Local Importance Sampling: A Novel Technique to Enhance Particle Filtering
- Peter Torma
- Csaba Szepesvari, Department of Computing Science; PI of AICML
In the low observation noise limit particle filters become inefficient. In this paper a simple-to-implement particle filter is suggested as a solution to this well-known problem. The proposed Local Importance Sampling based particle filters draw the particles’ positions in a two-step process that makes use of both the dynamics of the system and the most recent observation. Experiments with the standard bearings-only tracking problem indicate that the proposed new particle filter method is indeed very successful when observations are reliable. Experiments with a high-dimensional variant of this problem further show that the advantage of the new filter grows with the increasing dimensionality of the system.
Citation
P. Torma, C. Szepesvari. "Local Importance Sampling: A Novel Technique to Enhance Particle Filtering". Journal of Multimedia (JMM), 1, pp 32-43, January 2006.| Keywords: | machine learning |
| Category: | In Journal |
BibTeX
@article{Torma+Szepesvari:JMM06,
author = {Peter Torma and Csaba Szepesvari},
title = {Local Importance Sampling: A Novel Technique to Enhance Particle
Filtering},
Volume = "1",
Pages = {32-43},
journal = {Journal of Multimedia (JMM)},
year = 2006,
}Last Updated: March 07, 2007Submitted by Nelson Loyola
