Maximum-likelihood estimation of a class of chaotic signals

Author

Papadopoulos, Haralabos C. ; Wornell, Gregory W.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA

Volume

41

Issue

1

fYear

1995

fDate

1/1/1995 12:00:00 AM

Firstpage

312

Lastpage

317

Abstract

The chaotic sequences corresponding to tent map dynamics are potentially attractive in a range of engineering applications. Optimal estimation algorithms for signal filtering, prediction, and smoothing in the presence of white Gaussian noise are derived for this class of sequences based on the method of maximum likelihood. The resulting algorithms are highly nonlinear but have convenient recursive implementations that are efficient both in terms of computation and storage. Performance evaluations are also included and compared with the associated Cramer-Rao bounds

Keywords

Gaussian noise; chaos; filtering theory; maximum likelihood detection; maximum likelihood estimation; optimisation; prediction theory; recursive estimation; sequences; smoothing methods; white noise; Cramer-Rao bounds; chaotic sequences; chaotic signals; highly nonlinear algorithms; maximum likelihood; optimal estimation algorithms; performance evaluation; prediction; recursive implementations; signal filtering; smoothing; tent map dynamics; white Gaussian noise; Chaos; Chaotic communication; Filtering algorithms; Gaussian noise; Kalman filters; Maximum likelihood detection; Maximum likelihood estimation; Nonlinear dynamical systems; Recursive estimation; Smoothing methods;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.370091

Filename

370091