Bayesian estimation of discrete chaotic signals by MCMC

Author

Luengo, David ; Pantaleon, Carlos ; Santamaria, Ignacio

Author_Institution

DICOM, Univ. de Cantabria, Santander, Spain

fYear

2002

fDate

3-6 Sept. 2002

Firstpage

1

Lastpage

4

Abstract

This paper considers Markov Chain Monte Carlo (MCMC) methods for the estimation in Additive White Gaussian Noise (AWGN) of discrete chaotic signals generated iterating any unimodal map. In particular, the Metropolis-Hastings (MH) algorithm is applied to the estimation of signals generated by iteration of the logistic map. Using this technique, Bayesian Minimum Mean Square Error (MS) and Maximum a Posteriori (MAP) estimators have been developed for any unimodal map. Computer simulations show that the proposed algorithms attain the Cramer-Rao Lower Bound (CRLB), and outperform the existing alternatives.

Keywords

AWGN; Bayes methods; Markov processes; Monte Carlo methods; least mean squares methods; signal processing; AWGN; Bayesian estimation; Bayesian minimum mean square error; CRLB; Cramer-Rao lower bound; MCMC method; MH algorithm; Markov Chain Monte Carlo method; Metropolis-Hastings algorithm; additive white Gaussian noise; discrete chaotic signals; logistic map; maximum a posteriori estimator; unimodal map; Abstracts; Bayes methods; Estimation; Facsimile; Handheld computers; Logistics; Simulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference, 2002 11th European

Conference_Location

Toulouse

ISSN

2219-5491

Type

conf

Filename

7072030