Object tracking using the particle filter optimised by the improved artificial fish swarm algorithm. (9th September 2019)

• Record Type: Journal Article
• Authors: Zeng, Zhigao; Bao, Haixing; Wen, Zhiqiang; Zhu, Wenqiu
• Is Part Of: International journal of intelligent information and database systems. Volume 12:Number 1/2(2019)
• Page Start: 6
• Page End: 19
• Publication Date: 2019-09-09
• Subjects: object tracking -- particle filter -- artificial fish swarm algorithm -- global search; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

In particle filter algorithm, the weight values of particles will gradually decrease as the increase of iteration times and the variance of the weight value of the particles will increase. This will lead to an increase in the deviation between the estimated state and the true state. In order to deal with this problem, an improved particle filter algorithm is proposed in this paper. That is, an improved artificial fish swarm optimisation algorithm is used to optimise the traditional particle filter. In the improved particle filter algorithm, the resampled particles will be driven to the region with high likelihood function to increase the weight values of the particles. Thus, the estimated state is closer to the real state. Experiment results show the advantage of our new algorithm over a range of existing algorithms.