一种基于多分形奇异谱的噪声奇异性分析新方法

New method of analyzing singularities in noise based on singularity spectrum

在介绍了噪声中的奇异性以及其全局奇异性的表征方法———多分形奇异谱的基础上,提出并实现了一种基于子波分解模极大的计算噪声多分形奇异谱的方法,通过对仿真信号验证,证明了该方法准确可靠。在对实测信号分析中,也发现该方法得到的多分形奇异谱比功率谱更能充分刻画噪声信号的特性。

Singularity analysis is one of the most promising new approaches for extracting hidden information from noise. Singularity spectrum is a statistical method to show global singularities distributions in noise. We introduced an algorithm based on wavelet transform modulus maxima to get singularity spectrum. The experiment on emulation noises shows that the new algorithm is accurate and feasible. Also we found out that singularity spectrum had some advantage over the traditional analysis method： power spectrum.