Covariance vector sparsity-aware DOA estimation for monostatic MIMO radar with unknown mutual coupling

In this paper, a covariance vector sparsity-aware DOA estimation method is proposed for monostatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling. The new method firstly utilizes the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM) in both of the transmit and receive arrays to eliminate the unknown mutual coupling. Then a sparse representation framework of the array covariance vector is formulated for obtaining the coarse DOA estimation. Finally, a refined maximum likelihood estimation procedure is introduced to estimate the DOA based on the recovered result. Compared with conventional algorithms, the proposed method provides higher angular resolution and better angle estimation performance. Furthermore, the computational complexity of the proposed method is reasonable, because it only involves single measurement vector (SMV) problem and does not require a dense discretized sampling grid for the recovered procedure. Simulation results are used to verify the effectiveness and advantages of the proposed method. HighlightsThe DOA estimation problem for monostatic MIMO radar with unknown mutual coupling is considered.A sparse representation framework of covariance vector is proposed for the coarse DOA estimation.A maximum likelihood estimation procedure is exploited for the accurate DOA estimation.The proposed method provides better performance than both l1-SVD and ESPRIT-Like algorithms.