高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

相似网络构建与表征的水下声信号检测

张红伟 王海燕 闫永胜 申晓红

张红伟, 王海燕, 闫永胜, 申晓红. 相似网络构建与表征的水下声信号检测[J]. 电子与信息学报, 2024, 46(1): 58-66. doi: 10.11999/JEIT230253
引用本文: 张红伟, 王海燕, 闫永胜, 申晓红. 相似网络构建与表征的水下声信号检测[J]. 电子与信息学报, 2024, 46(1): 58-66. doi: 10.11999/JEIT230253
ZHANG Hongwei, WANG Haiyan, YAN Yongsheng, SHEN Xiaohong. Underwater Acoustic Signal Detection using Similarity Network Construction and Representation[J]. Journal of Electronics & Information Technology, 2024, 46(1): 58-66. doi: 10.11999/JEIT230253
Citation: ZHANG Hongwei, WANG Haiyan, YAN Yongsheng, SHEN Xiaohong. Underwater Acoustic Signal Detection using Similarity Network Construction and Representation[J]. Journal of Electronics & Information Technology, 2024, 46(1): 58-66. doi: 10.11999/JEIT230253

相似网络构建与表征的水下声信号检测

doi: 10.11999/JEIT230253
基金项目: 国家自然科学基金(62031021, 62271404, 62201439), 西北工业大学2023研究生创新基金(CX2023041)
详细信息
    作者简介:

    张红伟:男,博士生,研究方向为水声信号检测、复杂网络

    王海燕:男,博士,教授,博士生导师,研究方向为水声信息感知、水下电子对抗与智能电子系统、水声通信与组网、目标识别与跟踪

    闫永胜:男,博士,副教授,硕士生导师,研究方向为水声信息感知、目标识别与定位跟踪

    申晓红:女,博士,教授,博士生导师,研究方向为水声通信系统与通信信号处理、微弱信号检测与数字信号处理

    通讯作者:

    王海燕 hywang@sust.edu.cn

  • 中图分类号: TN911.23

Underwater Acoustic Signal Detection using Similarity Network Construction and Representation

Funds: The National Natural Science Foundation of China (62031021, 62271404, 62201439), The 2023 Innovation Fund for Graduate Students of Northwestern Polytechnical University (CX2023041)
  • 摘要: 水下声信号检测在海洋防御系统中扮演着不可或缺的角色,同时也广泛应用于民用领域。然而,在没有目标信号先验信息的情况下,目前仍缺乏行之有效的水下声信号检测方法。为此,该文提出了一种新的算法—相似网络,以解决在复杂海洋背景下水下目标检测的难题。该方法结合了信息几何和复杂网络理论,通过将节点相似度度量问题转化为矩阵流形上的几何问题,测量不同时间尺度上数据之间的相似性,并构建时间序列数据的网络表示。同时还引入了图信号处理理论,以提取目标信号内部隐藏的动力学特性,从而实现无目标先验信息下的水下声信号检测。通过对仿真和实测数据的研究验证,证明了该方法的有效性。结果表明,相似网络方法优于现有的网络构建和目标信号被动检测方法,能够更有效地检测水下声信号,实现无目标先验信息下的水下声信号检测。
  • 图  1  Chen混沌信号

