电子测量与仪器学报

周星,行鸿彦,叶如,赵迪.基于优化变分模态分解的海杂波去噪方法[J].电子测量与仪器学报,2023,37(11):81-90

基于优化变分模态分解的海杂波去噪方法

Sea clutter denoising algorithm based on optimized variational mode decomposition

DOI：

英文关键词:sea clutter whale optimization algorithm fuzzy entropy denoising

基金项目:国家自然科学基金(62171228)、国家重点研发计划(2021YFE0105500)项目资助

作者	单位
周星	1.南京信息工程大学电子与信息工程学院
行鸿彦	1.南京信息工程大学电子与信息工程学院
叶如	1.南京信息工程大学电子与信息工程学院
赵迪	1.南京信息工程大学电子与信息工程学院

Author	Institution
Zhou Xing	1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology
Xing Hongyan	1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology
Ye Ru	1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology
Zhao Di	1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology

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中文摘要:

文章分析了确定变分模态分解(VMD)参数存在的问题,提出了一种基于优化变分模态分解的海杂波去噪方法。利用鲸鱼优化算法(WOA)对模态个数 K 和惩罚参数 α 进行寻优,对海杂波原信号自适应分解,去除方差贡献率(VCR)较低模态分量,结合模糊熵筛选出噪声占主导的模态分量,将其进行 Savitzky-Golay(SG)滤波处理。对滤波后的分量和有用分量叠加重构去噪后的信号,通过最小二乘支持向量机(LSSVM)对海杂波信号进行预测并验证去噪效果。仿真结果表明,本文所提算法能够有效抑制噪声干扰,去噪后的均方根误差(RMSE)为 0. 000 29,比去噪前的均方根误差 0. 012 3 降低了两个数量级。

英文摘要:

The problem of determining the parameters of variational mode decomposition (VMD) is analyzed, and a sea clutter denoising method based on optimized variational mode decomposition (VMD) is proposed. The whale optimization algorithm (WOA) was used to optimize the number of modes K and penalty parameters, and the original sea clutter signal was decomposed adaptively to remove the modal component with low variance contribution rate (VCR). The modal component dominated by noise was screened by combining with fuzzy entropy and processed by Savitzky-Golay (SG) filtering. The least square support vector machine (LSSVM) was used to predict the sea clutter signal and verify the denoising effect for the signal reconstructed by superposition of filtered component and useful component. Simulation results show that the proposed algorithm can effectively suppress noise interference, and the root mean square error (RMSE) after denoising is 0. 000 29, which is two orders of magnitude lower than the root mean square error (RMSE) before denoising is 0. 012 3.

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