State Estimation in Nonlinear System Using Sequential Evolutionary Filter

摘要

As a commonly encountered problem in the particle filters (PFs), the particle impoverishment is caused partially by the reduction of particle diversity after resampling. In this paper, a novel particle filtering technique named sequential evolutionary filter (SEF) is introduced, by which the particle impoverishment problem can be effectively mitigated. SEF is proposed based on the genetic algorithm (GA). A GA-inspired strategy is designed and incorporated in SEF. With this strategy, the resampling used in most of the existing PFs is not necessary, and the particle diversity can be maintained. The experimental results also demonstrate the effectiveness of SEF.