Particle Filter with Differential Evolution for Trajectory Tracking

Over the last decades, Particle Filter also known as the Sampling Importance Resampling algorithm has successfully been applied to solve different problems in Engineering, e.g., trajectory tracking, non-linear estimation, and many others. Basically, the Particle Filter algorithm consists of a population of particles, which are sampled to estimate a posterior probability distribution. Unfortunately, in some cases the algorithm suffers from particle degeneracy, in which most particles converge prematurely to local minima due a loss of diversity of the population, and therefore do not contribute to estimation of the true probability distribution. In this paper, in order to tackle this drawback and to improve the performance of the standard Particle Filter we propose a modification to the algorithm by inserting a sampling mechanism inspired by Differential Evolution. Simulation results of the enhanced hybrid version are presented and compared with the standard Particle Filter algorithm and show the suitability of the proposed approach.