wpi precision personnel locator system: inertial

WPI Precision Personnel Locator System:Inertial Navigation Supplementation

V. Amendolare, D. Cyganski, R. J. Duckworth, S. Makarov,J. Coyne, H. Daempfling, B. Woodacre

Worcester Polytechnic Institute, Worcester, Mass., USA

BIOGRAPHY

Mr. Vincent Amendolare is a Ph.D. candidate in Elec-trical and Computer Engineering at WPI. Since complet-ing his BS and MS degrees at WPI in 2006 and 2007, hehas served as a research assistant in the WPI ConvergentTechnology Center sponsored by the NIJ/DOJ on thetopic of precision personnel location. His MS thesis de-termined techniques to attain synchronization necessaryfor the WPI precision personnel location system, and hisPh.D. research concerns improving the performance ofthis system with inertial navigation supplementation.

Dr. David Cyganski is professor of Electrical andComputer Engineering at WPI where he performs re-search and teaches in the areas of linear and non-linearmultidimensional signal processing, communications andcomputer networks, and supervises the WPI ConvergentTechnology Center. He is an active researcher in theareas of radar imaging, automatic target recognition,machine vision and protocols for computer networks. Heis coauthor of the book Information Technology: Insideand Outside. Prior to joining the faculty at WPI he wasan MTS at Bell Laboratories and has since held theadministrative positions of Vice President of InformationSystems and Vice Provost at WPI.

Dr. R. James Duckworth is an Associate Professorin the Electrical and Computer Engineering departmentat WPI. He obtained his Ph.D. in parallel processingfrom the University of Nottingham in England. Hejoined WPI in 1987. Duckworth teaches undergraduateand graduate course in computer engineering focusingon microprocessor and digital system design, includingusing VHDL and Verilog for synthesis and modeling.His main research area is embedded system design. Heis a senior member of the IEEE, and a member of theION, IEE, and BCS and is a Chartered Engineer of theEngineering Council of the UK.

ABSTRACT

This paper describes the latest developments in theWorcester Polytechnic Institute (WPI) Precision Per-sonnel Location (PPL) system. An RF-based systemwith inertial sensor supplementation is being developedfor tracking of first responders and other personnel inindoor environments. The system assumes no existinginfrastructure, no pre-characterization of the area ofoperation and is designed for spectral compliance andrapid deployment. The RF 3D location system has pre-viously demonstrated sub-meter positioning accuracy ofa transmitter even in difficult indoor environments withhigh multi-path with all receivers outside the building.However, accuracy can be severely degraded at particularlocations within a building such as when a large andnearby metal panel blocks the direct path from themobile transmitter to several receiving antennas. Thispaper describes the performance of our existing RFlocation system’s raw position estimate performance andhow these raw position estimates may be filtered withmotion constraints via the Kalman filter for improvedaccuracy. The paper goes on to investigate the useof inertial sensors to supplement the information fromour RF position estimates, and how this informationis fused together to provide improved overall systemperformance.

I. INTRODUCTION

Previous papers by the authors [1-6] have describedthe general system architecture, precision location ap-proach, hardware design challenges, signal structure,and experimental results paralleling the development andrefinement of the system’s hardware realization. Fig. 1is an artist’s rendition of the PPL concept. The goalof the system is to provide a robust real-time trackingsolution that requires no pre-installed infrastructure. Tobe tracked, firefighters and other emergency personneleach carry a transmitter emitting a multi-carrier wide-band (MC-WB) signal [4], which is sensed at receiving

Fig. 1. PPL concept illustration

TABLE ILOCATION TESTING MEAN ABSOLUTE ERRORS

Test Location Error BandwidthKaven Hall 0.37 m 60 MHzAtwater Kent 1.08 m 60 MHzCampus Ministry 1st fl. 0.59 m 60 MHzCampus Ministry 2nd fl. 0.72 m 60 MHzCampus Ministry 1st fl. 0.49 m 150 MHzCampus Ministry 2nd fl. 0.46 m 150 MHz

stations fixed upon emergency response vehicles. Uponarrival, the receiving stations form an ad-hoc networkand establish a local coordinate system.

