1. A New Evolutionary Particle Filter for the Prevention of Sample Impoverishment
- Author
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Hyung-Jin Kang, Seongkeun Park, Jae Pil Hwang, and Euntai Kim
- Subjects
Mathematical optimization ,Computational Theory and Mathematics ,Convergence (routing) ,Crossover ,Genetic algorithm ,Evolutionary algorithm ,Filter (signal processing) ,Particle filter ,Software ,Evolutionary computation ,Theoretical Computer Science ,Premature convergence ,Mathematics - Abstract
Particle filters perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. Unfortunately, there are some cases in which most particles are concentrated prematurely at a wrong point, thereby losing diversity and causing the estimation to fail. In this paper, genetic algorithms (GAs) are incorporated into a particle filter to overcome this drawback of the filter. By using genetic operators, the premature convergence of the particles is avoided and the search region of particles enlarged. The GA-inspired proposal distribution is proposed and the corresponding importance weight is derived to approximate the given target distribution. Finally, a computer simulation is performed to show the effectiveness of the proposed method.
- Published
- 2009
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