JOURNAL ARTICLE
Study on Multi-Target Tracking Based on Particle Filter Algorithm
Junying MengJiaomin LiuYongzheng LiJuan Wang
Year: 2013 Journal: Research Journal of Applied Sciences Engineering and Technology Vol: 5 (2)Pages: 427-432 Publisher: Maxwell Scientific Publications
Abstract
Particle filter is a probability estimation method based on Bayesian framework and it has unique advantage to describe the target tracking non-linear and non-Gaussian. In this study, firstly, analyses the particle degeneracy and sample impoverishment in particle filter multi-target tracking algorithm and secondly, it applies Markov Chain Monte Carlo (MCMC) method to improve re-sampling process and enhance performance of particle filter algorithm.
Keywords:
Particle filter Markov chain Monte Carlo Auxiliary particle filter Tracking (education) Degeneracy (biology) Algorithm Computer science Bayesian probability Monte Carlo method Filter (signal processing) Particle (ecology) Gaussian Mathematics Artificial intelligence Ensemble Kalman filter Kalman filter Extended Kalman filter Statistics Computer vision Physics
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2
Cited By
0.47
FWCI (Field Weighted Citation Impact)
9
Refs
0.80
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Citation History
Topics
Target Tracking and Data Fusion in Sensor Networks
Physical Sciences → Computer Science → Artificial Intelligence
Maritime Navigation and Safety
Physical Sciences → Engineering → Ocean Engineering
Advanced Measurement and Detection Methods
Physical Sciences → Engineering → Electrical and Electronic Engineering
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