Reinforced Likelihood Box Particle Filter

Abstract

This letter is concerned with the development of a general scheme for box particle filtering. It is based on the likelihood computation, the most crucial step of the estimation strategy. The proposed filter takes advantages from strong aspects of various existing box particle filters and adds an interesting reinforced likelihood computation method that enhances the estimation results. An overview on Box Particle Filters and discussions from assumptions used in the literature to the filters performance evaluation approach are presented. Also, comparative study of the obtained results by performing several scenarios on the illustration example is provided to highlight the efficiency of the proposed estimation strategy.