An optimal generalized theory of signal representation

Authors:
J. S. Goldstein;J. R. Guerci;I. S. Reed
Affiliations:
Lincoln Lab., MIT, Lexington, MA, USA;-;-
Venue:
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Year:
1999

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EURASIP Journal on Applied Signal Processing

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Abstract

A new generalized statistical signal processing framework is introduced for optimal signal representation and compression. Previous work is extended by considering the multiple signal case, where a desired signal is observed only in the presence of other non-white signals. The solution to this multi-signal representation problem yields a generalization of the Karhunen-Loeve transform and generates a basis selection which is optimal for multiple signals and colored-noise random processes under the minimum mean-square error criterion. The important applications for which this model is valid include detection, prediction, estimation, compression, classification and recognition.