Line Spectrum Enhancement Technology Based on Second-order Vector Hydrophone

doi:10.16182/j.issn1004731x.joss.25-1005

Abstract

Abstract:

In view of the problem of limited detection of line spectrum signals in low signal-to-noise ratio underwater environments, a spatial-temporal cooperative line spectrum enhancement technology based on a two-dimensional second-order vector hydrophone was proposed. A receiving signal model of the two-dimensional second-order vector hydrophone was established to clarify the spatial characteristics of its output signals. For spatial signal processing, the signals from each channel of the vector hydrophone were fused to suppress noise, and a channel combination method with high spatial directivity gain was proposed to achieve spatial processing gain. For temporal signal processing, an adaptive line spectrum enhancer was improved and combined with the time reversal convolution method to further suppress noise and enhance periodic line spectrum signals. Simulation results indicate that the spatial-temporal cooperative line spectrum enhancement technology can effectively enhance line spectrum signals with an input spectral signal-to-noise ratio of above -23 dB.

Key words: two-dimensional second-order vector hydrophone, line spectrum enhancement, spatial-temporal joint processing, channel combination, time reversal convolution, adaptive enhancement

CLC Number:

TP391.9

Wang Zhengkai, Yu Yirong, Hu Qing. Line Spectrum Enhancement Technology Based on Second-order Vector Hydrophone[J]. Journal of System Simulation, 2026, 38(8): 2312-2323.

