A New Particle Filter Based on Differential Evolution Method

The main problems of the Particle Filter (PF) are the sample degeneracy and impoverishment phenomenon. To deal with the problems, a new PF based on Differential Evolution (DE) is proposed. Firstly, the Importance Distribution (ID) which contains the newest measurements is produced with the Unscented Kalman Filter (UKF). Secondly, the particles sampling from the ID are no longer resampled by the conventional algorithm, however, they are regarded as the sample of the current population and their weights as the fitness function. Finally, a process of mutation, recombination and section is repeated until the optimum particles are found. The simulation result shows that the proposed method relieves effectively the sample degradation and poverty problems, improves the efficiency of particles and achieves preferable precision on estimation.