一种基于信号相关性检测的自适应小波变换及应用

为了克服传统小波变换的不足,提出了一种用样本相关性检测信号特征的自适应小波变换降噪方法.该方法以第二代小波变换为基础,在小波变换时提供多组备选的预测器和更新器,用变换样本与相邻样本之间的相关性来检测信号的局部特征,并根据相关系数的大小来确定每一尺度上的每个样本的最佳预测器和更新器,使小波能够较好地适应信号的局部特征.模拟实验和工程应用的结果表明,该方法克服了传统小波降噪方法丢失原始信号局部信息的缺陷,不仅可以有效地去除原始信号中的噪声,而且能够保留原始信号的局部特征.

Adaptive Wavelet Transform Using Signal Correlation Detecting and Its Application

In order to overcome the limitation of the classical wavelet transform, an adaptive wavelet transform denoising method was proposed which uses the correlation between samples to detect features of signal. On the basis of the second generation wavelet transform, several sets of predictors and updaters were prepared to be selected in the transform. Local features of the signal on each level were examined by using the correlation between the transforming sample and its neighbors. According to the magnitude of the correlation factors, an optimal predictor and an optimal updater were chosen for the transforming sample. So wavelets can nicely fit the local features of the original signal. The simulation experiments and engineering application showed that the proposed method can overcome the defect of classical wavelet denoising method that may lose some local information of the original signal. The present method can not only remove noise from the original signal effectively, but also retain the local information of the original signal.