Particle filtering is an non-parameterized algorithm via sequential Monte Carlo simulation to actualize bayesian estimation.

This paper expatiated the development and the research status of particle filtering at present.Then,introduces and

analyses the principle of particle filtering、the existed key problems and countermeasures in detail.Furthermore,it sum

up eleven improved methods of particle filtering algorithm.Meanwhile,applications are addressed in this paper.Finally

,the prospect of particle filtering is presented.