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SCPL: Indoor Device-Free Multi-Subject Counting and Localization Using Radio Signal StrengthRutgers University

Chenren Xu

Joint work with Bernhard Firner, Robert S. Moore, Yanyong ZhangWade Trappe, Richard Howard, Feixiong Zhang, Ning AnWINLABWINLAB1Device-free Localization 2

WINLAB2Device-free Localization3

WINLAB3Why Device-free Localization? Monitor indoor human mobility

Elder/health care4

WINLAB4Why Device-free Localization? Monitor indoor human mobility

Traffic flow statistics5

WINLAB5Why Device-free Localization? Monitor indoor human mobility Health/elder care, safetyDetect traffic flowProvides privacy protectionNo identificationUse existing wireless infrastructure6WINLAB6Previous WorkSingle subject localizationGeometry-based approach (i.e. RTI)7

WINLAB7Previous WorkSingle subject localizationFingerprinting-based approach

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WINLAB8Previous WorkSingle subject localizationFingerprinting-based approach

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Require fewer nodes

More robust to multipathWINLAB9Fingerprinting N Subjects?Multiple subjects localizationNeeds to take calibration data from N people for localizing N people

10WINLAB10Fingerprinting N Subjects11

9 trials in total for 1 personWINLAB11Fingerprinting N Subjects12

WINLAB12Fingerprinting N Subjects13

WINLAB13Fingerprinting N Subjects14

36 trials in total for 2 people!WINLAB14Fingerprinting N Subjects151 person9 cells99 1 min = 9 minWINLAB15Fingerprinting N Subjects161 person2 people9 cells93636 cells36630630 1 min = 10.5 hrWINLAB16Fingerprinting N Subjects171 person2 people3 people9 cells9368436 cells366307140100 cells1004950161700161700 1 min = 112 daysThe calibration effort is prohibitive !WINLAB17SCPLInput:Collecting calibration data only from 1 subjectObserved RSS change caused by N subjectsOutput:count and localize N subjects.Main insight:If N is known, localization will be straightforward.

18WINLAB18No Subjects19

WINLAB19One Subject20

WINLAB20Two Subjects21

WINLAB21Measurement22N = 0N = 1N = 2Link 1044Link 2057Link 3005Total (N)0916N N?

N / 1 = N?

WINLAB22Linear relationship23

WINLAB23Measurement24Nonlinear problem!1.6

N / 1 < NWINLAB24Closer Look at RSS change 25

4 dB5 dBWINLAB25Closer Look at RSS change 26

5 dB6 dBWINLAB26Closer Look at RSS change 274 dB + 0 dB = 4 dB 5 dB + 6 dB = 11 dB 7 dB X0 dB + 5 dB = 5 dB = +

?4 dB5 dB5 dB6 dB5 dB7 dB4 dBWINLAB27Closer Look at RSS change 285 dB + 6 dB 7 dB X

Shared links observe nonlinear fading effect from multiple people +

!5 dB6 dB7 dBWINLAB28SCPL Part ISequential Counting (SC)29WINLAB29Counting algorithm30

DetectionWINLAB30Phase 1: Detection31

Measurement in 1st round5 dB7 dB4 dBN = 4 + 7 + 5 = 16 dBN > 1More than one person!

WINLAB31Phase 2: Localization32

Measurement in 1st round5 dB7 dB4 dBFind this guy

PC-DfP:C. Xu, B. Firner, Y. Zhang, R. Howard, J. Li, and X. Lin. Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In Proceedings of the 11th international conference on Information Processing in Sensor Networks, IPSN 12WINLAB32Phase 3: Subtraction33

Calibrationdata5 dB6 dBWINLAB33Phase 3: Subtraction34Subject count ++Go to the next iteration= -

Measurement in 1st roundCalibrationdataMeasurementIn 2nd round4 dB1 dB5 dB6 dB5 dB7 dB4 dBWINLAB34Phase 3: Subtraction35Subject count ++Go to the next iterationHold on = -

CalibrationdataMeasurement in 1st roundMeasurementIn 2nd round4 dB1 dB5 dB6 dB5 dB7 dB4 dBWINLAB35Phase 3: Subtraction36