Given the receiving stations’ positions within the localcoordinate system, the signals collected at each stationare transferred to a central location where the relativepositions of the transmitters within the coordinate sys-tem are estimated. These estimates are then conveyedto command-and-control software in the hands of theincident commander. Accumulation of track informationfor many transmitters has the effect of providing theincident commander with valuable information on build-ing layout to assist with exit guidance or search andrescue operations. Efforts are also underway to integrateadditional information into the incident commander’sdisplay to enable monitoring of personnel physiologicalstatus and ambient environmental conditions.

0 5 10

−5

0

5

10

X [m]

Y [

m]

Tx

Rx

Error

Fig. 2. Campus Ministry: Mean Absolute Error - 0.49m

II. EXPERIMENTAL LOCATION TESTS

Publications [2] by the authors have presented locationtesting results from a 60 MHz RF system and from a new150 MHz wideband system [1]. The results of these testsare summarized here in Table I; all results involve 2Dlocation of a transmitter inside the building by anten-nas placed outside of it. The Kaven Hall test locationcomprises brick and steel-beam construction and housesa geotechnical lab on the WPI campus; the AtwaterKent location was a larger scale test centered aroundthe wing of a large brick building, passing throughsteel-studded walls and under metal-corrugated ceilings;and the Campus Ministry location is a typical wood-construction three-story residential structure completewith furniture and metal appliances in the kitchen. Theerrors in Table I provide a reference for the generalaccuracy of our system.

Fig. 2 displays a result from the Campus Ministrytest on the first floor with 150 MHz of bandwidth.The circles represent our receive antenna (Rx) locationsoutside the house, while the squares represent severaltransmitter (Tx) locations at which the transmitter wastruly positioned. At each location the transmitter wasstationary and it’s position was estimated using thereceived RF signals at the receive antennas. The plotshows error vectors which point from the true locationof the transmitter to the estimated position. We see thatin many cases the error is quite small, however somelocations are more challenged due to direct path blockageand reflections.

The positioning algorithm used in our system iscomputationally intensive and takes approximately 0.3

Position Location And Navigation SymposiumSession C2: Indoor/Personal Navigation

2 May 2008, Monterey, CA

2 3 4 5 6 7 8

0

2

4

6

8

10

X [m]

Y [

m]

Raw Est.

Truth Loc.

Stop Loc.

Approx. Path

Fig. 3. Campus Ministry - Continuous Capture

seconds to compute a position estimate with our currentmicroprocessor based hardware. It is possible howeverfor us to capture signal data at a faster rate continuouslyand post-process it. During a recent test at the CampusMinistry we performed a continuous data collection ata rate of approximately 30 captures per second whilethe transmitter was moved around the first floor of thehouse. The result of this data set is shown in Fig. 3.The transmitter was moved through several surveyedtruth locations depicted by blue circles. The blue lineis a spline curve passing through the truth locations thatapproximately describes the true path of the transmitter.Note that the transmitter was brought to a completestop at the stop locations (depicted by blue squares).This result provides great insight into how our positionestimates change as the transmitter is moved. We see thatgenerally it follows the true path. There are apparentlytwo types of errors. There are highly localized largedeviations from the truth path; fortunately these are oftenonly present for isolated samples so they can be moreeasily rejected in a tracking implementation. There are

also consistent deviations from the true path that last forseveral samples. Fortunately these are relatively small(less than 1 meter) and return shortly to the truth path.This same data set will be used throughout this paperfor comparison of different tracking approaches.

Based upon motion constraints of the person we wishto track, these raw solutions can be filtered to obtain amore precise tracking estimate.

