Blind separation algorithm for noise sources via uniformity estimation

Blind separation algorithm is a powerful method on identifying noise sources without any prior knowledge of sources and paths. The independence of source signals is usually a main requirement for blind separation algorithm. However, the independence can hardly be checked up because the probability density function is unknown or difficult to estimate. For solving this problem, the relationship of independence of random variable and joint distribution of its probability distribution function is illuminated theoretically. Then, an algorithm of estimating the new independence index-uniformity is presented by a simplified estimation method, and the corresponding blind separation algorithm is described. The feasibility of uniformity and blind separation algorithm were demonstrated by the mixed signals' separation experiment of electromotor and seawater pump, and the experiment results, including the separation effects and time cost, show that the proposed method is superior to the existing semi-entropy blind separation algorithms.