Deconvolution/identification techniques for nonnegative signals

Author

Goodman, Dennis M. ; Yu, David R.

Author_Institution

Lawrence Livermore Nat. Lab., Livermore, CA, USA

Volume

4

fYear

1992

fDate

23-26 Mar 1992

Firstpage

153

Abstract

Several methods for solving the nonparametric deconvolution/identification problem when the unknown is nonnegative are presented. The authors consider the constrained least squares method and discuss three ways to estimate the regularization parameter: the discrepancy principle, Mallow´s (1973) C_L, and generalized cross validation. They consider maximum entropy methods. A new conjugate gradient algorithm and a preliminary comparison are presented

Keywords

conjugate gradient methods; information theory; least squares approximations; signal processing; Mallow´s C_L method; conjugate gradient algorithm; constrained least squares method; discrepancy principle; generalized cross validation; maximum entropy methods; nonnegative signals; nonparametric deconvolution/identification; regularization parameter estimation; Acoustic measurements; Deconvolution; Electromagnetic measurements; Equations; Frequency estimation; Gaussian noise; Laboratories; Least squares methods; Parameter estimation; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on

Conference_Location

San Francisco, CA

ISSN

1520-6149

Print_ISBN

0-7803-0532-9

Type

conf

DOI

10.1109/ICASSP.1992.226463

Filename

226463