9 August 2013 Joint detection, tracking and classification of multiple maneuvering targets based on the linear Gaussian jump Markov probability hypothesis density filter
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Optical Engineering, 52(8), 083106 (2013). doi:10.1117/1.OE.52.8.083106
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.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Wei Yang, Yao-wen Fu, Xiang Li, "Joint detection, tracking and classification of multiple maneuvering targets based on the linear Gaussian jump Markov probability hypothesis density filter," Optical Engineering 52(8), 083106 (9 August 2013). http://dx.doi.org/10.1117/1.OE.52.8.083106
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KEYWORDS
Detection and tracking algorithms

Target detection

Kinematics

Systems modeling

Electronic filtering

Error analysis

Lithium

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