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Bayesian Estimation of Discrete Chaotic Signals by MCMC

Luengo David, Universidad de Cantabria
Pantaleón Carlos, Universidad de Cantabria
Santamaria Ignacio, Universidad de Cantabria

Page numbers in the proceedings:
Volume I pp 333-336

Nonlinear Signal and Systems / Adaptive Methods

Paper 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.

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