    图  2  不同信噪比信号重构网络拓扑图

    图  3  不同信噪比信号重构网络谱特性分析

    图  4  高斯白噪声重构网络最大谱值及其概率分布统计

    图  5  检测性能对比分析图

    图  6  试验船走航路线图

    图  7  时频图

    图  8  相似网络最大谱数值图

    图  9  试验数据检测结果

    表  1  试验数据记录表

    时间试验船经纬度航速(kn)与水听器布放点距离(km)走航路线对应点
    17:1819°21.694$' $N/115°5.30$' $E8.710A点
    17:50水听器正横位置8.72水听器正横位置
    18:3419°27.903$' $N/115°14.579$' $E8.010B点
    19:15水听器正横位置8.02水听器正横位置
    19:5519°20.765$' $N/115°5.981$' $E8.510C点
    20:30水听器正横位置8.52水听器正横位置
    21:1019°24.888$' $N/115°15.986$' $E8.510D点
    22:05E点位置8.520E点
    00:3018°58.785$' $N/115°28.206$' $E8.556终点
    下载: 导出CSV
  • [1] LEROY E C, SAMARAN F, STAFFORD K M, et al. Broad-scale study of the seasonal and geographic occurrence of blue and fin whales in the Southern Indian Ocean[J]. Endangered Species Research, 2018, 37: 289–300. doi: 10.3354/esr00927
    [2] 马石磊, 王海燕, 申晓红, 等. 复杂海洋环境噪声下甚低频声信号检测方法[J]. 兵工学报, 2020, 41(12): 2495–2503. doi: 10.3969/j.issn.1000-1093.2020.12.015

    MA Shilei, WANG Haiyan, SHEN Xiaohong, et al. Detection method of VLF acoustic signal in complex marine environmental noise[J]. Acta Armamentarii, 2020, 41(12): 2495–2503. doi: 10.3969/j.issn.1000-1093.2020.12.015
    [3] YANG Hong, LI Lulu, LI Guohui, et al. A novel feature extraction method for ship-radiated noise[J]. Defence Technology, 2022, 18(4): 604–617. doi: 10.1016/j.dt.2021.03.012
    [4] WAGHMARE R G, NALBALWAR S L, and DAS A. Transient signal detection on the basis of energy and zero crossing detectors[J]. Procedia Engineering, 2012, 30: 129–134. doi: 10.1016/j.proeng.2012.01.843
    [5] ALMOUNAJJED A, SAHOO A K, KUMAR M K, et al. Stator fault diagnosis of induction motor based on discrete wavelet analysis and neural network technique[J]. Chinese Journal of Electrical Engineering, 2023, 9(1): 142–157. doi: 10.23919/CJEE.2023.000003
    [6] WANG Xiaojuan, CHEN Feng, ZHOU Hongyuan, et al. Structural damage detection based on cross-correlation function with data fusion of various dynamic measurements[J]. Journal of Sound and Vibration, 2022, 541: 117373. doi: 10.1016/j.jsv.2022.117373
    [7] HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]. Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2980–2988.
    [8] GIRSHICK R, DONAHUE J, DARRELL T, et al. Region-based convolutional networks for accurate object detection and segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(1): 142–158. doi: 10.1109/TPAMI.2015.2437384
    [9] SHI Peiming, LI Mengdi, ZHANG Wenyue, et al. Weak signal enhancement for machinery fault diagnosis based on a novel adaptive multi-parameter unsaturated stochastic resonance[J]. Applied Acoustics, 2022, 189: 108609. doi: 10.1016/j.apacoust.2021.108609
    [10] 闫源江, 甘新年, 胡光波. 舰船辐射噪声的混沌特性检验[J]. 舰船电子工程, 2011, 31(1): 61–63,155. doi: 10.3969/j.issn.1627-9730.2011.01.020

