Target Localization in Multipath Propagation Environment Using Dictionary-Based Sparse Representation

This paper addresses the target localization problem in complex multipath propagation environment for three-dimensional (3-D) radar systems. Firstly, an approach based on the singular value decomposition (SVD) technique is developed to reduce the data dimension and formulate the joint multiple snapshot sparse representation problem in the signal subspace domain. Subsequently, a novel sparse representation based DOA estimation algorithm, combined with alternatingly iterative and dictionary refinement techniques, is proposed. The Cramér-Rao bounds (CRB) for the target DOA and attenuation coefficient estimations of multipath model are derived in closed forms. Experimental results based on both simulated data and measured data indicate that the target localization accuracy can be effectively enhanced by utilizing the proposed algorithm in complex terrain and/or limited snapshot scenarios.