Title :
A second-order method for blind separation of non-stationary sources
Author :
Zhang, Ruifeng ; Tsatsanis, Michail K.
Author_Institution :
Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA
Abstract :
The question addressed in this paper is whether and under what conditions blind source separation is possible using only second-order statistics. It is well known that for stationary, i.i.d. sources the answer is negative due to the inherent unitary matrix ambiguity of output second-order information. It is shown in this paper however, that if the sources´ power is allowed to vary with time, unique identifiability can be achieved without resorting to higher order statistics. In many applications the sources´ power does change with time (e.g., speech or fading communication signals), and therefore the result has practical relevance. A novel second-order source separation method is proposed based on a generalized eigen-decomposition of appropriate correlation matrices and the identifiability conditions are investigated. Asymptotic performance results for the output SIR are developed
Keywords :
array signal processing; correlation methods; eigenvalues and eigenfunctions; matrix algebra; statistical analysis; asymptotic performance results; blind source separation; correlation matrices; generalized eigen-decomposition; identifiability conditions; nonstationary sources; output SIR; second-order source separation method; second-order statistics; sensor array; zero-forcing beamforming vectors; Blind source separation; Fading; Filtering; Higher order statistics; Sensor arrays; Source separation; Speech; Training data; Wireless communication; Wireless sensor networks;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940227
