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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
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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
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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
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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
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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
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Outline Counting multiple subjects Localizing multiple subjects Experimental setup and result Limitation Conclusion
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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
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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
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Location-Link Coefficient Matrix
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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
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Outline Counting multiple subjects Localizing multiple subjects Experimental setup and result Limitation Conclusion
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Conditional Random Field Formulation
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Localization Algorithm
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Outline Counting multiple subjects Localizing multiple subjects Experimental setup and result Limitation Conclusion
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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
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Office environment – 13 transmitter, 9 receiver – 150 m^2, divided into 37 cell – Movement scenarios
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Counting Percentage
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Location-Link Coefficient
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Counting Result
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Localization Result
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Open Floor Space 12 transmitter, 8 receiver 400 m^2, 56 cells Movement scenarios
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Location-Link Coefficient
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Counting Result
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Localization Error
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Outline Counting multiple subjects Localizing multiple subjects Experimental setup and result Limitation Conclusion
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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
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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
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