Particle Filter Applied to Noisy Synchronization in Polynomial Chaotic Maps
Particle Filter Applied to Noisy Synchronization in Polynomial Chaotic Maps
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发表时间：
2009-11
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通讯作者：
Moussa Yahia;P. Acco;M. Benslama
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作者：
Moussa Yahia;P. Acco;M. Benslama
多项式映射提供了用于在噪声信道下的混沌同步范围内获得更好性能的分析性质。本文提出了一种新的方法来简化文献[1]中给出的精确多项式卡尔曼滤波器（ExPKF）的方程。这种快速算法与其他估计器的比较表明，所有考虑的观测器的性能迅速消失的信道噪声，使应用的混沌同步难以处理。对ExPKF的仿真表明，在低信道噪声条件下，为了保持发射极的稳定而在发射极上绘制的饱和严重影响了性能。然后，我们提出了一个粒子滤波器，优于所有其他卡尔曼结构观测器的情况下，嘈杂的渠道。关键词：混沌同步，饱和，快速ExPKF，粒子滤波，多项式映射。
Polynomial maps offer analytical properties used to obtain better performances in the scope of chaos synchronization under noisy channels. This paper presents a new method to simplify equations of the Exact Polynomial Kalman Filter (ExPKF) given in (1). This faster algorithm is compared to other estimators showing that performances of all considered observers vanish rapidly with the channel noise making application of chaos synchronization in- tractable. Simulation of ExPKF shows that saturation drawn on the emitter to keep it stable impacts badly performances for low channel noise. Then we propose a particle filter that outperforms all other Kalman structured observers in the case of noisy channels. Keywords—Chaos synchronization, Saturation, Fast ExPKF, Parti- cle filter, Polynomial maps.