Source localization utilizing weighted power iterative compensation via acoustic vector hydrophone array

To address the source localization issue in the condition of low signal-to-noise ratio (SNR) or closely-spaced targets, a sparse signal power estimation method based on sparse covariance matrix fitting criterion (SCMFC) is proposed, which uses the weighted power compensation strategy. The analytical expression of the sparse signal power is derived by utilizing the Frobenius norm property, and then the compensation weight with a user parameter q is designed to further enforce the sparsity of signal power in spatial domain. Furthermore, the signal power compensation method is given. Moreover, a theoretical guidance how to automatically update q is also presented according to the Bayesian information criterion (BIC). Extensive numerical simulation and experimental results verify the superiority of the proposed method compared to some existing methods.