III. TRACKING WITH KALMAN FILTER

Our system can be modeled as a discrete time linearsystem with state variables in a vector xk, where k isa discrete time index. The states in our system are theposition, velocity and acceleration column vectors of thetracked personnel denoted p, v, a respectively.

x =

pva

(1)

The value of xk+1, the next state is a function of thecurrent states and a process noise term wk. This processnoise term is a random vector, which models the changesin states of the tracked personnel as a random process.

xk+1 = Axk + wk, (2)

where the A is the state transition matrix that describesthe dynamics of the state variables. For our case

A =

I3 τI3 03

03 I3 τI3

03 03 I3

, (3)

where τ is the elapsed time between k and k + 1.This state transition matrix derived from the fact thatvelocity is the derivative of position, and acceleration isthe derivative of velocity.

Our system provides raw position estimates which canbe represented as

yk = Cxk + vk (4)

where C is called the observation matrix and v isthe measurement noise. For our case since our systemmeasures position C simply masks out the velocity andacceleratoin

C =[

I3 03 03

]. (5)

The errors in our raw position measurements are mod-eled by v.

This system model we have developed is the systemmodel for a discrete time Kalman filter [7]. This fil-tering technique is a common solution to the problem



of tracking with noisy estimates. The Kalman filter isoptimal for estimating the parameters of linear systemswith Gaussian noise. The noise in our system may not betruly Gaussian, but it may be approximated as such. Forour system we assume that the process and measurementnoise both have zero mean. The covariance of each ofthese noise sources must be known.

The Kalman filter algorithm has two phases, predictionand update [8]. The prediction phase makes a predictionof the state variables from the state estimate xk−1 of theprevious time step.

x−k = Axk−1 (6)

The Kalman filter not only estimates the state variables,but it also keeps track of the covariance of the statevariables. The Kalman filter treats the state variables asa multivariate Gaussian with a covariance matrix Pk.The prediction phase also involves the prediction ofthe covariance matrix based upon the estimate from theprevious time step Pk−1.

P−k = APk−1AT + Q (7)

where Q is the covariance matrix of the process noise.The second phase of the Kalman filter algorithm is the

update phase [8]. First the Kalman gain Kk is computed.

Kk = P−k C(CP−k CT + R

)−1(8)

where R is the covariance matrix of the measurementnoise. The Kalman gain takes an important role in thenext step, where it acts as a relative weight between thepredicted state x−k and the actual measurement zk toproduce the filtered measurement xk.

xk = x−k + Kk

(zk −Cx−k

)(9)

Similarly the covariance matrix Pk is estimated.

Pk = (I−KkC) P−k (10)

This process is repeated for each new measurement.In our system we use the discrete time Kalman filter

to improve our tracking performance based on estimatesof our position estimate noise variance and personnelmotion process variance. This method of tracking basedon position estimates will be denoted as the “positiononly” Kalman filter algorithm. The primary differencebetween our implementation and the classic Kalmanfilter described above is outlier rejection logic intendedto ignore large, isolated errors. Fig. 4 shows a set ofraw position estimates from our RF system as well asthe filtered version of this data. The sample rate for this

2 3 4 5 6 7 8 9

0

1

2

3

4

5

6

7

8

9

10

X [m]

Y [

m]

Raw Est.: 0.35m

Filt. Est.: 0.16m

Truth Loc.

Stop Loc.

Approx. Path

Fig. 4. Raw estimates and filtered estimates

data was reduced to approximately 3 updates per secondto replicate realistic timing for our real-time positioninghardware.

The error relative to the approximate truth path wascalculated before filtering to be 0.35 meters and afterfiltering to be 0.16 meters. This is a significant improve-ment and yields our desired accuracy of better than onefoot. We are still interested in techniques for further im-provement because we expect our raw position estimatesto be worse in more challenging multipath environments,hence the next pursuit of inertial supplementation to bediscussed below.

IV. INERTIAL SUPPLEMENTATION

This paper investigates the potential benefit of supple-menting our existing RF based positioning system withinertial sensors to improve overall performance. Recentwork has been aimed towards proof of this concept.

