Self Adaptive Particle Filter

Abstract

The particle filter has emerged as a useful tool for problems requiring dynamic state estimation. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to reallocate these particles at each iteration. Both features are specified beforehand and are kept fixed in the regular implementation of the filter. In practice this may be highly inappropriate since it ignores errors in the models and the varying dynamics of the processes. This work presents a self adaptive version of the particle filter that uses statistical methods to adapt the number of particles and the propagation function at each iteration. Furthermore, our method presents similar computational load than the standard particle filter. We show the advantages of the self adaptive filter by applying it to a synthetic example and to the visual tracking of targets in a real video sequence.