An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems

Ramazan Havangi

doi:10.1080/00051144.2018.1498207

Abstract

ABSTRACTParticle 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.

Highlights

Nonlinear filtering problems arise in many fields such as target tracking [1,2], robot navigation [3,4], etc
In APF, to increase the diversity of particles, the resampling process is done based on the particle swarm optimization (PSO)
In order to verify the performance of APF, the performance of it is evaluated and compared with unscented particle filter (UPF), PF, MCMC-based particle filtering and extended Kalman particle filter (EPF) on simulated data under different condition

Summary

Introduction

Nonlinear filtering problems arise in many fields such as target tracking [1,2], robot navigation [3,4], etc. Most of the particles share a few distinct values and the posterior distribution is insufficiently approximated [9,10] This problem can lead to the misleading state estimation results. By selecting a good the proposal distribution, which contains the current measurement information, the particle impoverishment problem can be alleviated Following this idea, the extended Kalman particle filter (EPF) is proposed in [15,16], which uses EKF to generate proposal distribution. The fuzzy inference system (FIS) supervises the performance of SRUPF with tuning statistical noises (Rt and Qt) to close theoretical covariance to actual covariance This adaptive tuning provides more robustness and consistency for the filter, which leads to results that are more accurate.

Background

Results

Conclusion

Disclosure statement