局部均值分解的新小波阈值去噪法及其应用

为了克服小波硬、软阈值函数本身存在的缺点,该文在硬、软阈值函数的基础上,提出一种新的小波阈值函数,并且基于局部均值分解的原理,构造了基于局部均值分解的新小波阈值去噪法。仿真数据对比分析表明,与单纯采用小波阈值去噪法、经验模态分解(EMD)滤波去噪法及局部均值分解滤波去噪法相比,该文方法的去噪效果更好,可有效提高信号的信噪比。利用本文方法对液体静力水准仪获取的高速铁路某桥梁实际监测数据进行去噪处理,结果表明去除监测数据中噪声的同时亦可保留变形细部特征。该文方法可为工程中的非线性、非平稳监测数据去噪提供参考和借鉴。

New wavelet threshold de-noising method based on local mean decomposition and its application

In order to overcome the shortcomings of the wavelet hard and soft threshold functions,this paper proposed a new wavelet threshold function based on the hard and soft threshold functions.Based on the principle of local mean decomposition,the new wavelet threshold de-noising method based on local mean decomposition was constructed.The simulation data comparison analysis showed that compared with the simple wavelet threshold de-noising method,empirical mode decomposition(EMD)filtering de-noising method and local mean decomposition filtering de-noising method,the de-noising effect of the proposed method was better,and the signal-to-noise ratio(SNR)of the signal could be effectively improved.The proposed method was used to denoise the actual monitoring data collected by a hydrostatic leveling on a high-speed railway bridge,and the result showed that more deformation detail could be retained while removing noise from the monitoring data,which could provide reference for the de-noising of non-stationary monitoring data in engineering.