Liverani, Silvia and Papavasiliou, Anastasia (2006) Entropy based adaptive particle filter CLASSICAL, UNSCENTED AND PARTICLE FILTERING METHODS. In: IEEE Nonlinear Statistical Signal Processing Workshop, Corpus Christi Coll, Cambridge, ENGLAND, SEP 13-15, 2006. Published in: NSSPW: NONLINEAR STATISTICAL SIGNAL PROCESSING WORKSHOP pp. 87-90. ISBN 978-1-4244-0579-4.

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Request Changes to record.

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.

Item Type:	Conference Item (UNSPECIFIED)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Journal or Publication Title:	NSSPW: NONLINEAR STATISTICAL SIGNAL PROCESSING WORKSHOP
Publisher:	IEEE
ISBN:	978-1-4244-0579-4
Official Date:	2006
Dates:	Date Event 2006 UNSPECIFIED
Number of Pages:	4
Page Range:	pp. 87-90
Publication Status:	Published
Title of Event:	IEEE Nonlinear Statistical Signal Processing Workshop
Location of Event:	Corpus Christi Coll, Cambridge, ENGLAND
Date(s) of Event:	SEP 13-15, 2006

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Repository staff actions (login required)

View Item