有噪复杂度寻踪的新算法

A novel algorithm for noisy complexity pursuit

复杂度寻踪是投影寻踪向时间序列数据,即具有时间结构信号的扩展。该方法是和具有时间依赖特性的源信号的盲分离和独立成分分析紧密联系的。在源信号是具有时间依赖特性和存在高斯噪声的情况下,现有的有噪复杂度寻踪算法没有给出自回归系数的估计方法,影响了算法的实际应用,提出了有噪复杂度寻踪的新算法,该算法给出了自回归系数的估计方法。对自然图像和人工信号的仿真表明了提出算法的有效性,和现有的盲源分离算法相比较,提出算法具有好的信号分离性能。

Complexity pursuit is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and independent component analysis （ICA）.In this paper,we consider the estimation of the data model of ICA when Gaussian noise is present and the components are time dependent.The separation result is affected because existing blind source separation algorithms do not give the method to estimate the autoregressive coefficients.A novel algorithm for noisy complexity pursuit is proposed.The algorithm gives the method to estimate autoregressive coefficients.Computer stimulations with natural images and artificial signals indicate the validity of the proposed algorithm.Moreover,comparisons with existing blind source separation algorithms further show the better performance of the proposed algorithm.