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

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