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.
Bibliographical noteFunding Information:
Manuscript received December 6, 2007; revised April 9, 2008 and August 28, 2008; accepted November 13, 2008. First version published June 30, 2009; current version published August 14, 2009. This work was supported by Grant number R01-2006-000-11016-0 from the Basic Research Program of the Korea Science & Engineering Foundation.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computational Theory and Mathematics