Figures/Tables 14

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

References 18

[1]	陈洪娟. 矢量传感器[M]. 哈尔滨: 哈尔滨工程大学出版社, 2006: 8-9.
[2]	程振阅, 赵荣荣, 马雄超, 等. 二阶镶拼圆管矢量接收换能器研究[J]. 声学技术, 2021, 40(1): 147-150.
	Cheng Zhenyue, Zhao Rongrong, Ma Xiongchao, et al. Research on the Second Order Vector Receiving Transducer Composed of a Segmented Ring[J]. Technical Acoustics, 2021, 40(1): 147-150.
[3]	李运赫, 董金鑫. 基于单矢量水听器的水中目标高分辨线谱增强方法[J]. 传感器与微系统, 2024, 43(6): 55-58.
	Li Yunhe, Dong Jinxin. High Resolution Line Spectrum Enhancement Method of Underwater Target Based on Single Vector Hydrophone[J]. Transducer and Microsystem Technologies, 2024, 43(6): 55-58.
[4]	王通, 张争气, 尚宇航. 基于镶拼圆管换能器的两种波束形成方法对比[J]. 压电与声光, 2017, 39(6): 834-838.
	Wang Tong, Zhang Zhengqi, Shang Yuhang. Contrast of Two Kinds of Beam Forming Methods Based on Segemented Ring Transducer[J]. Piezoelectrics & Acoustooptics, 2017, 39(6): 834-838.
[5]	马林, 赵安邦, 汪科任, 等. 矢量水听器组合通道后置自适应处理与应用[J]. 哈尔滨工程大学学报, 2020, 41(7): 991-997.
	Ma Lin, Zhao Anbang, Wang Keren, et al. Vector Hydrophone Combined Channel Post Adaptive Processing and Its Application[J]. Journal of Harbin Engineering University, 2020, 41(7): 991-997.
[6]	王宇杰, 李子高, 迟骋, 等. 单矢量水听器的组合二阶统计量解卷积方位估计[J]. 声学学报, 2023, 48(4): 656-667.
	Wang Yujie, Li Zigao, Chi Cheng, et al. Single Vector Hydrophone Time-domain Deconvolution Bearing Estimation Method Based on Combined Second-order Statistics[J]. Acta Acustica, 2023, 48(4): 656-667.
[7]	Silvia M T, Richards R T. A Theoretical and Experimental Investigation of Low-frequency Acoustic Vector Sensors[C]//OCEANS '02 MTS/IEEE. Piscataway: IEEE, 2002: 1886-1897.
[8]	D'Spain G L, Luby J C, Wilson G R, et al. Vector Sensors and Vector Sensor Line Arrays: Comments on Optimal Array Gain and Detection[J]. The Journal of the Acoustical Society of America, 2006, 120(1): 171-185.
[9]	Peng Chengyan, Zhang Xueliang, Meng Zhou. Bearing Estimation for Optical Fiber Vector Hydrophone with In-band Resonance[J]. Applied Acoustics, 2020, 158: 107055.
[10]	马林, 王彪, 赵安邦, 等. 基于声压振速时反卷积联合处理的宽带信号检测[J]. 江苏科技大学学报(自然科学版), 2021, 35(6): 64-71, 106.
	Ma Lin, Wang Biao, Zhao Anbang, et al. Broadband Source Detection Based on Sound Pressure and Vibration Velocity Time Reversal Convolution Joint Processing[J]. Journal of Jiangsu University of Science and Technology(Natural Science Edition), 2021, 35(6): 64-71, 106.
[11]	古天龙, 张清智, 李晶晶. 基于时-频注意力机制网络的水声目标线谱增强[J]. 电子与信息学报, 2024, 46(1): 92-100.
	Gu Tianlong, Zhang Qingzhi, Li Jingjing. Line Spectrum Enhancement of Underwater Acoustic Targets Based on a Time-frequency Attention Network[J]. Journal of Electronics & Information Technology, 2024, 46(1): 92-100.
[12]	杜栓平, 陈越超, 罗兆瑞. 利用深层卷积网络自适应增强学习的水声目标线谱提取方法[J]. 声学学报, 2023, 48(4): 699-714.
	Du Shuanping, Chen Yuechao, Luo Zhaorui. Line Spectrum Extraction of Underwater Acoustic Target Using Deep Convolution Network and Adaptive Enhancement Learning[J]. Acta Acustica, 2023, 48(4): 699-714.
[13]	裴明, 陈阳, 邓林红. 基于U-net网络改进的线谱检测方法[J]. 电子测量技术, 2021, 44(13): 85-90.
	Pei Ming, Chen Yang, Deng Linhong. An Improved Spectrum Line Extraction Method Based on U-net Network[J]. Electronic Measurement Technology, 2021, 44(13): 85-90.
[14]	李庆龙, 宋文华, 高大治, 等. 使用干涉谱图的浅海目标无源定位[J]. 声学学报, 2022, 47(5): 625-633.
	Li Qinglong, Song Wenhua, Gao Dazhi, et al. Passive Localization of Shallow Sea Targets Using Interferogram[J]. Acta Acustica, 2022, 47(5): 625-633.
[15]	王升贵, 胡桥, 陈迎亮, 等. 基于深度学习的水下目标识别方法研究[J]. 舰船科学技术, 2020, 42(23): 141-145.
	Wang Shenggui, Hu Qiao, Chen Yingliang, et al. Research on Underwater Target Recognition Method Based on Deep Learning[J]. Ship Science and Technology, 2020, 42(23): 141-145.
[16]	李雄辉, 梁国龙, 沈同圣, 等. 单矢量水听器的改进稀疏贝叶斯学习方位估计算法[J]. 声学学报, 2025, 50(1): 77-85.
	Li Xionghui, Liang Guolong, Shen Tongsheng, et al. Improved Sparse Bayesian Learning Direction Estimation Algorithm for Single Vector Hydrophones[J]. Acta Acustica, 2025, 50(1): 77-85.
[17]	洪连进, 王哲睿, 吴明泽. 水下声传感器及其工程应用[M]. 北京: 科学出版社, 2023: 163-164.
	Hong Lianjin, Wang Zherui, Wu Mingze. Underwater Acoustic Sensors and Their Engineering Applications[M]. Beijing: Science Press, 2023: 163-164.
[18]	Zhao Anbang, Ma Lin, Ma Xuefei, et al. An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System[J]. Sensors, 2017, 17(2): 412.