Selection method of optimal wavelet bases and de-nosing method of wavelet thresholds

Abstract

The invention provides a selection method of optimal wavelet bases. The method comprises following steps: selecting wavelet bases with smallest average errors for reconstructed signals and noised signals to ensure precision of signals during the process of de-nosing wavelet thresholds; considering sparse characteristics of different wavelet bases in wavelet domains as for concrete characteristics of different signals, picking best wavelet basis for concrete characteristics of output signals in order to benefit de-nosing of wavelet thresholds. The overall method is easy and quick so that rapid and optimized selection of wavelet bases is achieved; and de-nosing effect and signal processing efficiency in engineering applications are improved.

Description

technical field [0001] The invention relates to the technical field of radar, and more specifically relates to an optimal wavelet base selection method and a wavelet threshold value denoising method. Background technique [0002] Signal denoising is one of the classic problems in the field of signal processing. Traditional denoising methods mainly include linear filtering methods and nonlinear filtering methods, such as median filtering and wiener filtering. The shortcomings of traditional denoising methods are that the entropy of the signal after transformation is increased, the non-stationary characteristics of the signal cannot be described, and the correlation of the signal cannot be obtained. The wavelet denoising method overcomes the above shortcomings, and has good characteristics such as multi-resolution, low entropy, and flexibility, and has been more and more widely used. [0003] Wavelet threshold denoising is a simple and effective wavelet denoising method. Th...

AI Technical Summary

Problems solved by technology

However, for general signals, it is difficult to intuitively select the best wavelet base. At present, in practical engineering applications, most of them rely on empirical selection or comparison selection through a large number of trials. This method cannot determine which wavelet base to use when processing actual signals. The error generated is the smallest, and it is impossible to judge which wavelet base can obtain the best processing effect on the actual signal, and the efficiency of wavelet base selection is low, which reduces the efficiency of signal denoising processing in engineering applications. Affect the denoising effect

Images