Tensor-based real-valued subspace approach for angle estimation in bistatic MIMO radar with unknown mutual coupling

Abstract

In this paper, a tensor-based real-valued subspace approach for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar with unknown mutual coupling is proposed. Exploiting the inherent multidimensional structure of received data after matched filtering, a third-order measurement tensor signal model is formulated. For eliminating the effect of the unknown mutual coupling, a sub-tensor can be extracted from the third-order measurement tensor by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM). Then the sub-tensor can be turned into a real-valued one by forward–backward averaging and unitary transformation, and a real-valued signal subspace is constructed to estimate the DOD and DOA by the higher-order singular value decomposition (HOSVD). Owing to utilize the multidimensional structure of received data and forward–backward averaging technique, the proposed method has better angle estimation performance than MUSIC-Like and ESPRIT-Like algorithms. Furthermore, the proposed method is suitable for coherent targets. Simulation results verify the effectiveness and advantage of the proposed method.