Local confidence level enhanced cross-spectral DOA histogram of single vector hydrophone
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摘要: 单矢量水听器互谱方位直方图具有一定的多目标分辨能力，但其性能受到目标信号分离正交性(Window Dis‐joint Orthogonality, WDO)的制约。WDO特性越强，表示主导目标的能量占比越大，互谱方位估计的结果越接近主导目标的真实方位。文章提出利用局部置信度增强互谱方位直方图的多目标分辨性能。局部置信度表示样本中主成分与其他成分之间的比值关系，因此可以作为信号 WDO特性强弱的估计。在统计互谱方位直方图时，利用局部置信度对时频点的方位估计结果进行加权，增加 WDO特性强的时频点的方位估计结果在方位直方图中的贡献，提高目标真实方位处的谱峰，从而增强方位直方图多目标分辨的效果。湖试数据的分析表明了利用局部置信度加权能够有效提高多目标分辨的效果。Abstract: The cross spectral DOA histogram obtained by a single vector hydrophone can distinguish multi-targets to a certain extent. However, its performance depends on the window disjoint orthogonality (WDO) of target signals. The WDO indicates the proportion of the dominant signal in the total signals. The stronger the WDO, the closer the estimation of the cross spectral DOA histogram is to the real DOA of the dominant target. In this paper, local confidence level is used to enhance the cross spectral DOA histogram for multi-target resolution. Local confidence level represents the proportion of the principal component in the total components, so it can be used as an estimate of WDO. In counting cross spectral DOA histogram, the DOA estimation results at time-frequency points are weighted by local confidence levels to enhance the contribution of the DOA estimation results at the time-frequency points with strong WDO, so that the spectral peaks near the real DOAs of the targets are increased, and the multi-target resolution is improved. The analysis of lake trial data confirms the availability of local confidence weighting in improving multitarget resolution performance.
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[1] 杨德森,朱中锐,田迎泽.矢量声呐技术理论基础及应用发展趋势[J].水下无人系统学报, 2018, 26(3):185-192. YANG Desen, ZHU Zhongrui, TIAN Yingze. Theoretical bases and application development trend of vector sonar technology[J]. Journal of Unmanned Undersea Systems, 2018, 26(3):185-192.
[2] 孙芹东,张小川,韩梅,等.面向水下滑翔机平台的耐压复合同振式矢量水听器[J].兵工学报, 2020, 41(10):2063-2070. SUN Qindong, ZHANG Xiaochuan, HAN Mei, et al. Combined pressure-resistant co-vibrating vector hydrophone for underwater glider platform[J]. Acta Armamentarii, 2020, 41(10):2063-2070.
[3] FELISBERTO P, SANTOS P, JESUS S M. Acoustic pressure and particle velocity for spatial filtering of bottom arrivals[J]. IEEE Journal of Oceanic Engineering, 2019, 44(1):179-192.
[4] SONG Y, WONG K T. Three-dimensional localization of a near-field emitter of unknown spectrum, using an acoustic vector sensor corrupted by additive noise of unknown spectrum[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(2):1035-1041.
[5] 姚直象.单矢量水听器信号处理研究[D].哈尔滨:哈尔滨工程大学, 2005. YAO Zhixiang. Research on signal processing based on single vector hydrophone[D]. Harbin:Harbin Engineering University, 2005.
[6] STINCO P, TESEI A, FERRI G, et al. Passive acoustic signal processing at low frequency with a 3-D acoustic vector sensor hosted on a buoyancy glider[J]. IEEE Journal of Oceanic Engineering, 2021, 46(1):283-293.
[7] SONG A J, ABDI A, BADIEY M, et al. Experimental demonstration of underwater acoustic communication by vector sensors[J]. IEEE Journal of Oceanic Engineering, 2011, 36(3):454-461.
[8] 姚直象,惠俊英,殷敬伟,等.基于单矢量水听器四种方位估计方法[J].海洋工程, 2006, 24(1):122-127, 131. YAO Zhixiang, HUI Junying, YIN Jingwei, et al. Four approaches to DOA estimation based on a single vector hydrophone[J]. The Ocean Engineering, 2006, 24(1):122-127, 131.
[9] AGARWAL A, AGRAWAL M, KUMAR A. Higher-order-statistics-based direction-of-arrival estimation of multiple wideband sources with single acoustic vector sensor[J]. IEEE Journal of Oceanic Engineering, 2020, 45(4):1439-1449.
[10] 王文龙,王超,韩梅,等.矢量水听器在水下滑翔机上的应用研究[J].兵工学报, 2019, 40(12):2580-2586. WANG Wenlong, WANG Chao, HAN Mei, et al. Research on application of vector hydrophone onboard an underwater glider[J]. Acta Armamentarii, 2019, 40(12):2580-2586.
[11] 王超,孙芹东,张林,等.南中国海"G-Argo "声学浮标目标探测能力分析[J].应用声学, 2019, 38(6):1025-1032. WANG Chao, SUN Qindong, ZHANG Lin, et al. Analysis of target detection capability of" G-Argo"acoustic buoy in South China Sea[J]. Journal of Applied Acoustics, 2019, 38(6):1025-1032.
[12] 王逸林.希尔伯特黄变换在矢量信号处理中的应用研究[D].哈尔滨:哈尔滨工程大学, 2006. WANG Yilin. Research on the application of Hilbert-Huang transformation to vector signal processing[D]. Harbin:Harbin Engineering University, 2006. [13] 王逸林,蔡平,许丹丹.应用希尔伯特黄变换的单矢量传感器多目标分辨研究[J].声学技术, 2006, 25(4):376-380. WANG Yilin, CAI Ping, XU Dandan. Separation of multiple targets using a single vector sensor based on Hilbert-Huang transform[J]. Technical Acoustics, 2006, 25(4):376-380.
[14] 孙芹东,王超,张小川,等.二维矢量水听器及其在Argo浮标平台上的应用技术[J].兵工学报, 2020, 41(8):1566-1572. SUN Qindong, WANG Chao, ZHANG Xiaochuan, et al. Two-dimensional vector hydrophone and its application in Argo buoy platform[J]. Acta Armamentarii, 2020, 41(8):1566-1572.
[15] CHEN Y, WANG J X, YU Y, et al. The W-disjoint orthogonality of underwater acoustic signals and underdetermined source counting for acoustic vector sensor[C]//2019 IEEE International Conference on Signal, Information and Data Processing. Chongqing, China. IEEE, 2019:1-4.
[16] 韩雪,朴胜春,付金山.舰船辐射噪声听觉节奏的时变响度特征提取[J].哈尔滨工程大学学报, 2020, 41(4):487-492. HAN Xue, PIAO Shengchun, FU Jinshan. Time-varying loudness feature extraction of the audition rhythm of ship radiation noise[J]. Journal of Harbin Engineering University, 2020, 41(4):487-492.
[17] RICKARD S. The DUET Blind Source Separation Algorithm[M]//Blind Speech Separation, c2007:217-241.
[18] SGOUROS T, MITIANOUDIS N. A novel directional framework for source counting and source separation in instantaneous underdetermined audio mixtures[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020, 28:2025-2035.
[19] ARBERET S, GRIBONVAL R, BIMBOT F. A robust method to count and locate audio sources in a multichannel underdetermined mixture[J]. IEEE Transactions on Signal Processing, 2010, 58(1):121-133.
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