P. 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

P. 2/30 Outline Experiments 3 Discussions Sparse Recovery Introduction Motivation Linear Model

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

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

P. 5/30 Magic Square

P. 6/30 RF Sensor Networks

P. 7/30 The Network Layout

P. 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

P. 9/30 Linear Model

P. 10/30 Elliptical Weight Model

P. 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)

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

P. 13/30 Sparse Recovery

P. 14/30 Greedy Sparse Recovery Support DetectionSignal Estimation A, y x

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

P. 16/30 Orthogonal Matching Pursuit (OMP)

P. 17/30 Demo - OMP(1)

P. 18/30 Demo - OMP(2)

P. 19/30 Demo - OMP(3)

P. 20/30 Demo - OMP(4)

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

P. 22/30 Heuristic Selection via Feedback Info.

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

P. 24/30 Experiments-1 resolution 6x6

P. 25/30 Experiments-2 resolution 13x13

P. 26/30 Experiments-3 resolution 27x27

P. 27/30 Experiments-4 compressed meas.

P. 28/30 Experiments-5 compressed meas.

P. 29/30 Experiments-6 compressed meas.

P. 30/30 Discussions Thank You!