桥梁GNSS-RTK变形监测数据的多滤波联合去噪处理

针对桥梁GNSS-RTK变形监测中多路径效应和随机噪声的影响，提出了一种基于Chebyshev滤波和自适应噪声的完备集合经验模态分解（CEEMDAN），以及小波阈值（WT）降噪技术的多滤波联合降噪方法。该方法首先对监测信号实施Chebyshev滤波抑制多路径效应；然后进行CEEMDAN分解，基于自相关性分析，对噪声IMF分量进行WT降噪去除随机噪声。本文以天津海河大桥GNSS-RTK变形监测作为试验，对监测数据进行多滤波降噪处理。结果表明：本文所提的多滤波降噪方法能有效抑制多路径效应和随机噪声，GNSS-RTK与多滤波降噪相结合的方法能够准确识别桥梁真实动态位移，为桥梁GNSS-RTK监测数据降噪处理提供了一种良好的途径。

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

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.