Title :
A new criterion for blind deconvolution of colored input signals
Author :
Tsakalides, Panagiotis ; Nikias, Chrysostomos L.
Author_Institution :
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
In this paper, a new criterion with memory nonlinearity is introduced for blind deconvolution problems when the input signals are colored. The basic idea is to make use of the autocorrelation of the input sequence as the only statistical knowledge about the data. An adaptive weight algorithm is presented and tested with simulation examples of signals of known autocorrelation function. It is shown that the optimum memory size is directly related to the significant values of the autocorrelation function, and that the new algorithm converges faster than the Godard algorithm
Keywords :
adaptive signal processing; convergence of numerical methods; correlation theory; deconvolution; filtering theory; Godard algorithm; adaptive weight algorithm; autocorrelation; blind deconvolution; colored input signals; input sequence; memory nonlinearity; optimum memory size; signal processing; simulation examples; statistical knowledge; Autocorrelation; Blind equalizers; Cost function; Deconvolution; Delay; Image processing; Linearity; Signal processing; Signal processing algorithms; Testing;
Conference_Titel :
Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-4120-7
DOI :
10.1109/ACSSC.1993.342620
