基于修正后矩阵分解的最优协方差DOA估计

o solve the problems that the traditional direction-of-arrival(DOA) estimations have poor performance when processing coherent signals in the cases of low signal-to-noise ratio and non-uniform noise . This paper proposed a DOA method based on the modified matrix decomposition method, with the best covariance matrix by the convex optimization. The modified matrix decomposition method can deal with the extraction of the coherent sources, while, overcomes the aperture loss. Furthermore, the method reconstrtucted the noise free covariance matrix by the convex optimization. Finally, using minimization search to calculate DOA. The simulation results show that, comparing to the matrix decomposition (MD) algorithm, L1-norm singular vector decomposition(L1-SVD) algorithm and spatial smoothing based covariance rank minimization (SS-CRM) algorithm, the method suppress the non-uniform noise well and have good performance in low signal noise ratio (SNR) .