重构小波阈值函数在信号去噪中的应用与研究

Application and Research on Reconstructed Wavelet Threshold Functionin Signal Denoising

实际工程应用中,通过物理手段采集到的信号都带有噪声信息,这样就会淹没很多有用信号,传统的"带通、低通、高通"滤波技术往往显得无能为力,因此提取有用的特征信息就需要对原始信号进行去噪。常用的小波阈值去噪(硬、软阈值函数)方法,含有诸多缺点,本文在其基础上重新构造了一个新的阈值函数。在MTALAB(2014a)环境下,用传统的硬、软阈值函数及重构阈值函数对混入高斯白噪声信号进行去噪仿真分析,结果表明,重构的阈值函数处理的信号效果更好,更清晰。

In practical engineering applications, the signals collected by physical methods have noise information and this will submerge many useful signals used for analyzing the system characteristics. The traditional filtering techniques, such as band pass, low pass, high pass, seems powerless, so extracting useful feature information requires denoising of the original signal. Common wavelet threshold denoising methods have many shortcomings. In this paper, a threshold function is re-constructed on the basis of wavelet threshold denoising methods. To realize the simulation of denoising the Gaussian white noise, in the Mtalab （2014a） environment, the conventional hard, soft threshold function and the new threshold function are respectively used. Results show that signals become better and clearer by using the new threshold function.