Sensing matrix optimization for multi-target localization using compressed sensing in wireless sensor network

In the multi-target localization based on Compressed Sensing (CS), the sensing matrix's characteristic is significant to the localization accuracy. To improve the CS-based localization approach's performance, we propose a sensing matrix optimization method in this paper, which considers the optimization under the guidance of the t%-averaged mutual coherence. First, we study sensing matrix optimization and model it as a constrained combinatorial optimization problem. Second, the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes, where the threshold t is derived through the K-means clustering. With the settled optimality index, a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search (GA-TLS) is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix. Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method, with much less localization error compared to the traditional sensing matrix optimization methods.