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2011-7-28P. 1/30 Heping Song, Tong Liu, Xiaomu Luo and Guoli Wang Feedback based Sparse Recovery for Motion Tracking in RF Sensor Networks IEEE Inter.

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Presentation on theme: "2011-7-28P. 1/30 Heping Song, Tong Liu, Xiaomu Luo and Guoli Wang Feedback based Sparse Recovery for Motion Tracking in RF Sensor Networks IEEE Inter."— Presentation transcript:

1 2011-7-28P. 1/30 Heping Song, Tong Liu, Xiaomu Luo and Guoli Wang Feedback based Sparse Recovery for Motion Tracking in RF Sensor Networks IEEE Inter. Conf. on Networking, Architecture, and Storage (NAS 2011) July 28-30, 2011, Dalian, China

2 2011-7-28P. 2/30 Outline Experiments 3 Discussions Sparse Recovery Introduction Motivation Linear Model

3 2011-7-28P. 3/30 An image is a grid of pixels Matrix = a grid of pixels color by number

4 2011-7-28P. 4/30 Tomography Tomo- means “a slice/section/part” in Greek W ikipedia

5 2011-7-28P. 5/30 Magic Square 492 357 816 15

6 2011-7-28P. 6/30 RF Sensor Networks

7 2011-7-28P. 7/30 The Network Layout

8 2011-7-28P. 8/30 Radio Tomography Imaging x1x1 x4x4 x7x7 x2x2 x5x5 x8x8 x3x3 x6x6 x9x9 y6y6 y5y5 y2y2 y3y3 y1y1 y4y4 y x Inverse problem Weighte d Sum

9 2011-7-28P. 9/30 Linear Model

10 2011-7-28P. 10/30 Elliptical Weight Model

11 2011-7-28P. 11/30  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 2011-7-28P. 12/30  Track image max x / Kalman filter  The sparse nature of location finding  Directly track the location of moving targets Motivation

13 2011-7-28P. 13/30 Sparse Recovery

14 2011-7-28P. 14/30 Greedy Sparse Recovery Support DetectionSignal Estimation A, y x

15 2011-7-28P. 15/30 Support Detection Strategy  Select atoms of measurement matrix A to generate y  Determine active atoms in sparse representation of x

16 2011-7-28P. 16/30 Orthogonal Matching Pursuit (OMP)

17 2011-7-28P. 17/30 Demo - OMP(1)

18 2011-7-28P. 18/30 Demo - OMP(2)

19 2011-7-28P. 19/30 Demo - OMP(3)

20 2011-7-28P. 20/30 Demo - OMP(4)

21 2011-7-28P. 21/30 Compressed Measurements  Weight matrix --overcomplete dictionary  Feedback information

22 2011-7-28P. 22/30 Heuristic Selection via Feedback Info.

23 2011-7-28P. 23/30 Feedback Structure Predicted support The locations of the previous frame Recovered support Sparse recovery Next frame

24 2011-7-28P. 24/30 Experiments-1 resolution 6x6

25 2011-7-28P. 25/30 Experiments-2 resolution 13x13

26 2011-7-28P. 26/30 Experiments-3 resolution 27x27

27 2011-7-28P. 27/30 Experiments-4 compressed meas.

28 2011-7-28P. 28/30 Experiments-5 compressed meas.

29 2011-7-28P. 29/30 Experiments-6 compressed meas.

30 2011-7-28P. 30/30 Discussions Thank You!


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