The Application Research Of Wavelet Threshold Denoising Method In Processing Of Building's Deformation Monitoring Data

Under the influence of the observation environment, equipment and other factors, there is a certain amount of error between observation data and the true distortion of the building. The noise (error) will affect the resolution and stability of the system. And if noise (error)is serious, it will drown the normal signal, leading to not work. requiring original sign to be detected (such as Kalman filtering),while the wavelet transform mainly effective on long-term sustainability of the periodic signal in the traditional noise filtering method. which the wavelet transform suitable for not much prior knowledge of signal which formed by the short high frequency components and long low-frequency composition, so the wavelet analysis method can be considered for processing of monitoring data of building deformation (This signal is equivalent to the observed data, and noise is observation error) to remove errors to get the data which is denoised. the main contents include:Based on the theory of wavelet transform,a new method by using wavelet transform to eliminate the noise in observed data is introduced and a calculation example is also given. the characteristics of two traditional wavelet compromise methods,referred to as the soft-threshold method and the hard-threshold method,were analyzed in the wavelet domain.Three new threshold methods of compromise for soft/hard thresholds,a new compromise for soft/hard thresholds,the modulus squared model,based on the standard soft/hard threshold method,the new approach avoids the discontinuity of the hard-threshold method and also decreases the fixed bias between the estimated wavelet coefficients and the decomposed wavelet coefficients of the soft-threshold method.This article describes a new method:using fuzzy control filter to deal with Noisy signal first,Thereby reducing the variance of white noise,Then use hard threshold method to remove the noise of processed signal. The results show that denoise using wavelet transform is efficient and reliable and is particularly suitable for processing monitoring data of building deformation due to its sensitivity to the noise of data and the ability to identify useful information. And in the practical application of the wavelet function types must pay great attention to specific issues with considering the type of wavelet function, threshold selection, wavelet decomposition level and other factors.