Neural network for track initiation and maintenance

Michel Winter; Valerie Schmidlin; Gerard Favier

doi:10.1117/12.277183

23 June 1997 Neural network for track initiation and maintenance

Michel Winter, Valerie Schmidlin, Gerard Favier

Proceedings Volume 3086, Acquisition, Tracking, and Pointing XI; (1997) https://doi.org/10.1117/12.277183
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Michel Winter, Valerie Schmidlin, and Gerard Favier "Neural network for track initiation and maintenance", Proc. SPIE 3086, Acquisition, Tracking, and Pointing XI, (23 June 1997); https://doi.org/10.1117/12.277183

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