustively scanning the entire search space and thenselecting the point where the σART likelihood function ismaximized.

Fig. 2. σART likelihood function from the Unrestricted Deployment RFTest, March 2008.

ORIGINAL SIGNAL/ALGORITHM TEST RESULTS

The test site chosen for our new rapid deployment methodwas the WPI Campus Ministry building. This site consistsof a three story wooden house, on a typical urban houselot. To make the comparison between the previous and newdeployment methods more meaningful, the new results willbe compared with an older test performed at this same site.The test conditions are not identical; logistical constraints,such as furniture reconfiguration, off-limits rooms, and timelimitations forced us to create a different set of truth points inthe rapid deployment test. Although the points are different, weconsider the overall test conditions to be equivalent, since bothsets of points represent a large number of diverse locationsthroughout the house, which is assumed to have the same grossRF characteristics in both cases.

Unrestricted Antenna Deployment

In March of 2008, the PPL system was tested at the WPICampus Ministry building. Antennas were placed on all sidesof the building with various heights, as shown in Figure 3.The spatial diversity of the antennas in this configurationimproves the solution by providing a sensor geometry withlow geometric dilution of precision.

Figure 4 shows the error vectors from the unrestricted-deployment test. The XY mean absolute error is 0.93 m whilethe Z mean absolute error is 0.95 m.

Rapid Deployment

In July of 2009, the PPL system was again tested at theWPI Campus Ministry building. This time, flat panel receivingantennas were attached to aluminum extension ladders thatwere quickly1 moved into place on the right side of the house.In this case all of the antennas were approximately coplanarand parallel with the side of the house. For nearly coplanarantennas the position solution suffers a geometric dilution of

1Each ladder took two students less than 1 minute to deploy.

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Fig. 3. Campus Ministry with unrestricted deployment of PPL antennas,March 2008.

Fig. 4. Results of the Unrestricted Deployment RF Test, March 2008.

precision perpendicular to the plane. This increased ambiguityis seen as pronounced ambiguity in the X direction of theσART likelihood function, seen in Fig. 6, resulting in thepositioning errors seen in Fig. 7 which depicts the error vectorsfrom the rapid-deployment test. Owing to this large GDOP,the XY mean absolute error has degraded to 6.43 m whilethe mean absolute Z error is now 1.51 m. If we double thenumber of ladder-arrays employed, we find that the XY meanabsolute error improves to 3.95 m while the mean absolute Zerror becomes 0.82 m.

ADDITIONAL SOURCES OF INFORMATION

Complementary RF-Ranging

In the original system the mobile unit had transmit-onlycapability; for the new system under test, the mobile units havebeen given transceiver capability which was used to collectTOA information. The range between the mobile locator andeach receive antenna is computed using a round-trip TOAbased method. This range information is easily corrupted byRF propagation velocity changes that occur when the signalpasses through walls, and other obstructions and/or is reflected.

Fig. 5. Campus Ministry with two ladder rapid deployment configuration ofPPL antennas, July 2009.

Fig. 6. σART likelihood function from the Rapid Deployment RF Test, July2009.

Fig. 7. Results of the Rapid Deployment RF Test, July 2009.

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TABLE IPRESSURE BIAS AND SCALE FACTORS.

Unit Temp Constant (PA/·C) Bias1 -2.34 61.182 -2.55 2.463 -2.34 161.204 -4.22 125.29

To use these ranges to our advantage, we assigned a likelihoodfunction to each of them using the estimated range as the meanof the likelihood function. This likelihood function is then usedin the fusion algorithm to be described below.

Barometric Sensor

To obtain an additional independent source of height in-formation, we measured the air pressure difference betweenour locator and a reference unit outside, at ground level. Theambient pressure can change in time frames on the order ofminutes from natural weather conditions, and wind gusts canhave effects that cause changes on the order of seconds induration. The differential pair is used to cancel the effectsof these pressure changes. To account for pressure changesin the building that result from HVAC equipment wouldrequire another reference unit indoors. This is not a technicalchallenge, but logistical constraints may make this secondreference infeasible.

The barometric sensors are also significantly affected bytemperature, which presents a problem when one unit isindoors and the other is outside. We calibrated the pressuresensors with experimental data. The design of the currentlocator units themselves introduce some error because thetemperature sensor is not exactly co-located with the pressuresensor; this will be corrected with our next hardware version.

Every pressure sensor is assumed to have a constant bias aswell as a linear temperature dependence. To remove these ef-fects we conducted several experiments in which we measuredthe units at constant heights under varying temperatures. Thisdata was then processed to estimate the required correctionparameters. The resulting bias and temperature constants areshown for four units in Table I.

RAPID DEPLOYMENT ANTENNAS WITH BAYESIAN FUSION

The Bayesian algorithm that we developed allows multipledata sets to be fused together to generate a single loca-tion solution. This algorithm considers information from theσART likelihood function, barometric sensors, temperaturesensors, and RF based TOA ranges. This algorithm allows usto enhance our accuracy in the challenging rapid deploymentconfiguration. The Bayesian solution is obtained from an a pri-ori distribution, which in our case is based on the assumptionthat the correct location is uniformly distributed over a searchspace corresponding to a 3-D region slightly larger that thatwhich encloses the house.

