Particle flow for particle filtering

Particle flow algorithms have been developed as an alternative to particle filtering. In these algorithms, there is no importance sampling, and particles are migrated from the prior to the posterior via a «flow», described by differential equations. Aside from a few special cases, implementations involve multiple approximations, and their impact on the accuracy of the estimates is not clearly understood. In this paper, we propose algorithms that use particle flow procedures to construct an importance sampling distribution within a standard particle filter. The resultant algorithms retain the statistical consistency of sequential Monte Carlo methods, but acquire the desirable properties of particle flow techniques. We report the results of a multiple target tracking simulation study that combines highly informative measurements with a reasonably high-dimensional state space, leading to a challenging scenario for particle filters. Of the filters we test, the particle flow particle filter provides the smallest tracking error and achieves the largest average effective sample size.