ISSN : 1796-2048
Volume : 1    Issue : 1    Date : April 2006

Local Importance Sampling: A Novel Technique to Enhance Particle Filtering
Péter Torma and Csaba Szepesv´ari
Page(s): 32-43
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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.

Index Terms
Sequential Monte Carlo Methods, Particle Filters, Hidden Markov Models