一种基于小波变换的静电探测信号自适应去噪方法

为了实现对静电目标信号的提取和正确识别,本文对静电探测信号的形式以及干扰信号的形式进行了分析.提出采用基于Stein无偏似然估计(SURE)的小波软阈值去噪方法对静电探测信号进行处理.对SURE去噪方法的基本原理和改进方案进行了介绍.提出了自适应调整学习速率的方法,可以在提高运算速度的基础上得到信号的无偏似然估计.通过编写Matlab仿真程序进行验证,获得了较好的去噪结果.通过对几种软阈值去噪方法的仿真结果进行比较,可以看出该方法适用于频率成分较复杂的静电探测信号处理.

Adaptive Denoising of Electrostatic Detecting Signals Based on Wavelet Transform

The forms of the electrostatic detecting signal and the interfering signal are analyzed to extract and identify the electrostatic signals of targets correctly. The wavelet shrinkage method for denoising based on Stein's unbiased risk estimate (SURE) is provided for signal processing in electrostatic detecting. The principle and modified scheme of denoising method based on SURE are explained. The means of adaptive adjustment of learning rate is introduced, and then the unbiased risk estimate of signal can be obtained with high operating speed. This method can be verified with the simulative program by using Matlab, and the denoising results are proper. In comparison with other wavelet shrinkage methods, it is concluded that the means adopted in this paper is suitable to process the electrostatic detecting signal which has complicated frequency components.