噪声ICA模型去偏技术研究  

Study of bias removal techniques for noisy ICA

作　　者：张明亮[1] 王曙钊[1] 卢虎[1] 卞东亮[1] 王博[1] 杨勇[1] 

机构地区：[1]空军工程大学理学院,西安710051

出　　处：《计算机工程与应用》2013年第5期205-209,229,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61174194)

摘　　要：偏差去除算法通常假设高斯噪声条件下对普通ICA算法进行修正来消除噪声带来的影响。但是存在高斯噪声条件时,普通ICA算法对解混矩阵仍然可以辨识。故引入基于QR分解的RLS自适应噪声抵消算法和Fast-ICA算法相结合,只需对观测信号进行去噪处理,不需要对解混矩阵修正。并分别在同一噪声和相关噪声条件下做了仿真实验,与LMS-ICA算法进行了比较。仿真实验证明,该方法比后者效果显著。提出了用最小二乘算法计算分离信号的输出信噪比,作为评价算法的性能指标。Bias removal techniques usually remove the bias which is caused by noise in the method of correcting noiseless ICA. Nevertheless, the demixing matrix is still identifiable by using the noiseless/CA algorithms in the presence of additive Gaussian noise. So it is preferable to perform denoising with the vector of observed random variables, rather than to make modification to the demixing matrix. Then the QR-deeomposition-based Recursive Least Squares （RLS） adaptive noise cancellation （QRRLS） is introduced to combine with Fast-ICA algorithm. To test performance of the proposed approach, two experiments for it and the LMS-ICA algorithm are conducted on the conditions of identical noise and correlation noises respectively. By comparison, it shows that the proposed approach outperforms the latter, Moreover, in order to measure the performance availably, the least-squares method is adopted to calculate the Signal to Noise Ratio （SNR） of recovery signals.

关 键 词：噪声独立分量分析(ICA) 偏差去除技术 解混矩阵的可辨识性 递归最小二乘(RLS)自适应抵消 最小二乘算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]