Title :
An optimal generalized theory of signal representation
Author :
Goldstein, J.Scott ; Guerci, Joseph R. ; Reed, Irving S.
Author_Institution :
Lincoln Lab., MIT, Lexington, MA, USA
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
Keywords :
FIR filters; Karhunen-Loeve transforms; Wiener filters; discrete time filters; estimation theory; filtering theory; least mean squares methods; prediction theory; random noise; random processes; signal classification; signal detection; signal representation; FIR filter; Karhunen-Loeve transform; MMSE; basis selection; colored-noise random processes; discrete time wide-sense stationary signals; discrete-time finite impulse response Wiener filter; estimation theory; minimum mean-square error criterion; multiple signals; non-white signals; optimal generalized theory; prediction; recognition; signal classification; signal compression; signal detection; signal representation; statistical signal processing; Colored noise; Compaction; Finite impulse response filter; Laboratories; Maximum likelihood detection; Predictive models; Signal generators; Signal processing; Signal representations; Wiener filter;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5041-3
DOI :
10.1109/ICASSP.1999.756232
