Direction of arrival estimation for a non-ideal acoustic vector hydrophone array

In this article, to address the direction-of-arrival (DOA) estimation problem via a non-ideal acoustic vector hydrophone (AVH) array, a sparse alternating iterative minimization (SAIM) method is proposed. First, a non-ideal AVH array model is established by introducing the axial angle bias parameter into the signal model. Then, to provide accurate DOA estimation, a new cost function is formulated based on a regularized weighted least squares to recovery the sparse signal and the axial angle bias matrix. In particular, to obtain the closed-form solutions of signal and axial angle bias matrix, the Majorization-minimization algorithm is employed to turn the penalty term with a user parameter optimization problem into the weighted Frobenius norm one. In each iteration, to achieve more accurate DOA estimation, the desired axial angle bias matrix is reconstructed based on the distribution characteristics of axial angle bias parameter in the matrix. Extensive numerical simulation and experimental results show that the DOA estimation performance of the proposed method is superior to several well-known methods for a non-ideal AVH array.