Adaptive Regularized Particle Filter for Synchronization of Chaotic Colpitts Circuits in an AWGN Channel

Adaptive Regularized Particle Filter for Synchronization of Chaotic Colpitts Circuits in an AWGN Channel
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用于 AWGN 通道中混沌 Colpitts 电路同步的自适应正则化粒子滤波器

DOI:
10.1007/s00034-012-9506-y
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发表时间:
2012-10
期刊:
Circuits, Systems, and Signal Processing
影响因子:
--
通讯作者:
Chen Kangsheng
Chen Kangsheng
中科院分区:
其他
文献类型:
--
作者:
Shi Zhiguo;Wang Lin;Gu Yujie;Chen Kangsheng

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对于混沌轨迹,当系统参数固定时,它们通常被限制在有界状态空间中。在本文中,我们提出了一种自适应正则化粒子滤波器(RPF),它充分利用了这一固有特性,用于混沌Colpitts电路的相同同步,以对抗加性高斯白噪声(AWGN)信道失真。该滤波器结合了RPF,从后验密度的连续近似中重新采样以避免样本贫化,然后利用修正的Kullback-Leibler距离(KLD)采样自适应选择所使用的粒子数量。与已有的固定大粒子数量的粒子滤波器(PFs)相比,本文提出的自适应RPF在相同性能下传播的粒子数量更少,从而为该问题提供了更有效的解决方案。
For chaotic trajectories, when the system parameters are fixed, they are generally confined in a bounded state space. In this paper, we propose an adaptive regularized particle filter (RPF), which makes the best of this inherent characteristic, for identical synchronization of chaotic Colpitts circuits combating additive white Gaussian noise (AWGN) channel distortion. This proposed filter incorporates RPF that resamples from a continuous approximation of the posterior density to avoid sample impoverishment and then utilizes the revised Kullback–Leibler distance (KLD) sampling to adaptively select the number of particles used. Compared with the existing particle filters (PFs) with fixed large number of particles, this proposed adaptive RPF propagates less number of particles with similar performance and thus provides a much more efficient solution for this problem.
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