DOA estimation method based on the covariance matrix sparse representation

The performance of the L₁-norm-based sparse representation of array covariance vectors(L₁-SRACV) algorithm significantly degrades with the number of samples decreasing. This paper analyzes the essential cause of this performance degradation and proposes a new direction of arrival(DOA) estimation method based on the fast maximum likelihood(FML) algorithm. Firstly, the FML algorithm is employed to estimate the covariance matrix, which attenuates the instability of the small eigenvalues of the covariance matrix. Then the sparse representation model based on the FML is formulated for DOA estimation and finally, optimized by removing the diagonal elements of the covariance matrix to obtain better performance. Simulation results indicate that our method outperforms the L₁-SRACV with a higher accuracy and detection possibility, particularly under small samples support.