摘要
针对GNSS时间序列去噪时,传统变分模态分解(VMD)重构方法高度依赖于分解过程中关键参数的设定,如模态分解数量、二次惩罚因子取值,以及重构时子序列选择等的问题,提出一种融合北方苍鹰优化(NGO)VMD与赫尔伯特(Hilbert)变换的去噪算法:通过NGO算法预测VMD分解过程中的潜在最佳参数,并结合Hilbert变换对分解所得的子序列进行能量分析;然后依据能量占比决定用于重构的子序列,从而实现高质量去噪。实验结果表明,所提出的NGO-VMD算法能够显著提升VMD的去噪效果,同时减小天(U)方向速度场的不确定度。
Aiming at the problem that during the denoising of global navigation satellite system(GNSS)time series,the traditional variational mode decomposition(VMD)reconstruction method highly relies on the setting of key parameters in the decomposition process,such as the number of mode decompositions,the value of the quadratic penalty factor,and the selection of sub sequences in the reconstruction,the paper proposed a denoising algorithm that integrates northern eagle optimization(NGO)VMD and Hilbert transform:by using the NGO algorithm to predict the potential optimal parameters in the VMD decomposition process,combined with the Hilbert transform to perform energy analysis on the decomposed subsequences;then,the subsequences were determined to be reconstructed based on the energy ratio for realizing high-quality denoising.Experimental results showed that the proposed NGO-VMD algorithm could significantly improve the denoising effect of VMD and reduce the uncertainty of the U-direction velocity field at the mean time.
作者
魏嘉林
殷敏
王式太
刘续
陆增扬
许正阳
曹拜
WEI Jialin;YIN Min;WANG Shitai;LIU Xu;LU Zengyang;XU Zhengyang;CAO Bai(College of Geomatics and Geoinformation,Guilin University of Technology,Guilin,Guangxi 541006,China;Guilin Institute of Surveying and Mapping,Guilin,Guangxi 541006,China;Guangxi Key Laboratory of Spatial Information and Geomatics,Guilin,Guangxi 541006,China;The Fourth Engineering Co.Ltd.of China Communications Construction Second Engineering Bureau,Luoyang,Henan 471013,China)
出处
《导航定位学报》
北大核心
2025年第5期155-164,共10页
Journal of Navigation and Positioning
基金
广西空间信息与测绘重点实验室基金项目(19-050-11-27)
广西高校中青年教师科研基础能力提升项目(2022KY1163)。
