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Proceedings Paper

Comparison of the Hopfield neural network versus optimal control theory for the data association portion of the multitarget tracking problem
Author(s): Ron Abelson
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Paper Abstract

Considerably attention has been focused lately on using neural networks to optimize the solution to data association. A neural network has been shown to provide a good approximation to the joint probabilistic data association method. This paper will detail the method in which a Hopfield neural network can be used for data association in the multitarget tracking problem. In addition, a comparable optimal control theory solution will be presented. Both the Hopfield Neural Network and the optimal control theory approach were shown to provide adequate results in optimizing the data association portion of the multitarget tracking problem with neither method proving to be superior. In execution time, the optimal control theory approach is the preferred method. The purpose of this paper is not to state that optimal control theory is superior to the Hopfield Neural Network is solving constrained optimization problems. Optimal control theory cannot be used in cases where all goals are weighted equally, since no one goal can be viewed as a constraint. In conclusion, in certain data association problems, the optimal control theory approach in comparison with the Hopfield Neural Network is shown to be significantly more efficient with the same measure of accuracy.

Paper Details

Date Published: 15 September 1995
PDF: 13 pages
Proc. SPIE 2589, Sensor Fusion and Networked Robotics VIII, (15 September 1995); doi: 10.1117/12.220964
Show Author Affiliations
Ron Abelson, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 2589:
Sensor Fusion and Networked Robotics VIII
Paul S. Schenker; Gerard T. McKee, Editor(s)

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