Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique
Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique
复制标题
结合变分模态分解、机器学习在线优化和区间阈值技术的信号去噪方法
DOI：
10.1109/access.2020.3043182
复制
发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Chen Sicheng
中科院分区：
文献类型：
--
作者：
Liu Zhenxing;Chang Jianhua;Li Hongxu;Zhang Luyao;Chen Sicheng
随着距离的增加，激光雷达信号的信噪比迅速下降，严重影响了激光雷达探测技术的应用。变分模式分解（VMD）在处理噪声方面具有最优性，但模式数K和惩罚参数α必须预先设定。为此，提出了一种结合VMD、机器学习在线优化（MLOO）和区间阈值（IT）技术的激光雷达信号去噪方法VMD-MLOO-IT。该方法定义了新的适应度函数来评价基于VMD的去噪效果，并通过MLOO建立的模型来选择最优参数。此外，它被用来处理恢复的信号。实验结果表明，该方法优于其他经验模式分解和基于VMD的去噪方法。
The signal-to-noise ratio of lidar signals decreases rapidly with an increase in distance, which seriously affects the application of lidar detection technology. Variational mode decomposition (VMD) has performed optimality in dealing with noise, but the number of modes, K, and the penalty parameter, alpha has, must be preset. Therefore, a novel lidar signal denoising method that combines VMD with machine learning online optimization (MLOO) and the interval thresholding (IT) technique, named VMD-MLOO-IT, is proposed in this article. The proposed method defines new fitness functions to evaluate the result of VMD-based denoising, and selects the optimal parameters by the model which development by MLOO. In addition, IT is used to deal with the recovered signal. The experimental results demonstrate the superiority of the presented method over the other empirical mode decomposition-based and VMD-based denoising methods.
登录
查看更多内容
影响因子：
4.6
作者：
Wigley PB;Everitt PJ;van den Hengel A;Bastian JW;Sooriyabandara MA;McDonald GD;Hardman KS;Quinlivan CD;Manju P;Kuhn CC;Petersen IR;Luiten AN;Hope JJ;Robins NP;Hush MR
通讯作者：
Hush MR
影响因子：
3.3
作者：
Gomez, Julian L.;Velis, Danilo R.
通讯作者：
Velis, Danilo R.
影响因子：
3.5
作者：
Ko, Jonathan;Fox, Dieter
通讯作者：
Fox, Dieter
影响因子：
3.8
作者：
Chen Li;Zengxin Pan;Feiyue Mao;W. Gong;Shihua Chen;Q. Min
通讯作者：
Chen Li;Zengxin Pan;Feiyue Mao;W. Gong;Shihua Chen;Q. Min
DOI：
10.1007/s00340-018-7055-1
发表时间：
2018-08
期刊：
Applied Physics B
影响因子：
--
作者：
Hongxu Li;Jianhua Chang;Fan Xu;Binggang Liu;Zhenxing Liu;Lingyan Zhu;Zhenbo Yang
通讯作者：
Hongxu Li;Jianhua Chang;Fan Xu;Binggang Liu;Zhenxing Liu;Lingyan Zhu;Zhenbo Yang