基于ICEEMDAN与环境负载的GNSS坐标时序非线性形变去除

王勇; 曹慧鹏; 李锁; 闫勇; 杨军

doi:10.12265/j.gnss.2021092602

基于ICEEMDAN与环境负载的GNSS坐标时序非线性形变去除

doi: 10.12265/j.gnss.2021092602

王勇^{1, 2, ,},
曹慧鹏¹,
李锁³,
闫勇³,
杨军¹

1.
天津城建大学地质与测绘学院, 天津 300384
2.
中国科学院精密测量科学与技术创新研究院大地测量与地球动力学国家重点实验室, 武汉430077
3.
天津市地质工程勘测设计院有限公司, 天津 300191

基金项目: 中国科学院精密测量科学与技术创新研究院大地测量与地球动力学国家重点实验室开放基金资助项目(SKLGED-2021-2-4)；天津市科技计划项目(21KPHDRC00070)；天津市教委科研计划项目(2021ZD001)

详细信息

作者简介:
王勇：(1978—)，男，博士，教授，研究方向为GNSS数据处理与应用

曹慧鹏：(1996—)，男，硕士，研究方向为GNSS数据处理与应用

李锁：(1984—)，男，高级工程师，研究方向为地理信息工程研究与应用

闫勇：(1972—)，男，硕士，高级工程师，主要从事地质信息化建设与研究

通信作者:
王勇 E-mail：wangyongjz@126.com

中图分类号: P227；P228.49
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-26
- 网络出版日期: 2022-04-18

Time series nonlinear deformation removal of GNSS coordinates based on ICEEMDAN and environmental load

WANG Yong^{1, 2
, ,},
CAO Huipeng¹,
LI Suo³,
YAN Yong³,
YANG Jun¹

1.
School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China
2.
State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan 430077, China
3.
Tianjin Geological Engineering Survey and Design Institute Co. Ltd., Tianjin 300191, China

摘要

摘要: 非线性形变影响全球卫星导航系统(GNSS)坐标时序精度. 采用改进的自适应噪声总体集合经验模态分解(ICEEMDAN)和环境负载改正相结合的方法开展GNSS测站非线性形变去除研究. 首先使用GMIS软件将GNSS坐标时序补充完整并去除粗差，然后使用ICEEMDAN方法对GNSS坐标时序进行分解，使用排列熵算法选取包含噪声和非线性形变的高频分量，最后使用环境负载对高频分量进行去除，利用经验模态分解(EMD)方法和环境负载结合的方法进行去除效果对比. 研究结果表明：非线性形变去除后的GNSS坐标时序均方根(RMS)变化各有区别，垂向(U)方向最为明显，最大值达6.715 mm，东(E)方向次之，北(N)方向最小；ICEEMDAN方法和环境负载改正结合后N方向的非线性形变全部得到了削弱，E方向的非线性形变有75％得到了削弱，U方向的非线性形变有62.5％得到了削弱，其改正效果优于EMD方法和环境负载结合的改正效果.
- 改进的自适应噪声总体集合经验模态分解(ICEEMDAN) /
- 经验模态分解(EMD) /
- 环境负载 /
- 坐标时间序列
Abstract: Nonlinear deformation affects Global Navigation Satellite System (GNSS) coordinate timing accuracy. In this paper, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method and environmental load correction are combined to study the nonlinear deformation removal of GNSS stations. Firstly, use GMIS software to complete the GNSS coordinate timing and remove the gross error. Then use ICEEMDAN method to decompose the GNSS coordinate timing, and use the permutation entropy algorithm to select the high frequency components containing noise and nonlinear deformation. Finally, the environmental load is used to remove the high-frequency components, and the removal effect is compared with the empirical mode decomposition (EMD) method and the environmental load method. The research results show that the root mean squared (RMS) of GNSS coordinate time series after nonlinear deformation removal changes different, and the up (U) direction is the most obvious, with the maximum value of 6.715 mm, followed by the E direction and the north (N) direction. After combining ICEEMDAN met-hod and environmental load,the nonlinear deformation in N direction was weakened 75% of the nonlinear deformation in east (E) direction was weakened, and 62.5% of the nonlinear deformation in U direction was weakened. The correction effect was better than the combination of EMD method and environmental load.

