Joint Target Detection Tracking and Classification Based on Finite-Set Statistics Theory

Hong Wei Quan; Jun Hua Li; Xiao Juan Zhang

doi:10.4028/www.scientific.net/AMM.668-669.1072

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 668-669Joint Target Detection Tracking and Classification...

Joint Target Detection Tracking and Classification Based on Finite-Set Statistics Theory

Abstract:

In traditional target tracking methods, the target number, target states and target class can not be estimated in same time. This paper investigated the joint target detection, tracking and classification method which is based on finite-set statistics theory. First, the random set and finite-set statistics theory are introduced for theoretic analysis. Second, the finite-set model for target tracking is given to construct a generalized nonlinear fusion framework. Finally, the finite-set based Bayesian filter is developed to track the targets in surveillance region. By recursively calculating the probabilistic hypothesis density, the target number, target states and target class can be evaluated simultaneously.

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Periodical:

Applied Mechanics and Materials (Volumes 668-669)

Pages:

1072-1075

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.668-669.1072

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Online since:

October 2014

Authors:

Hong Wei Quan*, Jun Hua Li, Xiao Juan Zhang

Keywords:

Bayesian Filter, Finite-Set Statistics, Probabilistic Density, Target Tracking

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* - Corresponding Author

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