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Optical Engineering

Neural network data association with application to multiple-target tracking
Author(s): Henry Leung
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Paper 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.

Paper Details

Date Published: 1 March 1996
PDF: 8 pages
Opt. Eng. 35(3) doi: 10.1117/1.600661
Published in: Optical Engineering Volume 35, Issue 3
Show Author Affiliations
Henry Leung, Defence Research Establishment Ottawa (Canada)


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