Development and prospect of particle filtering

Particle filtering is a sequential Monte Carlo simulation based on nonlinear filtering algorithm. An overview of the status and development of research on particle filtering is presented. The principle, convergence, application and evolution of particle filtering are described in detail. First, the principle of sequential importance-sampling, the choice of importance distribution function, and the method of re-sampling are analyzed within Bayesian framework. Secondly, the improvement methods and novel variations of particle filtering are then summarized. Thirdly, the application and development in various areas are reviewed. Fourthly, the novel extension and trends of particle filtering are illustrated. Finally, further research prospects are introduced.