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

fYear

1993

fDate

1-3 Nov 1993

Firstpage

746

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-4120-7

Type

conf

DOI

10.1109/ACSSC.1993.342620

Filename

342620