A Primary Study Of Wavelet Technique For Signal Filtering

Wavelet analysis developed from Fourier analysis is a new time-frequency analysis tool which has favorable time-frequency localized and multi-resolution properties. Wavelet analysis has been widely applied in signal processing field and other fields. In this paper, a fast signal reconstruction algorithm and a signal de-noising algorithm from its wavelet transform modulus maxima are presented.In the process of signal collection, transform and transmission, the signal often mix noise ineluctably because the equipments, environments and even human errors. De-noising with the purpose of extracting desired information has been a crucial technique in signal processing. Based on the properties of wavelet transform and the statistical characteristics of noise, Donoho presented the threshold de-noising in wavelet transform domain. By selecting appropriate threshold and threshold function, the noise can be suppressed.According to the different characters of Wavelet coefficients of signal and noise, a new threshold selection method is put forward. The experimental results show that this method is efficient and practical. But there are discontinuity of hard-threshold function and biased estimation of soft-threshold function. In this paper, a new threshold function which overcomes the inherent disadvantages of hard-threshold and soft-threshold is proposed. Experiments show that the improved method has better performance of de-noising than the traditional methods.In short, the new thresholding algorithm can reach the expected outcome and has good stability and reliability.