Bearing-Only Multi-Target Localization for Wireless Array Networks: A Spatial Sparse Representation Approach

Bearing-only multi-target localization (BOMTL) using multiple sensors is generally required to solve the sophisticated data association problem which determines a designated sensor measurement originated from a particular target. In this paper, a novel spatial sparse representation based BOMTL method is proposed by fully utilizing a wireless array network structure. With array spatial features, the BOMTL problem can be formulated as a binary sparse vector recovery problem using the converted “pseudo-measurements” in frequency domain. The proposed method transforms the source location estimation problem into a spatial sparse representation (SSR) framework, which avoids dealing with the conventional data association. With orthogonal matching pursuit (OMP) exploiting the binary property of the sparse vector to be estimated, we develop a BOMTL-OMP algorithm to reconstruct the sparse vector. The numerical simulations demonstrate the performance of the proposed method.

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