A Post-Resampling Based Particle Filter for Online Bayesian Estimation and Tracking

For state estimation problem, particle filter is generally used to construct the posterior probability density function by a set of particles, which is regarded as a solution to state estimation. Many techniques have been developed to improve performance of particle filter, at the cost of largely increased computational burden for each particle. In this paper, we propose a post-resampling based particle filter. The modified particle filter is capable of accurately representing the posterior probability density function through properly sampling particles. We applied the proposed particle filter to the classic bearings-only tracking problem. Simulation results showed that our modified particle filter had superior performance and reasonably computational cost, compared with the general approaches. It may provide a promising alternative to the existent particle filters.