%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 }