Direction of arrival estimation via reweighted norm penalty algorithm for monostatic MIMO radar

In this paper, a reweighted norm penalty algorithm for direction of arrival (DOA) estimation in monostatic multiple-input multiple-output radar is proposed. In the proposed method, exploiting the inherent multidimensional structure of received data after matched filtering, the singular value decomposition (SVD) technique of the data matrix is employed to reduce the dimension of the received signal. Then a novel weight matrix is designed for reweighting the norm minimization by exploiting the coefficients of the reduced-dimensional Capon (RD-Capon) spatial spectrum. The proposed algorithm enhances the sparsity of the solution by the reweighted norm constraint minimization, and the DOAs can be estimated by finding the non-zero rows of the recovered matrix. Owing to utilizing the SVD technique and the novel weight matrix, the proposed algorithm can provide better angle estimation performance than RD-Capon and -SRACV algorithms. Furthermore, it is suitable for coherent sources and has a low sensitivity to the incorrect determination of the source numbers. The effectiveness and superior performance of the proposed algorithm are demonstrated by numerical simulations.