Efficient sparse representation algorithm for accurate DOA estimation of multiple targets with single measurement vector

In order to rapidly achieve accurate direction-of-arrival (DOA) estimation of multiple targets using a single measurement vector (i.e., array signal vector), we propose a new efficient sparse representation algorithm based on the orthogonal matching pursuit (OMP) using a step-by-step search approach. Regardless of the high mutual coherence of redundant dictionary (i.e., array manifold matrix), the optimal sparse solutions for DOAs of multiple targets can be yielded with a relatively low computational cost, as compared to the iterative local searching OMP method that is up-to-date. Several simulation and experimental results demonstrated that the proposed scheme is much more effective than the existing subspace-based algorithms in a noisy environment and other sparse representation algorithms, even in actual measurement cases.