We have been working with the nIMU (nano InertialMeasurement Unit) created by Memsense. This unitcontains a three axis accelerometer, three axis gyroscopeand three axis magnetometer with measurements at a rateof 150Hz in a small form factor [9]. For our testing thisunit was boxed and attached to a cart along with our RF



Fig. 5. Locator Cart

locator and a laptop for nIMU data logging, as shownin Fig. 5.

One significant issue with inertial measurement unitsis calibration. While inertial measurements are gener-ally considered to have Gaussian noise, they also mayhave deterministic errors. The most prominent of theseare biases and incorrect scale factors. Our device wascharacterized by the manufacturer to determine thesevalues in order to calibrate the device. Temperature canaffect these values as well, and although the nIMUfeatures internal temperature compensation [9], its biasesand scaling values can still change considerably. Ideallythe device would be fully calibrated immediately beforetracking, however an elaborate calibration procedure isnot practical for our system in the field. We employ asimple calibration procedure, holding the device station-ary for a few seconds, before tracking begins. This allowsus to determine any gyroscope bias and also estimate theinitial pitch and roll of the device from the accelerometermeasurements.

Before an attempt was made to integrate inertial andRF position estimates we observed how well we couldtrack the unit with inertial data alone. The algorithm usedto do this is based upon the Kalman filter implementationdescribed previously.

First the orientation of the cart (which we will callthe cart frame) relative to our system’s local coordinatesystem must be tracked. This relative orientation is arotation in three dimensions, which can be described

0 10 20 30 40 50−100

0

100

Ang. R

ate

[deg/s

]

Time [s]

yaw

pitch

roll

0 10 20 30 40 50−180

−90

0

90

180

Time [s]

Ang. [d

eg]

yaw

pitch

roll

Fig. 6. Orientation Tracking

by an orthogonal rotation matrix Rlc. This matrix will

map any vector ac in the cart coordinate system to it’sequivalent vector in the local coordinate system al.

al = Rlcac (11)

This rotation matrix can parameterized by three Eulerangles. For our implementation we use the aeronauticsconvention of yaw, pitch and roll. Which refer to positiverotations about the z, y and x axis sequentially [10].

As the orientation of the cart changes over time, thisrotation matrix changes. The derivative of this matrix canbe expressed as

Rlc(t) = Rl

c(t)Ωlc (12)

where Ωlc is the skew symmetric representation of the

three angular rates(ωl

cxωl

cyωl

cz

), the angular rates about

the three axes of the cart measured by the gyroscopes[12].

Ωlc =

0 −ωlcz

ωlcy

ωlcz

0 −ωlcx

−ωlcy

ωlcx

0

(13)

In discrete time, the orientation Rlc,k can be estimated

from the previous estimate and the current gyroscopemeasurement.

Rlc,k = Rl

c,k−1

(2I3 + Ωl

c,kτ) (

2I3 −Ωlc,kτ

)−1[12]

(14)This is currently being done with our three axis

gyroscope measurements. This is certainly not a validlong term solution as it is tracking based on angularrate, open loop; however for the short data captures wehave been working with thus far (1-2 minutes) it hasproven to be quite accurate. Fig. 6 shows the orientation



tracking for the same data set we have been treating (inFig. 3 and Fig. 4).

As the cart was wheeled about the floor, it remainedflat on the floor for the most part, thus its pitch androll remained fairly constant. It’s yaw however variedconsiderably as shown in Fig. 6. There was no wayfor us to measure our true orientation over time forcomparison with our estimated orientation. We will soonobserve however that using this orientation informationfor inertial tracking can yield quite accurate results,implying that our orientation is being well tracked.

