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A Novel Demodulation Method for Chaotic Parameter Modulation

机译:一种混沌参数调制的新型解调方法

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摘要

This paper proposes a particle filter to demodulate the chaotic parameter modulation (CPM) signal. CPM embeds the signal of transmission in the bifurcating parameter of a chaotic dynamical system, and uses the wide-band output of the chaotic system as the transmitted signal. As the bifurcating parameter of the chaotic dynamical system is unknown and the demodulation problem is just a system identification problem. Conventional methods like extended Kalman filter (EKF) and least mean square (LMS) have relatively poor performance. Incorporating Rao-Blackwellisaion (RB) and roughening noise method, particle filtering can be used to estimate the parameter sequentially. Simulation results confirm that the proposed particle filter has the improved performance compared to the EKF and LMS.
机译:本文提出了一种粒子滤波器来解调混沌参数调制(CPM)信号。 CPM在混沌动态系统的分叉参数中嵌入传输信号,并使用混沌系统的宽带输出作为传输信号。由于混沌动力系统的分叉参数未知,解调问题只是系统识别问题。常规方法,如扩展卡尔曼滤波器(EKF)和最小均方(LMS)的性能相对较差。包含Rao-Blackwellisaion(RB)和粗糙噪声方法,可用于顺序估计参数的颗粒滤波。仿真结果证实,与EKF和LMS相比,所提出的粒子过滤器具有改进的性能。

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