Grid-less DOA estimation of coherent sources based on the covariance matrix recovery

Abstract A grid-less direction of arrival (DOA) estimation algorithm via covariance matrix recovery is developed to improve the DOA estimation performance under low signal-to-noise ratio (SNR) for coherent and non-coherent sources. Firstly, we propose to recover the covariance matrix by utilizing both the Hermitian Toeplitz structure of the covariance matrix and the noise statistical characters in the multiple measurement vectors (MMVs). An efficient grid-less DOA estimation algorithm based on the covariance matrix’s first column is also presented to reduce grid mismatch effects. The simulation results show that the proposed DOA estimation algorithm can better recover the covariance matrix than the covariance fitting algorithms with non-coherent and coherent sources. Besides, simulations also verify that the proposed algorithm has a smaller root mean square error (RMSE) and higher estimation probability than the state-of-the-art algorithms in low SNR and small angle separation.