The likelihood that the correct location is at a given pointis:

TABLE IILOCATION TESTING MEAN ABSOLUTE ERRORS

Test Type Total XY Error Z ErrorUnrestricted Deployment 0.93 m 0.95 mRapid Deployment 2 6.43 m 1.51 mRapid Deployment 4 3.95 m 0.82 mFused Results 2 3.07 m 0.45 mFused Results 4 2.49 m 0.45 m

maxx,y,z

P (position|MσART ,MTOA,MBarometric) (9)

Where MσART ,MTOA,MBarometric denote the measureddata. The likelihood metric is evaluated at every point on thescan grid, and the location estimate is given as the point wherethe metric is maximized. We found that the X,Y solutionwas relatively insensitive to values of Z assumed during thelikelihood optimization process when these were restricted tothe range of values consistent with barometric height estimatesfor a given floor. Thus, to increase solution speed, we scannedonly the plane that was located at the height correspondingto the barometric measurement. This effectively changed theassumed prior distribution into a uniform distribution overthe truncated horizontal plane consistent with the barometricheight estimate, rather than a 3D space. Applying the remain-ing RF data to calculate the metric for every point on theplane gave us a 2D likelihood function whose maximum wasour location solution.

The fused results, shown in Fig. 8, demonstrated an im-proved XY mean absolute error of 2.49 m in the 2 laddercase, and 3.07 m in the 4 ladder case. The Z error was 0.47m in both cases. In both cases there is still noticeable GDOPin the X direction. The results of this evaluation compared tothat of the previously described configurations and algorithmsare shown in Table II.

CONCLUSIONS

The importance of precision indoor positioning with nopre-installed infrastructure is well understood, and this pa-per presents significant progress toward that goal. Previ-ously this research group constructed a system using RF-only technology with diversely distributed antennas whichsuccessfully demonstrated the concept of the new positioningapproach while realizing that deployment requirements of thefire fighting environment and severe deployment restrictionswould additionally need to be addressed. This paper explainedthese deployment requirements, the problem posed by them,the solution implemented, and the performance of a systemenhanced by fusion of information from three sources.

While the Rapid Deployment/Fusion outcomes are lessprecise than the unrestricted deployment approach, the flooris always correctly identified (with half meter precision). Thefloor occupied by the subject to be rescued is considered, byfirefighters, to be the single most valuable piece of informationto promote timely rescues. The Y direction information hasapproximately 2 meter maximum error (4 ladder case), which

ION ITM 2010, Session A4: Urban Indoor Navigation Technology, January 25-27, 2010, San Diego, CA 5

Fig. 8. Fused Results shown with two ladders(top) and 4 ladders(bottom).

can be probed with classic swinging firefighter’s “pike” orax handle. The larger (2-4 meter) X direction errors can beaccommodated with rapid linear search, which is faster thansearching the circular area swept by this same distance. Thusthis paper documents what must be considered a successfuldemonstration of firefighter location technology with a rapiddeployment system.

The new hardware (with mobile transceiver capability)which enabled the tests described in this paper also unlocksother opportunities that surpass in potential the basic fusionsystem just described. We are continuing active research aimedat further improving the precision of X,Y and Z estimates inthe rapid deployment configuration.

ACKNOWLEDGMENTS

The support of the Department of Homeland Security, U.S.Army Natick Soldier Research and Engineering Center andthe Federal Emergency Management Agency is gratefullyacknowledged.

REFERENCES

[1] D. Cyganski, J. Orr, R. Angilly, and B. Woodacre, “Performance limitsand field tests of a precision indoor positioning system using a multi-carrier approach,” in Proceceedings of the Institute of Navigation NationalTechnical Meeting, (San Diego, California), January 2005.

[2] D. Cyganski, J. A. Orr, and W. R. Michalson, “Performance of aprecision indoor positioning system using a multi-carrier approach,” inProceceedings of the Institute of Navigation National Technical Meeting,(San Diego, California), January 2004.

[3] D. Cyganski, J. Orr, D. Breen Jr., and B. Woodacre, “Error analysis ofa precision indoor positioning system,” in Proceceedings of the Instituteof Navigation National Annual Meeting, (Dayton, Ohio), June 2004.

[4] D. Cyganski, J. Orr, and W. R. Michalson, “A multi-carrier technique forprecision geolocation for indoor/multipath environments,” in Proceceed-ings of ION GPS/GNSS, (Portland, Oregon), September 2003.

[5] D. Cyganski, R. J. Duckworth, S. Makarov, W. Michalson, J. Orr,V. Amendolare, J. Coyne, H. Daempfling, S. Kulkarni, H. Parikh, andB. Woodacre, “WPI Precision Personnel Locator System: Demonstrationsand RF Design Improvements,” in Proceedings of the Institute of Navi-gation Annual Meeting, (Cambridge, Massachusetts), April 2007.

[6] V. Amendolare, D. Cyganski, and Duckworth, “WPI precision personnellocator system: Synchronization of wireless transceiver units,” 2009 JointNavigation Conference, Orlando, Florida, June 2009.

[7] B. Woodacre, D. Cyganski, J. Duckworth, and V. Amendolare, “WPIprecision personnel locator system: Automatic antenna geometry esti-mation using a robust multilateralization technique,” in Proceedings ofthe Institute of Navigation International Technical Meeting, (Anaheim,California), January 2009.

[8] V. T. Amendolare, “Synchronization in an indoor precision locationsystem,” Master’s thesis, Worcester Polytechnic Institute, May 2007.

[9] G. H. Golub and C. F. Van Loan, Matrix Computations. Johns HopkinsUniv. Press, 3rd ed., 1996.

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