Off-grid DOA Estimation with Unknown Nonuniform Noise via Covariance SBL Strategy

In this paper, a sparse Bayesian learning (SBL) strategy based on array covariance vector is proposed for off-grid DOA estimation with the unknown non-uniform noise. In the proposed method, the covariance matrix of array received signal is firstly vectorized and a reduced dimensional matrix is adopted to eliminate the redundant terms in the covariance vector for constructing a new covariance-based signal model. Then an off-grid sparse model is formulated to realize the off-grid DOA estimation with unknown non-uniform noise, where the signal variance and off-grid error are estimated by the expectation-maximization (EM) algorithm, and the covariance matrix of non-uniform noise is reconstructed by the least square (LS) strategy. Finally, numerical simulation experiments are conducted to verify the effectiveness and superiority of the proposed method.