SCPL: Indoor Device-Free Multi-Subject Counting and Localization Using Radio Signal Strength Chenren Xu†, Bernhard Firner†, Robert S. Moore ∗, Yanyong Zhang† Wade Trappe†, Richard Howard†, Feixiong Zhang†, Ning An§ †WINLAB, Rutgers University, North Brunswick, NJ, USA ∗ Computer Science Dept, Rutgers University, Piscataway, NJ, USA §Gerontechnology Lab, Hefei University of Technology, Hefei, Anhui, China IPSN 2013

About This Paper Indoor localization technique – RF-based device-free passive localization – Fingerprinting based approach – Count and track multiple subjects Result – Counting accuracy: 86% – Localization accuracy: 1.3m

Contributions The first work to simultaneous counting and localizing – Up to 4 objects – Only using RF-based technique Relying on data collected by single subjects Trajectory constraints to improve tracking accuracy Recognize the nonlinear fading effects – Cause by multiple subjects

Problem Formulation Partition into K cells Training phase – Measure ambient RSS value for L links – A single subject appear in single cell (randomly walk within cell) Take N measurement for L links Subtract ambient RSS Dataset D: K * N * L matrix – Subject’s present in Cell i: State S i D S1, D S1, D S1,……, D Sk

Problem Formulation Testing phase – Measure ambient RSS for L links – A subject appears in random cell Measure RSS for all L links Subtract ambient Form an RSS vector O Compare D and O – Classification algorithm

Outline Counting multiple subjects Localizing multiple subjects Experimental setup and result Limitation Conclusion

Impact of Multiple Subject Hypothesis: more subjects – Not only affect more links – But also higher level of RSS change Infer the number of subjects by RSS change – Total energy change: – Absolute RSS mean difference Distance between subjects – Distance > 4m  faraway – Else  closeby

Counting Subjects Successive cancellation – In each round, estimate the strongest subject’s cell number – Subtract it share of RSS change If (Impact from multiple subjects is linear) – Subtract the mean vector But the impact is Nonlinear – Need an coefficient

Location-Link Coefficient Matrix

Successive Cancellation Constructing upper and lower bound Iteration 1.If (energy change < C0 upper bound)  count = 0 2.Presence detection 1.If (energy change >= C1 upper bound) 1.Increment count by one, goto next 2.Else (goto End) 3.Cell Identification 1.Estimate the occupied cell 4.Contribution Substracting 1.Substracting from O 5.End 1.If (remained energy change < C1 upper bound) 2.Increase count

Outline Counting multiple subjects Localizing multiple subjects Experimental setup and result Limitation Conclusion

Conditional Random Field Formulation

Localization Algorithm

Outline Counting multiple subjects Localizing multiple subjects Experimental setup and result Limitation Conclusion

Experiment Setup CC1100 transceiver – 909.1MHz – Broadcast 10-byte packet every 0.1s RSS collected as a mean value over 1s Training phase: 30s in each cell Performance metrics – Counting percentage – Error distance

Office environment – 13 transmitter, 9 receiver – 150 m^2, divided into 37 cell – Movement scenarios

Counting Percentage

Location-Link Coefficient

Counting Result

Localization Result

Open Floor Space 12 transmitter, 8 receiver 400 m^2, 56 cells Movement scenarios

Location-Link Coefficient

Counting Result

Localization Error

Outline Counting multiple subjects Localizing multiple subjects Experimental setup and result Limitation Conclusion

Limitation Computation complexity – 0.87s and 0.88s for 4 objects – More that 1s for 5 objects or above Long-term test – Suffer from environmental change – Fingerprint aging

Conclusion Device free localization system Track multiple subjects Average 86% counting accuracy ?? Average 1.3m localization accuracy ?? Test in two different environments – How many iteration? Not very successful with more objects