On using Likelihood-adjusted Proposals in Particle Filtering: Local Importance Sampling
- Peter Torma
- Csaba Szepesvari, Department of Computing Science; PI of AICML
An unsatisfactory property of particle filters is that they may become inefficient when the observation noise is low. In this paper we consider a simple-to-implement particle filter, called `LIS-based particle filter', whose aim is to overcome the above mentioned weakness. LIS-based particle filters sample the particles in a two-stage process that uses information of the most recent observation, too. Experiments with the standard bearings-only tracking problem indicate that the proposed new particle filter method is indeed a viable alternative to other methods.
Citation
P. Torma, C. Szepesvari. "On using Likelihood-adjusted Proposals in Particle Filtering: Local Importance Sampling". Symposium on Image and Signal Processing and Analysis, January 2005.| Keywords: | machine learning |
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BibTeX
@incollection{Torma+Szepesvari:SymposiumonImageandSignalProcessingandAnalysis05,
author = {Peter Torma and Csaba Szepesvari},
title = {On using Likelihood-adjusted Proposals in Particle Filtering: Local
Importance Sampling},
booktitle = {Symposium on Image and Signal Processing and Analysis},
year = 2005,
}Last Updated: March 07, 2007Submitted by Nelson Loyola
