基于完备经验模态分解自相关消噪技术的GNSS高精度动态变形监测研究

为了更好地消除混杂在变形序列中的噪声，利用完备经验模态分解（CEEMD）将形变信号自适应分解为不同尺度的振动模态。针对分解分量中信号和噪声区分标准不唯一的问题，构造一种CEEMD与自相关分析相结合的去噪算法，实现有效信号和随机信号的分离。将该算法应用在仿真实验和GNSS变形监测实测数据处理中，并与传统的小波去噪方法进行比较。结果表明，该算法避免了小波基选择带来的影响。

GNSS High Precision Dynamic Deformation Monitoring Research Based on CEEMD Auto Correlation De-Noising Technique

In order to eliminate noise in the deformation sequence, the signal is decomposed into different scales using the CEEMD method. Aiming at the problem that the signal and noise distinguish criteria are not unique, a de-noising algorithm, based on the combination of CEEMD and auto correlation analysis, is proposed to separate the signals and random signals. The algorithm is applied to a simulation experiment and to GNSS deformation monitoring data, and compared with traditional wavelet de-noising methods. Compared with the wavelet method, the algorithm has a better effect.