Bayesian estimation of chaotic signals generated by piecewise-linear maps

Authors:
Carlos Pantaleón;Luis Vielva;David Luengo;Ignacio Santamaría
Affiliations:
Departamento de Ingenieria de Comunicaciones, Universidad de Cantabria, Avd. Castros s/n, 39005 Santander, Spain;Departamento de Ingenieria de Comunicaciones, Universidad de Cantabria, Avd. Castros s/n, 39005 Santander, Spain;Departamento de Ingenieria de Comunicaciones, Universidad de Cantabria, Avd. Castros s/n, 39005 Santander, Spain;Departamento de Ingenieria de Comunicaciones, Universidad de Cantabria, Avd. Castros s/n, 39005 Santander, Spain
Venue:
Signal Processing
Year:
2003

Citing 6
Cited 0

Bayesian estimation of a class of chaotic signals

ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
A general efficient method for chaotic signal estimation

IEEE Transactions on Signal Processing
Statistical analysis and spectral estimation techniques forone-dimensional chaotic signals

IEEE Transactions on Signal Processing
Methods for chaotic signal estimation

IEEE Transactions on Signal Processing
Estimating initial conditions of noisy chaotic signals generated bypiecewise linear Markov maps using itineraries

IEEE Transactions on Signal Processing
Maximum-likelihood estimation of a class of chaotic signals

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.08

Visualization

Abstract

Chaotic signals are potentially attractive in a wide range of signal processing applications. This paper deals with Bayesian estimation of chaotic sequences generated by piecewise-linear (PWL) maps and observed in white Gaussian noise. The existence of invariant distributions associated with these sequences makes the development of Bayesian estimators quite natural, Both maximum a posteriori (MAP) and minimum mean square error (MS) estimators are derived. Computer simulations confirm the expected performance of both approaches, and show how the inclusion of a priori information produces in most cases an increase in performance over the maximum likelihood (ML) case.