Improved particle filter based on differential evolution

Abstract

Resampling schemes for a particle filter based on the differential evolution (DE) algorithm are presented. By using these schemes, several types of differential evolution particle filters (DEPFs) are proposed. In the proposed filters, the unscented Kalman filter is utilised to generate the importance proposal distribution and the different DE algorithms are used as the resampling scheme. Simulation results demonstrate that the proposed DEPFs outperform the sequential importance resampling algorithm, the regularised particle filter, and the unscented particle filter.