Feedback Based Sparse Recovery for Motion Tracking in RF Sensor Networks

Device-free motion tracking with radio tomographic networks using received signal strength (RSS) measurements has attracted considerable research efforts. Since the motion scene to be reconstructed can often be assumed sparse, i.e., it consists only of several targets, the Compressed Sensing (CS) framework can be applied. We cast the motion tracking as a CS problem and employ an efficient algorithm, Orthogonal Matching Pursuit (OMP), for sparse recovery. Furthermore, we exploit a feedback structure which leads to a substantial reduction of the amount of measurements. The feedback structure utilizes the prior knowledge (locations of targets) in time sequence to predict next frame support. Compared with the least-square type methods, the proposed motion tracking based on feedback sparse recovery can directly determine where the targets are located in the network area and reduce the amount of measurements required for reliable tracking. Experimental results show its favorable performance.