A Review of Particle Filters

This paper reviews the algorithms for particle filtering. We first present the basic knowledge of the Recursive Bayesian Estimation, and the Importance Sampling/Resampling Techniques which are based on the Monte Carlo Method. We then describe in detail the two standard algorithms, Sequential Importance Sampling and Sequential Importance Resampling. We subsequently introduce recent progress in the research of particle-filtering algorithms to tackle issues encountered by the two standard algorithms in practical applications. We analyze key ideas of the new algorithms. We classify the new algorithms according to their improvements in the validity and real-time performance. We finally give some suggestions of future research directions.