Title :
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
fDate :
1/1/1995 12:00:00 AM
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;
Journal_Title :
Information Theory, IEEE Transactions on
