1 / 27

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

felix
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related