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DONG Hao, ZHANG Yu, QIN Wei, CHEN Shuai. Extraction Method for Multiple Feature Models of The Magnetic Field from Marine Targets[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0047
Citation: DONG Hao, ZHANG Yu, QIN Wei, CHEN Shuai. Extraction Method for Multiple Feature Models of The Magnetic Field from Marine Targets[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0047

Extraction Method for Multiple Feature Models of The Magnetic Field from Marine Targets

doi: 10.11993/j.issn.2096-3920.2025-0047
  • Received Date: 2025-03-20
  • Accepted Date: 2025-06-16
  • Rev Recd Date: 2025-06-10
  • Available Online: 2025-09-26
  • Maritime targets cause distortions in the surrounding magnetic field, forming the target's magnetic signature. This signature is widely used in target detection, ship stealth. Addressing the issue of multi-modal feature extraction for target magnetic fields, a magnetic anomaly signal model under an encounter scenario is established based on the magnetic signal generation mechanism. This model analyzes the characteristic differences between different source magnetic fields of the same target. A magnetic feature-based variational mode decomposition (VMD) method is proposed, which optimizes the parameters for decomposing the target's magnetic signal. Based on frequency characteristic differences, it separates the static magnetic field and the shaft-rate magnetic field signal. Actual marine interference signals are collected and combined with simulated target signals to validate the decomposition method. Compared with traditional algorithms, the proposed method achieves signals with a higher signal-to-noise ratio and lower error. Finally, the magnetic field of a ship was measured in practice. Using the measured data, the effectiveness of the proposed decomposition method for extracting maritime target features was validated.

     

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  • [1]
    林春生, 龚沈光. 舰船物理场[M]. 第2版. 北京: 兵器工业出版社, 2007.
    [2]
    JIN H, GUO J, WANG H, et al. Magnetic anomaly detection and localization using orthogonal basis of magnetic tensor contraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(8): 5944-54. doi: 10.1109/TGRS.2020.2973322
    [3]
    SHEN Y, WANG J Z, GAO J Q, et al. Noise suppression for vector magnetic anomaly detection by noise spatial characteristics investigation[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-4.
    [4]
    KIM Y S, LEE S K, KIM J M. Influence of anode location and quantity for the reduction of underwater electric fields under cathodic protection[J]. Ocean Eng, 2018, 163: 476-482. doi: 10.1016/j.oceaneng.2018.06.024
    [5]
    LU B J, ZHANG X B. Research on the design of a novel composite solenoid model ship simulation magnetic source based on DTW[J]. AIP Advances, 2024, 14, 015201: 1-12.
    [6]
    张伽伟, 喻鹏, 姜润翔, 等. 基于舰船电场的目标跟踪方法研究[J]. 兵工学报, 2020, 41(3): 559-566. doi: 10.3969/j.issn.1000-1093.2020.03.017

    ZHANG J W, YU P, JIANG R X, et al. Electric field tracking based on static potential difference[J]. Acta Armamentarii, 2020, 41(3): 559-566. doi: 10.3969/j.issn.1000-1093.2020.03.017
    [7]
    CHENG R, JIANG R X, GONG S G. Extraction of line spectrum of the ship shaft-rate electric field based on EMD and fourth-order cumulant[J]. Ship Science and Technology, 2016, 38(1): 94-98.
    [8]
    杜德锋, 陈帅, 王磊, 等. 一种近海水域磁异常信号检测方法[J]. 水下无人系统学报, 2023, 31(2): 269-277. doi: 10.11993/j.issn.2096-3920.202203008

    DU D F, CHEN S, WANG L, et al. A detection method of magnetic anomaly signal in offshore waters[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 269-277. doi: 10.11993/j.issn.2096-3920.202203008
    [9]
    JIN H H, ZHUANG Z H, WANG H B. None-asphericity-error method for magnetic dipole target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(8): 1294-98. doi: 10.1109/LGRS.2018.2827568
    [10]
    QIN T, ZHOU L, CHEN S, et al. The novel method of magnetic anomaly recognition based on the fourth order aperiodic stochastic resonance[J]. IEEE Sensors Journal, 2022, 22(17): 17043-53. doi: 10.1109/JSEN.2022.3192668
    [11]
    包中华, 于仕财, 龚沈光. 基于小波包分解和滑动功率谱的舰船轴频电场信号检测[J]. 海军航空工程学院学报, 2012, 27(3): 257-262.

