EAMUSE: an extended algorithm for multiple sources extraction
Ying‐Chang Liang, Yanda Li, Xian‐Da Zhang · 2002
This paper addresses the problem of multiple source signals separation in noise. As contrasted to the reported studies in which white noise in different sensors with same noise covariance was assumed, the additive noise sensors considered in this paper have different noise covariance. An extended algorithm for multiple sources extraction (EAMUSE) is proposed. The effectiveness of our approach is demonstrated through standard simulation examples.