一种新的含噪混沌信号降噪算法

该文针对低信噪比、非高斯加性噪声和混沌动力学系统参数未知的含噪混沌信号降噪问题，提出了一种基于粒子滤波(Particle Filtering, PF)的降噪新算法。该算法将混沌信号和动力学系统中的未知参数作为一个多维状态矢量，利用PF方法递推计算多维状态矢量的联合后验概率分布，进而实现了对混沌信号的最优估计。对于混沌信号轨道分离过快所导致的退化问题，提出了有效的解决方法，并利用核平滑和自回归(Auto-Regression, AR)模型建模的方法分别实现了非时变以及时变参数的递推估计。仿真实验的结果表明，与现有的降噪方法相比，该文提出的新算法能够更加有效地抑制含噪混沌信号中的加性噪声。

A Novel Denoising Algorithm for Contaminated Chaotic Signals

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.