Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002 · 11 citations · 8 references
ComputingComputer ScienceComputer VisionFeature EngineeringSensorsPattern RecognitionRadar InstrumentationRadar Signal ProcessingRadar SystemsSignal Detection TheoryTarget IdentificationIdentityRadar TrackingTarget ClassificationTarget DetectionTarget Tracking
Group tracking algorithms aim to identify and track multiple targets, yet spurious measurements and obscured targets can prevent accurate identity, making allegiance‑based classification with an IFFN sensor valuable. The study seeks to enable target classification when identity is unclear by leveraging allegiance information. The authors integrate an IFFN sensor into the GRIT (GRoup IMM‑JBPDAF Tracker) algorithm, allowing allegiance features to be used for classification in the absence of identity data. Simulations with a high‑range‑resolution radar and IFFN sensor demonstrate a 29 % reduction in computational classification effort when clutter is present.
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.
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Frame-rate omnidirectional surveillance and tracking of camouflaged and occluded targets
T.E. Boult, Ross J. Micheals, Xiang Gao et al. · 2003 · 93 citations