Tensor-decomposition MUSIC Algorithms for Vector Hydrophone Array

Vector hydrophone measures the acoustic pressure and acoustic particle velocity of the same point simultaneously, so more acoustic information is available than that of traditional scalar hydrophones. Multiple Signal Characterization is a spectral estimation algorithm with high resolution. In this paper, The 3rd tensor of the received signals from vector hydrophones are modeled, and the signal subspace is derived by the higher-order singular decomposition, so the DOA of sources are estimated using MUSIC. 3rd tensor-based signal subspace estimation via HOSVD is a better estimate of the desired signal subspace than the subspace estimate obtained by the SVD of a matrix which exploited the structure inherent in the multi dimensional measurement data, so significant improvement estimation of DOA are achieved by this method. Simulation results exhibit the superiority of tensor-decomposition MUSIC algorithm to the conventional MUSIC using vector hydrophone array, so it processed high value of engineering application.