Local Importance Sampling: A Novel Technique to Enhance Particle Filtering

Péter Torma, Csaba Szepesvári
2006 Journal of Multimedia  
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
more » ... 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 The goal is to construct π t such that I t,N (h) is close to E[h(X t )|Y 1:t ], independently of h.
doi:10.4304/jmm.1.1.32-43 fatcat:qgdqfi3mljcjzg7ngz554efwou