Fuzzy Particle Filtering for Uncertain Systems

In this paper, we propose a novel fuzzy particle filtering method for online estimation of nonlinear dynamic systems with fuzzy uncertainties. This approach uses a sequential fuzzy simulation to approximate the possibilities of the state intervals in the state–space, and estimates the state by fuzzy expected value operator. To solve the degeneracy problem of the fuzzy particle filter, one corresponding resampling technique is introduced. In addition, we compare the fuzzy particle filter with ordinary particle filter in both aspects of the theoretical basis and algorithm design, and demonstrate that the proposed filter outperforms standard particle filters especially when the number of the particles is small. The numerical simulations of two continuous-state nonlinear systems and a jump Markov system are employed to show the effectiveness and robustness of the proposed fuzzy particle filter.