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图 1 完整去噪后站点时间序列

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2 ICEEMDAN与EMD分解IMF分量PE值比较

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图 3 BJYQ站环境负载改正前后对比图

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表 1 测站时间序列经环境负载改正后RMS变化 mm

环境负载	方向	BJFS		BJGB		BJSH		BJYQ
环境负载	方向	EMD	ICEEMDAN	EMD	ICEEMDAN	EMD	ICEEMDAN	EMD	ICEEMDAN
海潮负载	N	1.3267	1.1604	1.1412	0.9975	1.1892	1.5130	1.2028	1.1655
	E	1.8554	1.5948	1.8902	1.6972	1.6264	1.1498	1.7398	1.5218
	U	6.4430	6.3967	5.6277	6.0264	7.6631	5.9743	7.3275	6.2579
大气压潮	N	1.1182	0.8917	1.0247	0.8400	0.9174	0.8501	0.8945	0.8565
	E	1.4973	0.8863	1.3292	1.0616	0.7747	0.8555	1.1685	0.8720
	U	5.9019	5.8407	5.5502	5.9285	6.3780	5.9729	7.0344	5.9540
地球极移	N	1.0844	0.8610	0.9911	0.8162	0.8976	0.8403	0.8699	0.8349
	E	1.5686	1.0066	1.4219	1.1199	0.8364	0.9487	1.2529	0.9479
	U	6.0097	6.1582	5.7058	6.2175	7.1230	6.7150	7.4728	6.5710
海洋极潮	N	1.0913	0.8677	0.9989	1.0006	0.9160	0.8446	0.8851	0.8346
	E	1.4718	0.8769	1.3089	0.8173	0.7336	0.8190	1.1337	0.8339
	U	5.9649	5.9020	5.0370	5.4494	5.7184	5.2696	6.7585	5.5327

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表 2 测站时间序列经环境负载改正后WRMS变化 mm

环境负载		BJFS		BJGB		BJSH		BJYQ
环境负载		EMD	ICEEMDAN	EMD	ICEEMDAN	EMD	ICEEMDAN	EMD	ICEEMDAN
海潮负载	N	0.0598	0.2043	−0.0298	0.0823	0.0551	0.1029	−0.1283	0.0952
	E	0.1190	−0.1382	0.0504	−0.0930	0.0371	0.0550	0.4177	0.1040
	U	0.3399	1.2806	−1.1154	−0.5922	0.9594	−0.0874	−0.2805	0.5085
大气压潮	N	0.0851	0.2304	0.0003	0.1137	0.0712	0.1222	−0.0869	0.1359
	E	0.3120	0.0606	0.2291	0.0857	0.1807	1.1985	0.5265	0.2124
	U	0.7383	1.9606	−1.0371	−0.4507	0.9631	−0.2797	0.3817	0.4633
地球极移	N	0.0829	0.2278	−0.0010	0.1113	0.0779	0.1281	−0.0959	0.1267
	E	0.2226	−0.0286	0.1405	−0.0016	0.1028	0.1207	0.4186	0.1052
	U	0.8168	1.5525	−1.2795	−1.0264	−0.0481	−1.0761	−0.2749	0.2247
海洋极潮	N	0.0554	0.2000	−0.0239	0.0887	0.0535	0.1048	−0.1174	0.1057
	E	0.2744	0.0225	0.1966	0.0538	0.1549	0.1727	0.4725	0.1579
	U	0.8577	1.9184	−0.4941	0.0788	1.5356	0.5682	0.2036	1.0991

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参考文献(20)