MeasurementIn 2nd round4 dB1 dBWINLAB36Phase 3: Subtraction37

MeasurementIn 2nd roundCalibrationdata4 dB1 dB4 dB5 dBWINLAB37Phase 3: Subtraction38

MeasurementIn 2nd roundCalibrationdata

= -We over-subtracted its impact on shared link!4 dB1 dB4 dB5 dB-4 dBWINLAB38Measurement39

WINLAB39Measurement40

1st roundWINLAB40Measurement41

1st roundWINLAB41Measurement42

1st round2st roundWINLAB42Phase 3: Subtraction43We need to multiply a coefficient [0, 1] when subtracting each link= -

CalibrationdataMeasurement in 1st roundMeasurementIn 2nd round4 dB1 dB5 dB6 dB5 dB7 dB4 dBWINLAB43Location-Link CorrelationTo mitigate the error caused by this over-subtraction problem, we propose to multiply a location-link correlation coefficient before successive subtracting:

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WINLAB44Phase 3: Subtraction45Subject count ++Go to the next iteration= -

4.6 dB6 0.4 dBMeasurement in 1st roundCalibrationdataMeasurementin 2nd round5 0.8 dB4 dB5 dB7 dB4 dB1 dBWINLAB45

Phase 3: Subtraction46

Measurementin 2nd roundCalibrationdata= -Measurementin 3rd roundWe are done !4.6 dB4 dB1 dB6 0.6 dB4 0.8 dB1 dB1 dB1 dBWINLAB46SCPL Part IIParallel Localization (PL)47WINLAB47LocalizationCell-based localizationAllows use of context information Reduce calibration overheadClassification problem formulation48C. Xu, B. Firner, Y. Zhang, R. Howard, J. Li, and X. Lin. Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In Proceedings of the 11th international conference on Information Processing in Sensor Networks, IPSN 12WINLAB48Linear Discriminant Analysis RSS measurements with persons presence in each cell is treated as a class/state kEach class k is Multivariate Gaussian with common covarianceLinear discriminant function:

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Link 1 RSS (dBm)Link 2 RSS (dBm)k = 1k = 2k = 3

C. Xu, B. Firner, Y. Zhang, R. Howard, J. Li, and X. Lin. Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In Proceedings of the 11th international conference on Information Processing in Sensor Networks, IPSN 12WINLAB49LocalizationCell-based localizationTrajectory-assisted localizationImprove accuracy by using human mobility constraints50WINLAB50Human Mobility Constraints51You are free to go anywhere with limited step size inside a ring in free space

WINLAB51Human Mobility Constraints52In a building, your next step is constrained by cubicles, walls, etc.

WINLAB52Phase 1: Data Likelihood Map53

WINLAB53Impossible movements54

WINLAB54Impossible movements55

WINLAB55Phase 2: Trajectory Ring Filter56

WINLAB56Phase 3: Refinement57

WINLAB57Here you are!58

WINLAB58Viterbi optimal trajectorySingle subject localization

Multiple subjects localization

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ViterbiScore =WINLAB59System DescriptionHardware: PIP tagMicroprocessor: C8051F321Radio chip: CC1100Power: Lithium coin cell batteryProtocol: Unidirectional heartbeat (Uni-HB)Packet size: 10 bytesBeacon interval: 100 msec60

WINLAB60Office deployment61

Total Size: 10 15 mWINLAB61Office deployment6237 cells of cubicles, aisle segments

WINLAB62Office deployment6313 transmitters and 9 receivers

WINLAB63Office deployment64Four subjects testing paths

WINLAB64Counting results65

WINLAB65Counting results66

WINLAB66Localization results67

WINLAB67Open floor deployment68Total Size: 20 20 m

WINLAB68Open floor deployment6956 cells, 12 transmitters and 8 receivers

WINLAB69Open floor deployment70Four subjects testing paths

WINLAB70Counting results71

WINLAB71Localization results72

WINLAB72Conclusion and Future WorkConclusionCalibration data collected from one subject can be used to count and localize multiple subjects.Though indoor spaces have complex radio propagation characteristics, the increased mobility constraints can be leveraged to improve accuracy. Future workCount and localize more than 4 people

73WINLAB73Q & AThank you

74WINLAB74