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Optical Engineering

Joint detection, tracking and classification of multiple maneuvering targets based on the linear Gaussian jump Markov probability hypothesis density filter
Author(s): Wei Yang; Yao-wen Fu; Xiang Li
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Paper Abstract

This paper is to account for joint detection, tracking, and classification (JDTC) of an unknown and time-varying number of maneuvering targets in clutter. Unlike most tracking algorithms that use only the kinematic measurements, this paper proposes a recursive JDTC algorithm to exploit the coupled information between target detection, tracking and classification based on the Gaussian mixture probability hypothesis density filter (GMPHDF) in the linear Gaussian jump Markov systems (LGJMS) multitarget model. The original LGJMS-GMPHDF, devoted to joint detect and track all potential targets utilizing an identical and fixed set of models, has been modified to incorporate target class information and the class-dependent kinematic model set. The mutual dependence between target kinematics and class is exploited twice: first to construct the combined model sets, then to compute the combined measurement likelihood. The proposed algorithm is illustrated via a simulation example involving tracking of two closely spaced parallel moving targets and two crossing moving targets from different classes, where targets can appear and disappear.

Paper Details

Date Published: 9 August 2013
PDF: 13 pages
Opt. Eng. 52(8) 083106 doi: 10.1117/1.OE.52.8.083106
Published in: Optical Engineering Volume 52, Issue 8
Show Author Affiliations
Wei Yang, National Univ. of Defense Technology (China)
Yao-wen Fu, National Univ. of Defense Technology (China)
Xiang Li, National Univ. of Defense Technology (China)

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