Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2018.1498207
An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems
Ramazan Havangi
; Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
Sažetak
Particle filters have been widely used in nonlinear/non-Gaussian Bayesian state estimation problems. However, the particle filter (PF) is inconsistent over time. The inconsistency of PF mainly
results from the particle depletion in resampling step and an incorrect priori knowledge of process and measurement noise. To cope with this problem and enhance the accuracy and consistency of the state estimation, an adaptive particle filter(APF) is proposed in this paper. In APF, an adaptive fuzzy square-root unscented Kalman filter (AFSRUKF) is used to generate the proposal distribution. This causes that beside the merit of reducing the computational cost, APF has some other advantages such as increasing consistency that leads to more numerical stability and better performance. Moreover,APF can work in unknown statistical noise behaviour and is more robust. This is why the fuzzy inference system (FIS) supervises the performance of square-root
unscented particle filter (SRUPF) using tuning statistical noises. In APF, to increase the diversity of particles, the resampling process is done based on the particle swarm optimization (PSO). With this resampling strategy, the small-weight particles are modified to the large-weight ones without duplication and elimination of particles. The effectiveness of APF is demonstrated by using two experiment examples through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method.
Ključne riječi
Fuzzy inference system; particle filter; particle swarm optimization (PSO)
Hrčak ID:
225184
URI
Datum izdavanja:
6.8.2018.
Posjeta: 765 *