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Multi-target tracking method based on improved firefly algorithm optimized particle filter

delete2019-09-01
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PRE
AI
M
Mengchu Tian
Y
Yuming Bo
Z
Zhimin Chen *
P
Panlong Wu
DOI:10.1016/j.neucom.2019.06.003delete
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Abstract

Abstract

En 中文
Joint probabilistic data association-particle filter (JPDA-PF) algorithm is often used to solve the problem of data association in multi-target tracking, it has the advantages of particle filter in dealing with nonlinear and non-Gaussian systems, but at the same time, the resampling process of particle filter will lead to the problem of particle impoverishment, which will affect the tracking accuracy. In this article, a new multitarget tracking method based on particle filter optimized by improved firefly algorithm is proposed. To solve the problem of weight degradation and avoid the problem of particle impoverishment, the improved firefly algorithm replaces the traditional resampling method and optimizes the distribution of particles in particle filter. According to the simulation results, the improved algorithm has better tracking accuracy than JPDA-PF as the number of particles is equal. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Particle filter
Firefly algorithm
Multi-target tracking
Data association
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Journal

Neurocomputing cover
Neurocomputing
IF:
6.5
Papers:
2.5W
Citations:
6.5W

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