| 单矢量水听器稀疏近似最小方差方位估计算法 |
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| 引用本文： | 王超, 笪良龙, 韩梅, 孙芹东, 王文龙. 单矢量水听器稀疏近似最小方差方位估计算法[J]. 声学学报, 2021, 46(6): 1050-1058. DOI: 10.15949/j.cnki.0371-0025.2021.06.024 |
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| 作者姓名： | 王超 笪良龙 韩梅 孙芹东 王文龙 |
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| 作者单位： | 海军潜艇学院 青岛 266199;青岛海洋科学与技术试点国家实验室 青岛 266237 |
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| 基金项目： | 青岛海洋科学与技术试点国家实验室问海计划项目(2017WHZZB0601)资助国家重点研发计划项目(2019YFC0311700) |
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| 摘 要： | 针对单矢量水听器海上目标探测问题,利用稀疏近似最小方差(Sparse Asymptotic Minimum Variance,SAMV)算法进行目标方位估计,该算法利用单矢量水听器自身具有阵列流形的特点,将整个扫描空间离散化,目标方位分布于某一离散方向位置上,利用空间信号的稀疏性可提高目标方位估计性能。仿真结果表明,SAMV算法在各信噪比条件下方位估计噪声背景级明显优于常规波束形成(Conventional Beam Forming,CBF)算法和最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)算法,当信噪比大于0dB时,该算法测向结果均方根误差小于2°,且SAMV算法具有更好的空间方位分辨能力。消声水池和海上声学浮标海上试验数据处理结果表明,SAMV算法给出了噪声背景级更低的目标方位历程图,有效验证了SAMV算法对海上目标的探测性能及其有效性。
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| 关 键 词： | 矢量水听器 稀疏近似最小方差 方位估计 声学浮标 |
| 收稿时间： | 2020-02-18 |
| 修稿时间： | 2020-05-05 |
Single vector hydrophone sparse asymptotic minimum variance bearing estimation algorithm |
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| WANG Chao, DA Lianglong, HAN Mei, SUN Qindong, WANG Wenlong. Single vector hydrophone sparse asymptotic minimum variance bearing estimation algorithm[J]. ACTA ACUSTICA, 2021, 46(6): 1050-1058. DOI: 10.15949/j.cnki.0371-0025.2021.06.024 |
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| Authors: | WANG Chao DA Lianglong HAN Mei SUN Qindong WANG Wenlong |
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| Affiliation: | 1. Navy Submarine Academy, Qingdao 266199;2. Pilot National Laboratory for Marine Science and Technology, Qingdao 266237 |
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| Abstract: | 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. |
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| Keywords: | Vector hydrophone Sparse asymptotic minimum variance Direction estimation Acoustic buoy |
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