Fallback Methods for Particle Filters

Abstract

In this paper, we consider situation where the number of weighted particles in particle filter becomes too small. Usual approach for avoiding this is the resampling, but there are situations where this may still happen. It is possible that the realized measurement is unlikely and there are not enough particles to handle this situation. We propose to use a Kalman type filter that uses particles from the last time step as fallback method in this kind of situation.