.png){kind=icon}
.png){kind=icon}
Return
State Estimation in Nonlinear System Using Sequential Evolutionary Filter
.png){kind=icon}
PRE
AI
DOI：10.1109/TIE.2016.2522382.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Genetic algorithm (GA)
nonlinear system
particle filter (PF)
sequential evolutionary filter (SEF)
state estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.2
Papers:
1.8W
Citations:
9.8W