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Proceedings Paper

Neural network for track initiation and maintenance
Author(s): Michel Winter; Valerie Schmidlin; Gerard Favier
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Paper Abstract

Classical methods for multiple target tracking rely on the suboptimal decomposition of the problem into three steps: track initiation, maintenance and deletion. This paper presents an alternative solution that solves the tracking problem in an integrated way, through a global optimization. This problem is known to be a complex combinatorial one, for which neural models are particularly interesting. Previous works, based on the Hopfield model, proposed neural solutions that are drastically sensitive to the choice of some parameters, specially the size of the network and the weights in the cost function to be minimized. This kind of network often converges towards unacceptable solutions. We propose a new neural solution based on a recursive mode, where the constraints are taken into account in introducing competition between the neurons. The network optimizes an objective function that is a measure of the global quality of the tracking, computed as the sum of the track qualities at a given antenna turn. We finally present some simulation results for multiple target monosensor tracking that enable a comparison with classical techniques.

Paper Details

Date Published: 23 June 1997
PDF: 10 pages
Proc. SPIE 3086, Acquisition, Tracking, and Pointing XI, (23 June 1997); doi: 10.1117/12.277183
Show Author Affiliations
Michel Winter, Univ. of Nice/Sophia-Antipolis (France)
Valerie Schmidlin, Univ. of Nice/Sophia-Antipolis (France) and SYSTELIA (France)
Gerard Favier, Univ. of Nice/Sophia-Antipolis (France)

Published in SPIE Proceedings Vol. 3086:
Acquisition, Tracking, and Pointing XI
Michael K. Masten; Larry A. Stockum, Editor(s)

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