Low-Complexity DOA Estimation via OMP and Majorization-Minimization

Traditional sparse representation algorithms for direction-of-arrival (DOA) estimation always discrete successive azimuths domain and assume the DOAs lie in prior discretized spatial grid. However, discretization incurs errors and leads to poor performance in practice owning to that there always exist mismatches between the discrete azimuths and the true continuous DOAs. Several efforts have been worked to resolve grid mismatches issue, but these techniques involve serious computational burden. In Ibis paper, a low-complexity DOA estimation method is proposed, which firstly efficiently shrinks dimension of dictionary utilizing Orthogonal Matching Pursuit (OMP), then a iterative refine algorithm is developed by Majorization-Minimization (MM) method. Numerical results show that the proposed algorithm achieves superior performance for handing DOA estimation with low-complexity as well as high accuracy.