基于VMD算法的GNSS-MR潮位反演方法

doi:10.13474/j.cnki.11-2246.2024.0404

摘要/Abstract

摘要： GNSS-MR作为一种新兴的遥感技术,利用GNSS信号在传播过程中的多路径效应反演地表参数,近年来在潮位反演领域得到广泛应用。但传统GNSS-MR技术在利用低阶多项式提取SNR残差序列时,由于提取方式过于粗糙,没有充分考虑GNSS干涉信号的特征,导致在卫星高度角较大时,提取的SNR残差序列含有部分非干涉信号,进而直接导致在LSP频谱分析时出现虚假峰值现象,造成反演失真。本文利用变分模态分解(VMD)算法可将合成信号自适应分解成一系列具有特殊物理意义的IMF分量的优势,提出了基于VMD算法的改进GNSS-MR潮位反演方法。通过对美国华盛顿州SC02站100 d的潮位反演结果表明,本文提出的3个基于VMD算法的潮位方法中,将VMD算法的分解层数设置为5的VMD-5反演方法具有最高的反演精度与稳定性,其RMSE为33.74 cm,相关系数为0.920 5,异常反演点数为40;较传统反演方法、VMD-4反演方法、VMD-6反演方法的RMSE分别降低89.80%、10.81%、48.48%;相关系数分别增加305.40%、2.30%、25.99%;异常反演点数分别降低83.67%、6.98%、14.89%。VMD-5的GNSS-MR潮位反演方法能够有效降低传统反演方法存在的异常反演点数量,显著提高反演精度及时间分辨率,能够实现在卫星高度角较大时进行长期高精度的、高稳定性、高时间分辨率的潮位反演。

关键词: GNSS-MR, 反演失真, 变分模态分解, 信噪比

Abstract: As an emerging remote sensing technology, GNSS-MR utilizes the multipath effect of GNSS signals during propagation to retrieve surface parameters. It has been widely applied in the field of tidal inversion in recent years. However, traditional GNSS-MR techniques, when extracting the SNR residual sequence using low-order polynomials, often employ a coarse extraction method that does not fully consider the characteristics of GNSS interference signals. As a result, when the satellite elevation angle is large, the extracted SNR residual sequence contains non-interference components, leading to false peak phenomena during LSP frequency spectrum analysis and resulting in inversion distortion. In this study, we leverage the advantages of the variational mode decomposition (VMD) algorithm to adaptively decompose the synthesized signal into a series of intrinsic mode functions (IMFs) with specific physical meanings. We propose an improved GNSS-MR tidal inversion method based on VMD. The tidal inversion results from the SC02 station in Washington State over a period of 100 days indicate that among the three tidal inversion methods based on VMD proposed in this paper, the inversion accuracy and stability are highest when the number of IMFs obtained from VMD decomposition is set to 5 (referred to as VMD-5). Its RMSE is 33.74 cm, correlation coefficient is 0.920 5, and the number of anomalous inversion points is 40. Compared with the traditional inversion method, VMD-4 inversion method, and VMD-6 inversion method, the RMSE is reduced by 89.80%, 10.81% and 48.48% respectively. The correlation coefficients are increased by 305.40%, 2.30% and 25.99%, respectively. And the number of anomalous inversion points is reduced by 83.67%, 6.98% and 14.89%, respectively. The proposed VMD-5-based improved GNSS-MR tidal inversion method effectively reduces the number of anomalous inversion points in traditional inversion methods, significantly improves the accuracy and temporal resolution of inversions, and enables long-term, high-precision, high-stability, and high-temporal-resolution tidal inversion even at large satellite elevation angles.

Key words: GNSS-MR, inversion distortion, VMD, SNR

中图分类号:

P229
P237

简灵活, 王新鹏, 陶庭叶. 基于VMD算法的GNSS-MR潮位反演方法[J]. 测绘通报, 2024, 0(4): 18-22,34.

JIAN Linghuo, WANG Xinpeng, TAO Tingye. GNSS-MR tidal level inversion based on VMD[J]. Bulletin of Surveying and Mapping, 2024, 0(4): 18-22,34.

参考文献

[1] 张通,俞永强,效存德,等.IPCC AR6解读:全球和区域海平面变化的监测和预估[J].气候变化研究进展,2022,18(1):12-18.
[2] 蒋光伟.基于GNSS的区域参考框架维持方法研究[D].西安:长安大学,2022.
[3] 郑凯.单站GNSS宽频带地震地表位移监测方法和关键技术研究[D].武汉:武汉大学,2020.
[4] 张纯志.浅谈GNSS技术在煤田地质勘探中的应用[J].全球定位系统,2016,41(3):97-100.
[5] 孙鹏.GNSS实时水汽反演关键算法研究[D].徐州:中国矿业大学,2022.
[6] 徐小静.基于GNSS的崇明区大气可降水量反演及台风监测研究[D].上海:华东师范大学,2022.
[7] 刘根友,高铭,尹翔飞,等.基于GNSS精密单点定位的高精度云平台授时[J].测绘学报,2022,51(8):1736-1743.
[8] 谭俊雄.基于PPP的高精度GNSS授时接收机技术[D].武汉:武汉大学,2019.
[9] 吴昊舰,刘立龙,章传银,等.变分模态分解和机器学习融合的GNSS-IR土壤湿度反演[J].测绘科学,2022,47(7):27-34.
[10] 陈亮宇,安家春,王泽民,等.同天线不同接收机的GNSS-IR雪深反演分析[J].武汉大学学报(信息科学版),2023,48(8):1312-1321.
[11] PENG Dongju,HILL E M,LI Linlin,et al.Application of GNSS interferometric reflectometry for detecting storm surges[J].GPS Solutions,2019,23(2):47.
[12] 吕铮,冯威,黄丁发.GNSS SNR信号反演大坝水位变化[J].大地测量与地球动力学,2020,40(2):146-151.
[13] LARSON K M,LÖFGREN J S,HAAS R.Coastal sea level measurements using a single geodetic GPS receiver[J].Advances in Space Research,2013,51(8):1301-1310.
[14] NAKASHIMA Y,HEKI K.GPS tide gauges using multipath signatures[J].Journal of the Geodetic Society of Japan,2014,59:157-162.
[15] LÖFGREN J S,HAAS R.Sea level measurements using multi-frequency GPS and GLONASS observations[J].EURASIP Journal on Applied Signal Processing,2014,2014(1):50.
[16] ZHANG Shuangcheng,LIU Kai,LIU Qi,et al.Tide variation monitoring based improved GNSS-MR by empirical mode decomposition[J].Advances in Space Research,2019,63(10):3333-3345.
[17] 王瑞芳.利用EMD进行GNSS-MR潮位监测研究[J].测绘通报,2020(10):114-117.
[18] HU Yuan,YUAN Xintai,LIU Wei,et al.Snow depth estimation from GNSS SNR data using variational mode decomposition[J].GPS Solutions,2022,27(1):33.
[19] DRAGOMIRETSKIY K,ZOSSO D.Variational mode decomposition[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.