| 沈鑫玉,陈涛,郭良浩,刘建军,陈艳丽.遗传算法优化变分模态分解提取舰船辐射噪声特征线谱方法[J].,2024,43(1):1-11 |
| 遗传算法优化变分模态分解提取舰船辐射噪声特征线谱方法 |
| Feature line spectrum extraction of ship radiated noise based on variable mode decomposition optimized by genetic algorithm |
| 投稿时间：2022-09-30 修订日期：2023-12-28 |
| 中文摘要: |
| 特征线谱提取是舰船目标识别的一个重要研究方向。针对传统的DEMON(Detection of Envelope Modulation On Noise)谱分析对噪声抑制能力弱的缺点，提出了一种基于遗传算法(Genetic Algorithm, GA)优化变分模态分解(Variational mode decomposition, VMD)的舰船辐射噪声特征线谱提取方法。首先利用遗传算法对VMD的两个关键参数进行优化，经过VMD分解得到各阶固有模态分量(Intrinsic mode function, IMF)。其次，通过对各阶分量的判别，保留信号主导分量，去除噪声主导分量，从而实现对噪声干扰的抑制效果。最后，对降噪后的重构信号进行频谱分析获得目标噪声特征线谱。仿真和实验数据处理结果均表明，相比DEMON谱分析法，基于GA-VMD的特征线谱提取方法具有更好的噪声抑制能力，对舰船辐射噪声的特征线谱提取效果更优。 |
| 英文摘要: |
| Feature line spectrum extraction is an important research direction of ship target recognition. Aiming at the weakness of traditional DEMON spectral analysis in noise suppression, a method for extracting the characteristic line spectrum of ship radiated noise based on genetic algorithm (GA) optimized variational mode decomposition (VMD) is proposed. Firstly, the genetic algorithm is used to optimize the two key parameters of VMD, and the inherent mode function (IMF) of each order is obtained through VMD decomposition. Secondly, the dominant component of the signal is retained and the dominant component of the noise is removed by distinguishing the components of each order, so as to achieve the suppression effect of noise interference. Finally, the target noise characteristic line spectrum is obtained by spectral analysis of the reconstructed signal after noise reduction. Simulation and experimental data processing results show that compared with DEMON spectral analysis method, the feature line spectrum extraction method based on GA-VMD has better noise suppression ability, and the feature line spectrum extraction effect of ship radiated noise is better. |
| DOI：10.11684/j.issn.1000-310X.2024.01.001 |
| 中文关键词: 舰船辐射噪声 遗传算法 变分模态分解 特征线谱提取 |
| 英文关键词: Ship radiation noise Genetic algorithm Variable mode decomposition Feature extraction. |
| 基金项目: |
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