Iterative reweighted proximal projection based DOA estimation algorithm for monostatic MIMO radar

Abstract

The problem of nonconvex and nonsmooth sparse representation for direction of arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar is addressed in this paper, which is dealt with by a novel iterative reweighted proximal projection method. The proposed method firstly obtains the array covariance vector by performing the vectorization operation on the reduced dimensional covariance matrix. Then a sparse representation framework is formulated for DOA estimation through minimizing the nonconvex and nonsmooth sparsity promoting function, and the weighted matrix, which is based on the high-order power of the inverse of the reduced dimensional covariance matrix, is designed for reweighting the nonconvex and nonsmooth minimization to enhance the sparsity of the solution. Thereafter, an iterative algorithm using proximal projection approach along with reweighted penalty ideas is developed to recover the sparse solution. Finally, DOA estimation is accomplished by searching the spectrum of the solution. Due to achieving a better approximation to the l0 norm, the proposed method exhibits better DOA estimation accuracy than the reweighted l1-SVD algorithm and reweighted SL0 algorithm. Furthermore, the proposed method can avoid any a-priori information on the number of targets. Simulation results are presented to verify the effectiveness of the proposed method.