Title :
Independent component analysis in noise
Author :
Tong, Lang ; Kung, S.Y.
Author_Institution :
Dept. of Electr. Eng., Cornell Univ., Ithaca, NY, USA
Abstract :
Estimating statistically independent sources in the presence of unknown channel distortion and additive white Gaussian noise is considered. A geometrical approach is presented to the analysis of the class of minimum entropy (ME) receivers. It is shown that ME receivers have the signal space property, i.e., all ME receivers reside in the column space of the channel mixing matrix. A necessary and sufficient condition that ME and the minimum mean square error (MMSE) receivers are co-linear is given. Two examples are presented to illustrate effects of source statistics. It is shown that ME receiver may deviate considerably from the MMSE receiver when there are both sub-Gaussian and super-Gaussian sources.
Keywords :
AWGN; least mean squares methods; minimum entropy methods; parameter estimation; receivers; signal processing; statistical analysis; telecommunication channels; MMSE receivers; additive white Gaussian noise; channel distortion; channel mixing matrix; column space; geometrical approach; independent component analysis; minimum entropy receivers; minimum mean square error; necessary condition; signal processing; signal space property; source statistics estimation; statistically independent sources; sub-Gaussian source; sufficient condition; super-Gaussian source; Contracts; Deconvolution; Entropy; Estimation; Independent component analysis; Noise measurement; Performance analysis; Random variables; Samarium; Signal analysis;
Conference_Titel :
Signals, Systems & Computers, 1998. Conference Record of the Thirty-Second Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-5148-7
DOI :
10.1109/ACSSC.1998.751594