[1]	REBISCHUNG P, ALTAMIMI Z, RAY J, et al. The IGS contribution to ITRF2014[J]. Journal of geodesy, 2016, 90(7): 611-630. DOI: 10.1007/s00190-016-0897-6
[2]	DENG L S, LI Z, WEI N, et al. GPS-derived geocenter motion from the IGS second reprocessing campaign[J]. Earth planets and space, 2019, 71(1): 74-91. DOI: 10.1186/s40623-019-1054-2
[3]	姜卫平, 李昭, 刘鸿飞, 等. 中国区域IGS基准站坐标时间序列非线性变化的成因分析[J]. 地球物理学报, 2013, 56(7): 2228-2237. DOI: 10.6038/cjg20130710
[4]	DENG L S, JIANG W P, LI Z, et al. Assessment of second- and third-order ionospheric effects on regional networks: case study in China with longer CMONOC GPS coordinate time series[J]. Journal of geodesy, 2017, 91(2): 207-227. DOI: 10.1007/s00190-016-0957-y
[5]	明锋, 杨元喜, 曾安敏, 等. 中国区域IGS站高程时间序列季节性信号及长期趋势分析[J]. 中国科学(地球科学), 2016, 46(6): 834-844.
[6]	GU Y C, YUAN L G, FAN D M, et al. Seasonal crustal vertical deformation induced by environmental mass loading in mainland china derived from GPS, GRACE and surface loading models[J]. Advances in space research, 2017, 59(1): 88-102. DOI: 10.1016/j.asr.2016.09.008
[7]	姜卫平, 夏传义, 李昭, 等. 环境负载对区域GPS基准站时间序列的影响分析[J]. 测绘学报, 2014, 43(12): 1217-1223.
[8]	陈岸, 吴毅江, 林洪栋, 等. 华北区域GNSS基准站高程时间序列的非线性变化分析[J]. 全球定位系统, 2020, 45(6): 86-91.
[9]	尹涛涛, 王潜心. 一种削弱区域CORS站坐标时间序列非线性变化的方法[J]. 大地测量与地球动力学, 2021, 41(7): 695-699.
[10]	张双成, 何月帆, 李振宇, 等. EMD用于GPS时间序列降噪分析[J]. 大地测量与地球动力学, 2017, 37(12): 1248-1252.
[11]	蒋志浩, 张鹏, 秘金钟, 等. 顾及有色噪声影响的CGCS2000下我国CORS站速度估计[J]. 测绘学报, 2010, 39(4): 355-363.
[12]	邓连生. GPS坐标时间序列中未模型化误差和环境负载的影响研究[J]. 测绘学报, 2018, 47(11): 136.
[13]	SUN H P, DUCARME B, DEHANT V. Effect of the atmospheric pressure on surface displacements[J]. Journal of geodesy, 1995, 70(3): 131-139. DOI: 10.1007/BF00943688
[14]	COLOMINAS M A, SCHLOTTHAUER G, TORRES M E. Improved complete ensemble EMD: a suitable tool for biomedical signal processing[J]. Biomedical signal processing and control, 2014, 14(1): 19-29. DOI: 10.1016/j.bspc.2014.06.009
[15]	卿龙, 袁林果, 郝景恺, 等. 台湾连续GPS网主成分空间滤波分析[J]. 大地测量与地球动力学, 2020, 40(8): 838-842.
[16]	JIANG W P, LI Z, VAN D T, et al. Comparative analysis of different environmental loading methods and their impacts on the GPS height time series[J]. Journal of geodesy, 2013, 87(7): 687-703. DOI: 10.1007/s00190-013-0642-3
[17]	LIU N, DAI W J, SANTERRE R, et al. A MATLAB-based kriged kalman filter software for interpolating missing data in GNSS coordinate time series[J]. GPS solutions, 2017, 22(1): 635-639. DOI: 10.1007/s10291-017-0689-3
[18]	FAHIMAH A AL-A, ALHAJRAF A. Prediction of non-methane hydrocarbons in kuwait using regression and bayesian kriged kalman model[J]. Environmental & ecological statistics, 2012, 19(3): 393-412. DOI: 10.1007/s10651-012-0192-5
[19]	BANDT C, POMPE B. Permutation entropy: a natural complexity measure for time series[J]. Physical review letters, 2002, 88(17): 174102. DOI: 10.1103/PhysRevLett.88.174102
[20]	郑近德, 程军圣, 杨宇. 改进的EEMD算法及其应用研究[J]. 振动与冲击, 2013, 32(21): 21-26. DOI: 10.3969/j.issn.1000-3835.2013.21.004