Chaotic synchronization mechanism based on Gaussian Particle Filtering

Sequential Bayesian estimation for dynamic state space models described by the logistic map involves recursive estimation of hidden chaos driving states based on noisy observations in response system end. The Gaussian Particle Filter (GPF) is introduced as a new synchronization method for the chaotic security communication when the presence of noise in chaotic drive-response system, wireless channel and initial parameter mismatch. It is analytically shown that, if the Gaussian approximations hold true, the GPF minimizes the root mean square error of the estimated dynamic state messages asymptotically. Compared to other chaotic synchronization methods, GPF technique improves the system response speed, and is more robust and stable over the EKF, especially for highly nonlinear system model where the EKF can diverge. Simulation results reveal our work can provide desirable chaotic synchronization.