首页|参数优化变分模态分解的GNSS坐标时间序列降噪方法

参数优化变分模态分解的GNSS坐标时间序列降噪方法

扫码查看
原文链接
  • 万方数据
  • 维普
针对全球导航卫星系统(global navigation satellite system,GNSS)坐标时间序列中噪声成分难以有效滤除的问题,构建一种基于参数优化变分模态分解(variational mode decomposition,VMD)的降噪方法.该方法首先以排列熵结合互信息为适应度函数,利用灰狼优化(grey wolf optimization,GWO)算法自适应获取VMD的模态分解个数K和二次惩罚因子α的最优参数组合;然后将GNSS坐标时间序列分解为K个本征模态函数分量,并利用样本熵确定有效模态分量,将其重构为有效信号,从而实现信号与噪声的有效分离;最后,利用仿真信号和中国地壳运动观测网络的20个基准站的实测数据进行实验,将GWO-VMD方法与经验模态分解、小波降噪和基于VMD的降噪方法进行对比分析.结果表明,GWO-VMD方法能够更为有效地去除GNSS坐标时间序列中的噪声,且能较好地保留信号的原始特征,为后续的分析处理提供可靠数据.
GNSS Coordinate Time Series Denoising Method Based on Parameter-Optimized Variational Mode Decomposition
Objectives:In order to effectively filter out complex noise components in GNSS coordinate time series and extract effective signals,we construct a denoising method based on parameter-optimized varia-tional modal decomposition(VMD).Methods:First,the combination of permutation entropy and mutual information is used as fitness function,and the optimal parameter combination of the mode decomposition number K and the quadratic penalty factor α of VMD is obtained by using grey wolf optimization algorithm(GWO).Then the GNSS coordinate time series is decomposed into K eigen mode function components by VMD.Finally,the sample entropy is used to determine the effective modal component,which is recon-structed as an effective signal,so as to realize the effective separation of signal and noise.The GWO-VMD method is compared and analyzed with the empirical mode decomposition(EMD),wavelet denoising(WD)and IVMD methods by using the simulated signal and the measured data from 20 reference stations of the crustal movement observation network of China for experiments.Results:The simulated signal experi-ments show that the three denoising evaluation indexes of root mean square error,correlation coefficient and signal-to-noise ratio of GWO-VMD denoising signal are better than EMD,WD and IVMD methods.The experiments on the measured data show that the GWO-VMD method can reduce the amplitude of noise sig-nificantly.In terms of the velocity uncertainty of the reference station,the overall GWO-VMD method re-duces the velocity uncertainty better than the EMD,WD and IVMD methods.Conclusions:The GWO-VMD method can more effectively remove the noise from GNSS coordinate time series and better pre-serve the original characteristics of the signal,which can provide reliable data for subsequent analysis and processing.

GNSS coordinate time seriesVMDGWOpermutation entropymutual informationsam-ple entropysignal denoising

鲁铁定、何锦亮、贺小星、陶蕊

展开 >

东华理工大学测绘与空间信息工程学院,江西 南昌,330013

自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室,江西 南昌,330013

江西理工大学土木与测绘工程学院,江西 赣州,341000

GNSS坐标时间序列 VMD GWO 排列熵 互信息 样本熵 信号降噪

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金江西省自然科学基金江西省自然科学基金2022年度中国科协科技智库青年人才计划

4206107742374040420640014210402320202BABL21303320202BAB212010

2024

10.13203/j.whugis20220363

武汉大学学报(信息科学版)
武汉大学

武汉大学学报(信息科学版)

CSTPCD北大核心
影响因子：1.072
ISSN：1671-8860
年,卷(期)：2024.49(10)