Robust evolutionary particle filter

The particle filter (PF) has been widely applied for non-linear filtering owing to its ability to carry multiple hypotheses relaxing the linearity and Gaussian assumptions. However, PF is inconsistent over time due to the loss of particle diversity caused mainly by the particle depletion in resampling step and incorrect a priori knowledge of process and measurement noise. To overcome these problems, in this paper, robust evolutionary particle filter is proposed. The proposed method can work in unknown statistical noise and does not require a prior knowledge about the system. In addition, to increase diversity, a resampling process is done based on the differential evolution (DE). The effectiveness of the proposed algorithm is demonstrated through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method.