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.
%0 Journal Article
%1 torma2006
%A Torma, P.
%A Szepesvári, Cs.
%D 2006
%J Journal of Multimedia
%K application filtering, particle theory, vision,
%P 32--43
%T Local Importance Sampling: A Novel Technique to Enhance Particle Filtering
%V 1
%X 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.
@article{torma2006,
abstract = {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.},
added-at = {2020-03-17T03:03:01.000+0100},
author = {Torma, P. and Szepesv{\'a}ri, {Cs}.},
biburl = {https://www.bibsonomy.org/bibtex/2e26082c239f926897bcd8ac8acb9231e/csaba},
date-modified = {2010-09-02 13:09:16 -0600},
interhash = {108c97c22ab58f0ae56339ff83d92e14},
intrahash = {e26082c239f926897bcd8ac8acb9231e},
journal = {Journal of Multimedia},
keywords = {application filtering, particle theory, vision,},
pages = {32--43},
pdf = {papers/jmm01013243.pdf},
timestamp = {2020-03-17T03:03:01.000+0100},
title = {Local Importance Sampling: A Novel Technique to Enhance Particle Filtering},
volume = 1,
year = 2006
}