水下目标轴频电场弱信号综合检测方法研究

余平洋; 王宏磊; 杨益新

doi:10.11993/j.issn.2096-3920.2025-0079

水下目标轴频电场弱信号综合检测方法研究

doi: 10.11993/j.issn.2096-3920.2025-0079

西北工业大学航海学院, 陕西西安, 710129

基金项目: 国家自然科学基金联合基金重点支持项目资助(U2341201); 国家自然科学基金面上项目资助(52271350); 基础产品创新科研项目资助(14520208040).

详细信息

作者简介:
余平洋(2000-), 男, 硕士, 主要研究方向为水下目标轴频电场探测

通讯作者:
王宏磊(1987-), 男, 博士, 副教授, 主要研究方向为水下目标非声探测和跨介质通信.

中图分类号: TJ610.2; TP274.5; TN911.6
计量
- 文章访问数: 19
- HTML全文浏览量: 28
- PDF下载量: 56
- 被引次数: 0
出版历程
- 收稿日期: 2025-06-17
- 修回日期: 2025-08-27
- 录用日期: 2025-09-08
- 网络出版日期: 2025-11-18

Research on Comprehensive Detection Methods for Weak Signals in Underwater Target Shaft Frequency Electric Fields

College of Marine Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China

摘要

摘要: 针对舰船轴频电场信号检测中目标信号弱且易被噪声掩盖的问题, 提出了一种基于“优先检测与选择性增强”原则的电场信号检测方法。首先利用自适应噪声完备集合经验模态分解(CEEMDAN)结合窄带功率谱能量峰值熵比(EPER)特征, 再通过滑动窗口与动态门限技术实现对目标信号的检测。信号检测成功后, 触发三稳态随机共振与变步长最小平均P范数(VSS-LMP)增强机制, 进一步增强目标信号的线谱特征, 并在此基础上实现目标信号特征频率的提取。仿真结果表明, 所提方法在信噪比为−12 dB的条件下检测准确率超过85%, 误检率在30%以下, 且能准确提取到目标信号特征频率, 为舰船弱电场信号的实时监测提供了可行的技术路径。
- 轴频电场信号 /
- 动态门限 /
- 自适应噪声完备集合经验模态分解 /
- 水下目标
Abstract: To address the issue of weak target signals that are easily masked by noise in the detection of ship shaft frequency electric field signals, this paper proposes an electric field signal detection method based on the principle of ‘priority detection and selective enhancement.’ First, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is combined with narrowband power spectrum energy peak entropy ratio (EPER) features. Then, sliding window and dynamic threshold techniques are used to detect the target signal. After successful detection, the proposed method triggers a tri-stable stochastic resonance and variable step-size least mean P-norm (VSS-LMP) enhancement mechanism to further enhance the spectral characteristics of the target signal, thereby enabling the extraction of the target signal's characteristic frequency. Simulation results show that the proposed method achieves a detection accuracy rate exceeding 85% under a signal-to-noise ratio of -12 dB, with a false detection rate below 30%, and can accurately extract the target signal's characteristic frequency, providing a feasible technical approach for real-time monitoring of weak electromagnetic field signals from ships.
- shaft frequency electric field signals /
- dynamic threshold /
- complete ensemble empirical mode decomposition with adaptive noise /
- underwater target

HTML全文

图 1 算法整体流程示意图

Figure 1. Diagram of the overall flow of the algorithm

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图 2 原始电场信号时域波形及频谱图

Figure 2. Diagram of the time-domain waveform and spec$trum of the original electric field signal

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图 3 预处理信号时域波形及频谱图

Figure 3. Diagram of the time-domain waveform and spectrum of the preprocessed signal

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图 4 三稳态随机共振算法处理仿真结果图

Figure 4. Diagram of simulation results processed by the tri-stable stochastic resonance algorithm

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图 5 VSS-LMP算法处理仿真结果图

Figure 5. Diagram of simulation results processed by the VSS-LMP algorithm

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图 6 基于文中检测算法的检测性能分析结果图

Figure 6. Diagram of detection performance analyzed using the detection algorithm proposed in this work

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图 7 基于小波阈值去噪算法的检测性能分析结果图

Figure 7. Diagram of detection performance analyzed using the wavelet threshold denoising algorithm

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