We derive the Cramer-Rao Lower Bound (CRLB) for the estimation of initial conditions of noise-embedded orbits produced by general one-dimensional maps. We relate this bound`s asymptotic behavior to the attractor`s Lyapunov number and show numerical examples. These results pave the way for more suitable choices for the chaotic signal generator in some chaotic digital communication systems. (c) 2006 Published by Elsevier Ltd
Calculation of the Cramer-Rao lower bound, i.e., the inverse of the Fisher information matrix, for o...
We study the modeling and control of evolving dynamical systems. In particular we model the dynamic...
We present a noise-filtering scheme which works on a chaotic signal containing a certain level of no...
This paper describes a simple method for detecting a class of first order or low dimensional discret...
A dynamic programming algorithm and a suboptimal but computationally efficient method for estimation...
We consider the problem of signal estimation where the observed time series is modeled as y(i) = x(i...
Recently several new results for Cramer-Rao lower bounds (CRLBs) in dynamical systems have been deve...
Recently several new results for Cramer-Rao lower bounds (CRLBs) in dynamical systems have been deve...
The treatment of noise in chaotic time series remains a challenging subject in nonlinear time series...
We show two examples of noise{induced synchronization. We study a 1-d map and the Lorenz systems, b...
This paper investigates the identification of global models from chaotic data corrupted by purely ad...
The finest state-space resolution that can be achieved in a physical dynamical system is limited by ...
The performance of the maximum likelihood estimator for a 1-D chaotic signal in white Gaussian noise...
We discuss abilities of quantifying low-dimensional chaotic oscillations at the input of two thresho...
Includes bibliographical references (p. 209-214).Supported by the U.S. Air Force Office of Scientifi...
Calculation of the Cramer-Rao lower bound, i.e., the inverse of the Fisher information matrix, for o...
We study the modeling and control of evolving dynamical systems. In particular we model the dynamic...
We present a noise-filtering scheme which works on a chaotic signal containing a certain level of no...
This paper describes a simple method for detecting a class of first order or low dimensional discret...
A dynamic programming algorithm and a suboptimal but computationally efficient method for estimation...
We consider the problem of signal estimation where the observed time series is modeled as y(i) = x(i...
Recently several new results for Cramer-Rao lower bounds (CRLBs) in dynamical systems have been deve...
Recently several new results for Cramer-Rao lower bounds (CRLBs) in dynamical systems have been deve...
The treatment of noise in chaotic time series remains a challenging subject in nonlinear time series...
We show two examples of noise{induced synchronization. We study a 1-d map and the Lorenz systems, b...
This paper investigates the identification of global models from chaotic data corrupted by purely ad...
The finest state-space resolution that can be achieved in a physical dynamical system is limited by ...
The performance of the maximum likelihood estimator for a 1-D chaotic signal in white Gaussian noise...
We discuss abilities of quantifying low-dimensional chaotic oscillations at the input of two thresho...
Includes bibliographical references (p. 209-214).Supported by the U.S. Air Force Office of Scientifi...
Calculation of the Cramer-Rao lower bound, i.e., the inverse of the Fisher information matrix, for o...
We study the modeling and control of evolving dynamical systems. In particular we model the dynamic...
We present a noise-filtering scheme which works on a chaotic signal containing a certain level of no...