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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-04

AUTHORS

Shaohua Hong, Zhiguo Shi, Lin Wang, Yujie Gu, Kangsheng Chen

ABSTRACT

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. More... »

PAGES

825-841

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00034-012-9506-y

DOI

http://dx.doi.org/10.1007/s00034-012-9506-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045038886


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