Multi-filtering and joint denoising of bridge deformation data monitored by GNSS-RTK

doi:10.13474/j.cnki.11-2246.2020.0221

Abstract

Abstract: Aiming at the influence of multi-path effect and random noise in bridge GNSS-RTK deformation monitoring, a multi-filter combined denoising method based on Chebyshev filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold (WT) denoising technology is proposed. Chebyshev filter is used to suppress the multipath effect, then CEEMDAN decomposition is carried out, based on autocorrelation analysis, the noise IMF component is denoised by WT to remove random noise. As an experiment, the deformation of Haihe bridge in Tianjin city is monitored by GNSS-RTK, and the monitoring data are processed by multi-filter noise reduction. The results showe that:the multi-filter noise reduction method proposed in this paper can effectively suppress the multi-path effect and random noise, the combination of GNSS-RTK and multi-filter noise reduction can accurately identify the real dynamic displacement of the bridge, it provides a better way for noise reduction of bridge GNSS-RTK monitoring data.

Key words: bridge monitoring with GNSS-RTK, denoising analysis, adaptive noise, complete ensemble empirical mode decomposition, multi-filter joint

CLC Number:

P258

XIONG Chunbao, WANG Meng, YU Lina. Multi-filtering and joint denoising of bridge deformation data monitored by GNSS-RTK[J]. Bulletin of Surveying and Mapping, 2020, 0(7): 93-96,142.

References

[1] 安庆, 江博, 吴树森. 基于GNSS和传感技术的桥梁安全监测系统设计[J]. 全球定位系统, 2016, 41(5):107-112.
[2] 熊春宝, 陈雯, 翟京生, 等. GNSS RTK技术下海洋平台桩腿动态变形监测及数据处理[J]. 测绘通报, 2019(5):102-105.
[3] 余加勇, 邵旭东, 晏班夫, 等. 基于全球导航卫星系统的桥梁健康监测方法研究进展[J]. 中国公路学报, 2016, 29(4):30-41.
[4] WU Z H, HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceedings of the Royal Society of London A:Mathematical, Physical and Engineering Sciences, 2004, 460(2046):1597-1611.
[5] WU Z H, HUANG N E. Ensemble empirical mode decom-position:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41.
[6] 熊春宝, 张雪芳, 牛彦波, 等. 一种基于RTK-GNSS技术的大跨径悬索桥动态特性分析方法[J]. 天津大学学报(自然科学与工程技术版), 2019, 52(7):699-708.
[7] 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.
[8] 陈松, 柯敏勇, 刘海祥. 经验模态分解在桥梁检测中的应用[J]. 现代交通技术, 2009, 6(3):34-36.
[9] 王海, 岳东杰. 基于EMD的桥梁GPS动态监测数据处理[J]. 测绘工程, 2015, 24(11):68-72.
[10] 韩厚增, 王坚, 孟晓林. GPS与加速度计融合桥梁变形信息提取模型研究[J]. 中国矿业大学学报, 2015, 44(3):549-556.
[11] 余腾, 胡伍生, 吴杰, 等. 基于小波阈值去噪与EMD分解方法提取润扬大桥振动信息[J]. 振动与冲击,2019, 38(12):264-270.
[12] YE X J, CHEN X H, LEI Y X, et al. An integrated machine learning algorithm for separating the long-term deflection data of prestressed concrete bridges[J]. Sensors, 2018, 18(11):4070.
[13] 杨孟, 王瑾, 周西峰, 等. 基于CEEMD和小波包的降噪方法研究[J]. 南京邮电大学学报(自然科学版), 2018, 38(2):41-47.
[14] 张建文, 刘洋, 张大朋, 等. 基于CEEMDAN与小波自适应阈值的去噪新方法[J]. 电测与仪表, 2018, 55(10):14-18.
[15] 秦长彪. 基于GPS与加速度计的桥梁结构振动频率研究[D]. 徐州:中国矿业大学, 2016.
[16] 蔡东健, 岳建平, 张永超. 改进的小波阈值去噪方法在CORS数据处理中的应用研究[J]. 测绘通报, 2012(4):4-7.
[17] STIROS S C. Errors in velocities and displacements deduced from accelerographs:an approach based on the theory of error propagation[J]. Soil Dynamics and Earthquake Engineering, 2008, 28(5):415-420.
[18] JIANG C, ZHANG S B, CAO Y Z, et al. A robust fault detection algorithm for the GNSS/INS integrated navigation systems[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(1):12-24.