A MAP WAVELET-BASED PARTICLE FILTER FOR ESTIMATING CHAOTIC STATES WITH UNCERTAIN PARAMETERS AND UNKNOWN MEASUREMENT NOISES
In this paper,we develop a Maximum-A-Posterior waveletbased particle filter (MAP-WPF) and apply it to estimating the states and parameters of the chaotic systems with uncertain parameters and unknown parameters.To implement the proposed method,the covariance of the observation sequence is estimated using the wavelet transform,and the proper weights of particles are obtained accordingly.In addition,we obtain the parameters by the Maximum-APosterior (MAP) method to converge at the true parameters.Therefore,the MAP-WPF can effectively alleviate the sample degeneracy problem which is common in the standard particle filter (PF).Numerical simulations of Logistic map indicate the effectiveness of our proposed method which produces significant accuracy improvement than the PF.
MAP wavelet-based particle filter Chaotic state estimation Uncertain parameters Unknown measurement noises
Zhao Dexin Huang Anqi Li Ting Su Shaojing
College of Mechatronics and Automation,National University of Defense Technology,China National University of Defense Technology,No.47,Yanwachi Street,Changsha,Hunan Province,410073,P.R.C
国际会议
北京
英文
127-132
2013-04-27（万方平台首次上网日期，不代表论文的发表时间）