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Proceedings Paper

Feature-aided JBPDAF group tracking and classification using an IFFN sensor
Author(s): Erik P. Blasch; Tom Connare
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Paper Abstract

Recent work has been conducted to develop group tracking algorithms that identify and track multiple targets. One of the characteristics of the group tracking algorithms is the ability to correctly identify the target. If enough evidence has been accumulated to identify the target, the algorithms perform well. However, in the case of spurious measurements and obscured targets, the target identity may not be completely realizable. For the case in which the target identity is not discerned, it is important to classify the target based on some methodology to aid the user. Such a classification could be an allegiance so that when the algorithm groups targets, the information is useful to the human. One sensor that is ideal for the scenario is an Identify Friend Foe Neutral (IFFN) sensor which can classify the target allegiance. By incorporating an IFFN sensor in the GRoup IMM-JBPDAF Tracker (GRIT) algorithm, results show that when identity information is not available, target classification is realizable with allegiance features. Results are simulated for a high-range resolution radar (HRR) and an IFFN sensor and a 29% reduction in the computational classification due to the presence of clutter.

Paper Details

Date Published: 7 August 2002
PDF: 10 pages
Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); doi: 10.1117/12.478505
Show Author Affiliations
Erik P. Blasch, Wright State Univ. (United States)
Tom Connare, Univ. of Dayton (United States)

Published in SPIE Proceedings Vol. 4728:
Signal and Data Processing of Small Targets 2002
Oliver E. Drummond, Editor(s)

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