Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10633
Title: Entropy based adaptive particle filter
Authors: Liverani, S
Papavasiliou, A
Keywords: Markov;Entropy;Distributed computing;Kernel;Nonlinear dynamical systems;Parameter estimation;Random variables;State estimation;Statistics
Issue Date: 2006
Publisher: IEEE
Citation: NSSPW - Nonlinear Statistical Signal Processing Workshop 2006, 2006
Abstract: We propose a particle filter for the estimation of a partially observed Markov chain that has a non dynamic component. Such systems arise when we include unknown parameters or when we decompose non ergodic systems to their ergodic classes. Our main assumption is that the value of the non dynamic component determines the limiting distribution of the observation process. In such cases, we do not want to resample the particles that correspond to the non dynamic component of the Markov chain. Instead, we take a weighted average of particle filters corresponding to different values of the non dynamic component. The computation of the weights is based on entropy and the number of particles corresponding to each particle filter is proportional to the weights.
URI: http://bura.brunel.ac.uk/handle/2438/10633
DOI: http://dx.doi.org/10.1109/NSSPW.2006.4378826
ISBN: 1424405815
9781424405817
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf1.14 MBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.