    BAO Z H, YU S C, GONG S G. Detection of ship shaft-rate electric field signals based on wavelet packet decomposition and sliding PSD[J]. Journal of Naval Aeronautical and Astronautical University, 2012, 27(3): 257-262.
    [12]
    喻鹏, 程锦房, 张伽伟, 等. 基于Rao检测器的舰船轴频电场滑动门限检测方法[J]. 兵工学报, 2021, 42(4): 827-834. doi: 10.3969/j.issn.1000-1093.2021.04.016

    YU P, CHENG J F, ZHANG J W, et al. Ship shaft-rate electric field sliding threshold detection method based on Rao detector[J]. Acta Armamentarii, 2021, 42(4): 827-834. doi: 10.3969/j.issn.1000-1093.2021.04.016
    [13]
    李松, 石敏, 栾经德, 等. 舰船轴频电场信号特征提取与检测方法[J]. 兵工学报, 2015, 36(增刊2): 220-224.

    LI S, SHI M, LUAN J D, et al. The feature extraction and detection for shaft-rate electric field of a ship[J]. Acta Armamentarii, 2015, 36(S2): 220-224.
    [14]
    程锐, 陈聪, 张伽伟. 基于EEMD和改进功率谱熵的船舶轴频电场检测[J]. 华中科技大学学报(自然科学版), 2017, 45(5): 11-16.

    CHENG R, CHEN C, ZHANG J W. Detection of ship shaft-rate electric field based on EEMD and modified power spectral entropy[J]. Journal of Huazhong University of Science and Technology(Nature Science Edition), 2017, 45(5): 11-16.
    [15]
    WANG J Z, JIANG Z K, GAO J Q, et al. Frequency characteristics analysis for magnetic anomaly detection[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
    [16]
    刘芙妍, 颜冰. 磁偶极子阵列模型的适用性研究与优化分析[J]. 物理学报, 2022, 71(12): 1-13.

    LIU F Y, YAN B. Applicability and optimization analysis of magnetic dipole array model[J]. Acta Physica Sinica, 2022, 71(12): 1-13.
    [17]
    WU Z Q, ZHU X H, LI B. Modeling and measurements of alternating magnetic signatures of ships[J]. Sensor & Transducers, 2015, 186(3): 161-167.
    [18]
    ZOLOTAREVSKII Y, BULYGIN F, PONOMAREV A. Methods of measuring the low-frequency electric and magnetic fields of ships[J]. Measurement Techniques, 2005, 48(11): 1140-44. doi: 10.1007/s11018-006-0035-6
    [19]
    熊露, 姜润翔, 龚沈光. 浅海中船舶轴频电场建模方法[J]. 国防科技大学学报, 2014, 36(1): 98-103. doi: 10.11887/j.cn.201401018

    XIONG L, JIANG R X, GONG S G. Ship modeling method of shaft-ELFE in shallow sea[J]. Journal of National University of Defense Technology, 2014, 36(1): 98-103. doi: 10.11887/j.cn.201401018
    [20]
    陈齐乐, 钱鹏飞, 孔志杰, 等. 基于变分信号分解的脉冲多普勒雷达抗扫频式干扰方法[J]. 兵工学报, 2024, 45(6): 2076-84.

    CHEN Q L, QIAN P F, KONG Z J. Anti-sweep-jamming method for PD radar based on variational signal decomposition[J]. Acta Armamentarii, 2024, 45(6): 2076-84.
    [21]
    DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. doi: 10.1109/TSP.2013.2288675
    [22]
    ZHANG X L, CAO L Y, CHEN Y, et al. Microseismic signal denoisingy ombining variational mode decomposition with permutation entropy[J]. Applied Geophysics, 2022, 19(1): 65-80. doi: 10.1007/s11770-022-0926-6
    [23]
    李华, 王天杨, 张飞斌, 等. KurVMDPgram: 一种用于旋转机械故障诊断的信号分解算法[J]. 机械工程学报, 2025, 61(4): 11-23.

    LI H, WANG T Y, ZHANG F B, et al. KurVMDPgram: A signal decomposition algorithm for fault diagnosis of rotating machinery[J]. Journal of Mechanical Engineering, 2025, 61(4): 11-23.
    [24]
    弭晴, 马永涛, 黄祉同. 基于多序列 WOA-VMD 算法的超宽带雷达心率检测[J]. 传感技术学报, 2024, 37(7): 1144-53. doi: 10.3969/j.issn.1004-1699.2024.07.006

    MI Q, MA Y T, HUANG Z T. Heart rate detection based on multi-sequence WOA-VMD algorithm using UWB radar[J]. Chinese Journal of Sensors and Actuators, 2024, 37(7): 1144-53. doi: 10.3969/j.issn.1004-1699.2024.07.006
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