Uncorrelated component analysis for blind source separation

The uncorrelated component analysis (UCA) of a stationary random vector pro cess consists of searching for a linear transformation that minimizes the t emporal correlation between its components. Through a general analysis we s how that under practically reasonable and mild conditions UCA is a solution for blind source separation. The theorems proposed in this paper for UCA p rovide useful insights for developing practical algorithms. UCA explores th e temporal information of the signals, whereas independent component analys is (ICA) explores the spatial information; thus UCA can be applied for sour ce separation in some cases where ICA cannot. For blind source separation, combining ICA and UCA may give improved performance because more informatio n can be utilized. The concept of single UCA (SUCA) is also proposed, which leads to sequential source separation.