Greedy Orthogonal Matching Pursuit for sparse target detection and counting in WSN

The recently emerged Compressed Sensing (CS) theory has widely addressed the problem of sparse targets detection in Wireless Sensor Networks (WSN) in the aim of reducing the deployment cost and energy consumption. In this paper, we apply CS approach for both sparse events recovery and counting. We first propose a novel Greedy version of the Orthogonal Matching Pursuit (GOMP) algorithm allowing to account for the decomposition matrix non orthogonality. Then, in order to reduce the GOMP computational load, we propose a two-stages version of GOMP, the 2S-GOMP, which separates the events detection and counting steps. Simulation results show that the proposed algorithms achieve a better tradeoff between performance and computational load when compared to the recently proposed GMP algorithm and its two stages version denoted 2S-GMP.