The accelerometers in the nIMU measure the motioninduced acceleration of the device summed with theacceleration of gravity. Our local coordinate system ischosen such that gravity is in the −zl direction. Theacceleration due to gravity can be represented in the localcoordinate system as

gl =

00−g

(15)

where g is the magnitude of acceleration due to gravity(approximately 9.80665 m

s2 ). The measurement of theaccelerometer in the cart frame fc is

fc = RlcT

(al + gl) (16)

where al is the acceleration of the object in the localcoordinate system. Thus al can be estimated at discretetime index k

al,k = Rlc,kfc,k − gl (17)

These local frame acceleration estimates can be pro-cessed by our Kalman filter if we change our observationmatrix to

C =[

I3 03 I3

]. (18)

Or if we have acceleration measurements and no positionmeasurements

C =[

03 03 I3

]. (19)

For our implementation we have inertial measurementsat 150Hz and position estimates at approximately 2-3Hz,so our observation matrix changes depending on whichmeasurements are present.

Fig. 7 shows the outcome of tracking our data setwith inertial information alone. We see that our estimatedpath quickly diverges from the true path. A small biasin our angular rate measurements causes our estimatedorientation to deviate from our true orientation. Thuswhen we attempt to subtract the effect of gravity it will

2 3 4 5 6 7 8 9

0

1

2

3

4

5

6

7

8

9

10

X [m]

Y [

m]

Filt. Inert.: 11.52m

Truth Loc.

Stop Loc.

Approx. Path

Fig. 7. Inertial Tracking

not be canceled perfectly and leave some residue. Thisresidue acts as a bias on our acceleration measurements(in the local coordinate frame), which quickly yields alarge position error.

One common and powerful technique to curb theerror growth in inertial navigation systems is to usezero velocity updates (ZUPTs).[11] If the unit is knownto have zero velocity at some moment in time thisinformation can be used to reset some of the growingerrors in the Kalman filter. First, the velocity state in theKalman filter may be set to zero. Second, between twozero velocity update times the average acceleration inthe local frame must be zero. To take full advantage ofthis fact, one must wait for a ZUPT to repair the inertialdata from before that ZUPT and use it for tracking. Thisis acceptable if the time between ZUPTs is short andsome lag in the tracking solution is acceptable. For theanalysis in this paper it has been assumed that this lag isacceptable. As our system is intended to track personnelit is possible for us to obtain these ZUPTs if our unit isplaced on a part of the body that stops frequently suchas the foot.

Recall for our data set the cart was brought to acomplete stop at the locations depicted on the truth path.



2 3 4 5 6 7 8 9

0

1

2

3

4

5

6

7

8

9

10

X [m]

Y [

m]

Inert. w/ZUPT: 0.19m

Truth Loc.

Stop Loc.

Approx. Path

Fig. 8. Inertial Tracking with ZUPTs

We can take advantage of these with ZUPTs to improveour tracking performance dramatically. Fig. 8 showsthe tracking performance with inertial information andZUPTs. We see that the error is now 0.19m, comparableto our position only tracking result. The data set wastaken over a short enough period of time that thistechnique resulted in estimates which did not deviatesignificantly from the true path. This is certainly not aviable long term solution however as it is an open loopinertial tracker and the path estimate will thus ultimatelydiverge due to sensor bias.

The goal of these investigations is to use both theposition estimates from our RF system as well as iner-tial information to create an accurate long term stabletracking system. Since the inertial tracker is quite ac-curate in the short term we can apply a small weighton information from our RF position estimates in ourKalman filter. Our RF position estimates may have largeerrors, but as long as they don’t tend towards a certaindirection (that is, as long as they have zero mean) theywill keep the inertial tracker from slowly drifting awayfrom the proper path. Using this fusion of data in ourKalman filter we achieve the result shown in Fig. 9. Weobtain the smallest error seen thus far for this data set at

2 3 4 5 6 7 8 9

0

1

2

3

4

5

6

7

8

9

10

X [m]

Y [

m]

Pos.+ Inert.: 0.14m

Truth Loc.

Stop Loc.

Approx. Path

Fig. 9. Tracking with Inertial and RF Position Estimates

0.14m. While this is not dramatically smaller than ourposition only tracker, it does show inertial informationcan be used to improve the RF tracking while provid-ing RF based amelioration of inertial drift that wouldeventually accumulate and then rapidly degrade positioninformation. In more challenging environments such aslarger buildings we expect our RF position estimatesto present larger errors if employed alone, while theinertially supplemented solutions will be substantiallyunaffected.

