1 March 1996 Neural network data association with application to multiple-target tracking
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Optical Engineering, 35(3), (1996). doi:10.1117/1.600661
Abstract
Data association is the process of relating sensor measurements in a data fusion system. It can be structured in a basic framework very similar to that of the classic traveling salesman problem. The derivation of the energy function is presented, and the solution is based on a modified Hopfield network which uses the Runge–Kutta method and Aiyer’s network structure. The neural data association is then applied to the problem of multiple-target tracking (MTT). The proposed neural MTT system consists of a modified Hough transform track initiator, a Kalman filter state estimator and the Hopfield probabilistic data association. Real-life air surveillance data are used to evaluate the practicality of the neural MTT system, and the results show that the neural system works efficiently in real-life tracking environments.
Henry Leung, "Neural network data association with application to multiple-target tracking," Optical Engineering 35(3), (1 March 1996). http://dx.doi.org/10.1117/1.600661
JOURNAL ARTICLE
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KEYWORDS
Neural networks

Neurons

Hough transforms

Radar

Data fusion

Distance measurement

Optical engineering

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