Title :
EAMUSE: an extended algorithm for multiple sources extraction
Author :
Liang, Ying-Chang ; Li, Yan-Da ; Zhang, Xian-Da
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
fDate :
30 Apr-3 May 1995
Abstract :
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
Keywords :
covariance analysis; eigenvalues and eigenfunctions; random noise; signal detection; EAMUSE; additive noise sensors; extended algorithm; multiple sources extraction; noise covariance; signal separation; Adaptive signal processing; Additive noise; Array signal processing; Automation; Noise measurement; Pollution measurement; Signal processing algorithms; Source separation; White noise; Working environment noise;
Conference_Titel :
Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2570-2
DOI :
10.1109/ISCAS.1995.523881
