A Novel Denoising Algorithm for Contaminated Chaotic Signals
一种新的含噪混沌信号降噪算法

A novel algorithm for denoising the contaminated chaotic signals is proposed, which is based on Particle Filtering (PF), and adapted for low SNR, additive non-Gaussian noise and the chaotic dynamic system with unknown parameters. Basic idea behind the proposed algorithm is that, chaotic signal and unknown parameters in the chaotic dynamic system are considered as a high dimension state vector, and the joint posterior probability density of these state vectors can be recursively calculated by utilizing the principle of Particle Filtering, then the optimum estimation of chaotic signal can be attained. In order to overcome the degenerate phenomena caused by the rapid divergence of the chaotic orbits, an effective strategy is taken in the proposed algorithm. Kernel smoothing method and Auto Regression (AR) model are used to recursively estimate the non-time-varying and time-varying parameters, respectively. The simulation results show that, compared with the existing denoising methods, the proposed algorithm can more effectively denoise additive noise in contaminated chaotic signals.