Local importance sampling: a novel technique to enhance particle filtering
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.