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

Abstract: 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 ICA 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-decomposition-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.

Key words: noisy independent component analysis, bias removal techniques, identifiability of demixing matrix, Recursive Least Squares（RLS） adaptive noise cancellation, least-squares method