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

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

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

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  1. SCPL: Indoor Device-Free Multi-Subject Counting andLocalization 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

  2. 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

  3. 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

  4. 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 Si • DS1, DS1, DS1 ,……, DSk

  5. 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

  6. Outline • Counting multiple subjects • Localizing multiple subjects • Experimental setup and result • Limitation • Conclusion

  7. 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

  8. 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

  9. Location-Link Coefficient Matrix • For each link, calculate the correlation between a cell pair (i,j)  ij • Coefficient Matrix • When two cell close to each other • High correlation • When only one cell affect link l • Low correlation

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

  11. Outline • Counting multiple subjects • Localizing multiple subjects • Experimental setup and result • Limitation • Conclusion

  12. Conditional Random Field Formulation • Transition model • Define • Cell neighbors: adjacent cells which can be entered • Order of Neighbor: neighbor distance • Trajectory ring: • Radius r: area consist of up to r-order neighbors • Let be the cells in i’s r-trajectory • Nr(i) be the size of , thus

  13. Localization Algorithm • Viterbi algorithm: find highest probably path • Denote Q = {q1,…,qc}, C is total number of subjects • For current state Qt, permutation • For each permutation, compute Viterbi score

  14. Outline • Counting multiple subjects • Localizing multiple subjects • Experimental setup and result • Limitation • Conclusion

  15. 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

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

  17. Counting Percentage

  18. Location-Link Coefficient

  19. Counting Result

  20. Localization Result

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

  22. Location-Link Coefficient

  23. Counting Result

  24. Localization Error

  25. Outline • Counting multiple subjects • Localizing multiple subjects • Experimental setup and result • Limitation • Conclusion

  26. 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

  27. 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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