A parameter estimation and filtering method of chaotic system based on particle filter

The parameter estimation of chaotic system is a premise of system control and synchronization. In view of chaotic system's characteristics, such as sensitivity to initial condition, long-term unpredictability and so on, a filter applying to chaotic system was proposed based on chaotic system state space theory and particle filter (PF) theory. In a superimposed noise conditions, the parameter estimation and filtering of Lorenz chaotic system were simulated and analyzed. The simulation results show the proposed filtering algorithm is better than a chaotic system parameter estimation and filtering method based on extended Kalman filter (EKF) in bias estimates, and is an effective method for estimating the parameters of chaotic system and filter.