Abstract:

Particle filters have been widely used in nonlinear/non- Gaussian Bayesian state estimation problems. However, efficient distribution of the limited number of particles in state space remains a critical issue in designing a particle filter. A simplified unscented particle filter (SUPF) is presented, where particles are drawn partly from the transition prior density (TPD) and partly from the Gaussian approximate posterior density (GAPD) obtained by a unscented Kalman filter. The ratio of the number of particles drawn from TPD to the number of particles drawn from GAPD is adaptively determined by the maximum likelihood ratio (MLR). The MLR is defined to measure how well the particles, drawn from the TPD, match the likelihood model. It is shown that the particle set generated by this sampling strategy is more close to the significant region in state space and tends to yield more accurate results. Simulation results demonstrate that the versatility and estimation accuracy of SUPF exceed that of standard particle filter,
extended Kalman particle filter and unscented particle filter.