    YAN Yuanjiang, GAN Xinnian, and HU Guangbo. Testing for chaotic property for ship radiated noise[J]. Ship Electronic Engineering, 2011, 31(1): 61–63,155. doi: 10.3969/j.issn.1627-9730.2011.01.020
    [11] SUN Yilin and ZHANG Xiaomin. Analysis of chaotic characteristics of ship radiated noise signals with different data lengths[C]. OCEANS 2022-Chennai, Chennai, India, 2022: 1–7.
    [12] LI Yuxing, GAO Peiyuan, TANG Bingzhao, et al. Double feature extraction method of ship-radiated noise signal based on slope entropy and permutation entropy[J]. Entropy, 2022, 24(1): 22. doi: 10.3390/e24010022
    [13] SONG Xingjian and XIAO Fuyuan. Combining time-series evidence: A complex network model based on a visibility graph and belief entropy[J]. Applied Intelligence, 2022, 52(9): 10706–10715. doi: 10.1007/s10489-021-02956-5
    [14] GAO Jianxi, BARZEL B, and BARABÁSI A L. Universal resilience patterns in complex networks[J]. Nature, 2016, 530(7590): 307–312. doi: 10.1038/nature16948
    [15] LI Aming, CORNELIUS S P, LIU Yangyu, et al. The fundamental advantages of temporal networks[J]. Science, 2017, 358(6366): 1042–1046. doi: 10.1126/science.aai7488
    [16] ZHANG J and SMALL M. Complex network from pseudoperiodic time series: Topology versus dynamics[J]. Physical Review Letters, 2006, 96(23): 238701. doi: 10.1103/PhysRevLett.96.238701
    [17] ZOU Yong, DONNER R V, MARWAN N, et al. Complex network approaches to nonlinear time series analysis[J]. Physics Reports, 2019, 787: 1–97. doi: 10.1016/j.physrep.2018.10.005
    [18] HU Jun, ZHANG Yujie, WU Peng, et al. An analysis of the global fuel-trading market based on the visibility graph approach[J]. Chaos, Solitons & Fractals, 2022, 154: 111613. doi: 10.1016/j.chaos.2021.111613
    [19] GAO Zhongke, CAI Qing, YANG Yuxuan, et al. Time-dependent limited penetrable visibility graph analysis of nonstationary time series[J]. Physica A:Statistical Mechanics and its Applications, 2017, 476: 43–48. doi: 10.1016/j.physa.2017.02.038
    [20] ZHANG Hongwei, WANG Haiyan, YAN Yongsheng, et al. Weighted dynamic transfer network and spectral entropy for weak nonlinear time series detection[J]. Nonlinear Dynamics, 2023, 111(10): 9345–9359. doi: 10.1007/s11071-023-08310-3
    [21] AKBARI H, SADIQ M T, UR REHMAN A, et al. Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features[J]. Applied Acoustics, 2021, 179: 108078. doi: 10.1016/j.apacoust.2021.108078
    [22] 华小强, 程永强, 王宏强, 等. 矩阵信息几何中值检测器[J]. 电子学报, 2022, 50(2): 284–294. doi: 10.12263/DZXB.20200684

    HUA Xiaoqiang, CHENG Yongqiang, WANG Hongqiang, et al. Matrix information geometric median detectors[J]. Acta Electronica Sinica, 2022, 50(2): 284–294. doi: 10.12263/DZXB.20200684
    [23] ZHANG Hongwei, WANG Haiyan, YAN Yongsheng, et al. Remote passive sonar detection by relative multiscale change entropy[J]. IEEE Sensors Journal, 2022, 22(18): 18066–18075. doi: 10.1109/JSEN.2022.3195994
    [24] TOOTOONI M S, RAO P K, CHOU C A, et al. A spectral graph theoretic approach for monitoring multivariate time series data from complex dynamical processes[J]. IEEE Transactions on Automation Science and Engineering, 2018, 15(1): 127–144. doi: 10.1109/TASE.2016.2598094
    [25] ZHANG Hongwei, WANG Haiyan, LIANG Xuanming, et al. Weighted undirected similarity network construction and application for nonlinear time series detection[J]. IEEE Signal Processing Letters, 2023, 30: 728–732. doi: 10.1109/LSP.2023.3286809
    [26] MANIS G, AKTARUZZAMAN M D, and SASSI R. Bubble entropy: An entropy almost free of parameters[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(11): 2711–2718. doi: 10.1109/TBME.2017.2664105
    [27] ZHANG Zhen, WANG Minggang, XU Hua, et al. Research on the co-movement between high-end talent and economic growth: A complex network approach[J]. Physica A:Statistical Mechanics and its Applications, 2018, 492: 1216–1225. doi: 10.1016/j.physa.2017.11.049
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  303
  • HTML全文浏览量:  200
  • PDF下载量:  88
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-04-11
  • 修回日期:  2023-07-25
  • 网络出版日期:  2023-07-26
  • 刊出日期:  2024-01-17

目录

    /

    返回文章
    返回