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Heping Song , Tong Liu, Xiaomu Luo and Guoli Wang

Heping Song , Tong Liu, Xiaomu Luo and Guoli Wang. IEEE Inter. Conf. on Networking, Architecture, and Storage (NAS 2011) July 28-30, 2011, Dalian, China. Feedback based Sparse Recovery for Motion Tracking in RF Sensor Networks. Outline. 3. Introduction. Linear Model. Motivation.

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Heping Song , Tong Liu, Xiaomu Luo and Guoli Wang

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  1. Heping Song, Tong Liu, Xiaomu Luo and Guoli Wang IEEE Inter. Conf. on Networking, Architecture, and Storage (NAS 2011) July 28-30, 2011, Dalian, China Feedback based Sparse Recovery for Motion Tracking in RF Sensor Networks

  2. Outline 3 Introduction Linear Model Motivation Sparse Recovery Experiments Discussions

  3. An image is a grid of pixels Matrix = a grid of pixels color by number

  4. Tomography Tomo- means “a slice/section/part” in Greek Wikipedia

  5. Magic Square 15 15 15 15 15 15 15

  6. RF Sensor Networks

  7. The Network Layout

  8. Radio Tomography Imaging y6 y5 y4 Weighted Sum y1 y3 y2 Inverse problem y x

  9. Linear Model

  10. Elliptical Weight Model

  11. Video cameras. Don’t work in dark, through smoke or walls. Privacy concerns. Thermal imagers. Limited by walls. High cost. Motion detectors. Also limited by walls. High false alarms. Ultra wideband (UWB) radar. High cost. Received signal strength (RSS) in WSN Device-free Localization (DFL)

  12. Track image max x/ Kalman filter The sparse nature of location finding Directly track the location of moving targets Motivation

  13. Sparse Recovery

  14. Greedy Sparse Recovery x Support Detection Signal Estimation A, y

  15. Support Detection Strategy Select atoms of measurement matrix Ato generate y Determine active atoms in sparse representation of x

  16. Orthogonal Matching Pursuit (OMP)

  17. Demo - OMP(1)

  18. Demo - OMP(2)

  19. Demo - OMP(3)

  20. Demo - OMP(4)

  21. Compressed Measurements • Weight matrix --overcomplete dictionary • Feedback information

  22. Heuristic Selection via Feedback Info.

  23. Feedback Structure The locations of the previous frame Predicted support Sparse recovery Recovered support Next frame

  24. Experiments-1 resolution 6x6

  25. Experiments-2 resolution 13x13

  26. Experiments-3 resolution 27x27

  27. Experiments-4 compressed meas.

  28. Experiments-5 compressed meas.

  29. Experiments-6 compressed meas.

  30. Discussions Thank You!

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