Sparse asymptotic minimum variance based bearing estimation algorithm for a single vector hydrophone

For underwater target detection using a single vector hydrophone,sparse asymptotic minimum variance(SAMV)method is used to estimate the target bearing.The SAMV discretizes the entire scanning space and the target bearing is located at the position of the discrete direction.The SAMV algorithm utilizes the sparsity of the spatial signal to improve the estimation performance of the target bearing.Background noise level(BNL)of the bearing estimation of SAMV algorithm is lower than those of the conventional beam forming(CBF)method and minimum variance distortionless response(MVDR)method for different signal noise ratios(SNRs).When the SNR is higher than 0 d B,the direction-finding error of this algorithm is less than 2°.Moreover,the SAMV algorithm has a better dimensional orientation resolution capability.The experimental results show that the SAMV algorithm gives a bearing and time recording map with a lower BNL,which effectively verifies the effectiveness of SAMV algorithm in terms of underwater target detection.