V. FUTURE WORK

The idea of an inertial supplementation to our RFsystem seems promising at this time but more work isrequired to bring it to full fruition. Future research willfocus on the several areas of effort described below.

We still need to close the loop on our orientationtracking, as currently we are only using gyroscope mea-surements. The most common solution to this problemis to use the direction of measured gravity as a pitchand roll reference and magnetometer measurements as ayaw reference. We are investigating means to use our RFinformation to provide more reliable information for thispurpose. This will likely involve a solution with coupling



between orientation tracking and position tracking suchas an extended Kalman filter. [12]

We wish to evaluate our tracking performance in moredifficult RF environments, as well as with tracking overa longer period of time.

Results in this paper were obtained by post-processing. Ultimately the inertial supplementation willneed to be integrated into our real-time tracking system.As the inertial unit must be worn by the personnel to betracked, the recorded data must be sent wirelessly to oursignal processing station.

VI. CONCLUSIONS

This paper has explored the proposed idea of im-proving WPI’s existing RF-based Precision PersonnelLocation system by adding information from an inertialmeasurement unit. We have shown that indeed suchinformation can improve the tracking performance of oursystem compared to tracking with RF position estimatesalone.

ACKNOWLEDGMENT

The support of the National Institute of Justice of theDepartment of Justice is gratefully acknowledged.

REFERENCES

[1] J. Duckworth et al., “WPI Precision Personnel Locator System:Evaluation by First Responders”, Proc. ION GNSS, Sepetember2007, Fort Worth, Texas.

[2] D. Cyganski, et al., “WPI Precision Personnel Locator System:

Indoor Location Demonstrations and RF Design Improvements”,Proc. Institute of Navigation, 63rd Annual Meeting, April 2007,Cambridge, Mass.

[3] D. Cyganski, J. A. Orr, R. Angilly, and B. Woodacre, “Per-formance Limitations of a Precision Indoor Positioning SystemUsing a Multi-Carrier Approach”, Proc. Institute of Navigation,National Technical Meeting 2005, January 24-26, San Diego,Calif.

[4] D. Cyganski, J. A. Orr, and W. R. Michalson, “Performanceof a Precision Indoor Positioning System Using Multi CarrierApproach,” Proc. ION NTM 2004, January 26-28, San Diego,Calif.

[5] D. Cyganski, J. A. Orr, D. Breen, B. Woodacre, “Error Analysisof a Precision Indoor Positioning System,” Proc. ION AM, June7-9, 2004, Dayton, Ohio.

[6] D. Cyganski, J. Orr, W. Michalson, “A Multi-Carrier Techniquefor Precision Geolocation for Indoor/Multipath Environments”,Proc. Institute of Navigation GPS/GNSS 2003, September 9-12,Portland, Oregon.

[7] Kalman, R. E. , “A New Approach to Linear Filtering andPrediction Problems”, Transaction of the ASME-Journal of BasicEngineering, 82(Series D), 1960.

[8] Brown, R. G. , & Hwang, P. Y. C. Introduction to Random Signalsand Applied Kalman Fitering. New York: John Wiley & Sons.1992.

[9] MEMSense, Nano Inertial Measurement Unit Series Documen-tation, Document:DN00010, November 2007.

[10] E. Foxlin, “Inertial Head-Tracker Sensor Fusion by a Comple-mentary Separate-Bias Kalman Filter”, Proc. IEEE VRAIS 1996,March 30-April 3, Santa Clara, CA.

[11] E. Foxlin, “Pedestrian Tracking with Shoe-Mounted InertialSensors”, IEEE Computer Graphics and Applications, Volume25, Issue 6, Nov.-Dec. 2005.

[12] A. Schumacher, “Integration of a GPS aided Strapdown InertialNavigation System for Land Vehicles”, Master’s Thesis, RoyalInstitute of Technology, Sweden, March 2006.



wpi precision personnel locator system: inertial

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