应用粒子滤波器实现混沌通信系统的盲信道均衡

粒子滤波器(Particle filter,PF)是一种结合重要性权重抽样的序贯蒙特卡罗方法,能够应用到任意状态空间模型,并且能较好地估计经过非线性变化后的随机变量的统计特性.本文应用粒子滤波器和信号建模技术研究混沌通信系统的盲信道均衡问题,发展基于混沌的通信系统的盲均衡技术.仿真结果证实了,当Logistic映射作为混沌发生器和通信场景为固定参数与时变衰落信道时,该盲信道均衡器与基于扩展卡尔曼滤波算法的盲均衡器和基于无先导变换的自适应盲均衡器相比,有较好的均衡实现.此外,利用本文的盲均衡算法,实现了一种混沌调制通信系统的解调.

Blind channel equalization's performance of chaos-based communication systems with particle filter

Particle filter is the sequential Monte Carlo method incorporating with the re-sampling of importance weights, which can be applied to any state-space model and deal well with the statistical estimations of the nonlinear-transformed random variables. This paper addresses the issue of blind channel equalization for chaos-based communication systems by using particle filter and the modeling technique for signal, and develops blind equalization technique for chaos-based communication systems. Simulation results demonstrate that when the Logistic map is used as the chaotic generator, and communication channels are with fixed and time-varying fading parameters, the new equalizer has a better performance in comparison with the blind equalizer based on the extended Kalman filter and the adaptive equalizer based on the unscented transform. In addition, the equalization algorithm is also used to realize the demodulation for a chaotic modulation system.