Computer Science
Scientific paper
Jun 1997
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3086..172w&link_type=abstract
Proc. SPIE Vol. 3086, p. 172-181, Acquisition, Tracking, and Pointing XI, Michael K. Masten; Larry A. Stockum; Eds.
Computer Science
Scientific paper
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.
Favier Gerard
Schmidlin Valerie
Winter Michel
No associations
LandOfFree
Neural network for track initiation and maintenance does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Neural network for track initiation and maintenance, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Neural network for track initiation and maintenance will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1481572