Single vector hydrophone sparse asymptotic minimum variance bearing estimation algorithm

Aiming at the problem of target detection for single vector hydrophone at sea, a Sparse Asymptotic Minimum Variance (SAMV) target direction estimation algorithm based on single vector hydrophone is proposed. The SAMV algorithm utilizes the characteristics of the single vector hydrophone itself array flow vector, and discretize the entire scan space. The target bearing will be distributed in a discrete direction, and uses the sparsity of spatial signals can improve target azimuth estimation performance. The simulation results show that the SAMV algorithm's direction estimation background noise level is significantly better than the CBF and MVDR algorithms under various Signal to Noise Ratio (SNR) conditions. When the SNR is greater than 0 dB, the root mean square error of the azimuth estimation of the SAMV algorithm is less than 2°, and the SAMV algorithm has better spatial orientation resolution. The anechoic tank data and acoustic buoy experimental data processing results of SAMV algorithm can gives a bearing time recording map with lower noise background level, and effectively verified the detection performance and effectiveness of SAMV algorithm. © 2021 Acta Acustica.