Neural Networks for Improved Tracking

In this letter, we have developed a neural network (NN) based upon modeling fields for improved object tracking. Models for ground moving target indicator (GMTI) tracks have been developed as well as neural architecture incorporating these models. The neural tracker overcomes combinatorial complexit...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2007-11, Vol.18 (6), p.1854-1857
Hauptverfasser:	Perlovsky, L.I., Deming, R.W.
Format:	Artikel
Sprache:	eng
Schlagworte:	Application software Applied sciences Architecture Artificial intelligence Combinatorial analysis Combinatorial complexity Complexity Computer science control theory systems Connectionism. Neural networks Exact sciences and technology Force sensors ground moving target indicator (GMTI) radar Grounds Laboratories Limiting Moving target indicators multitarget tracking Neural networks neural networks (NNs) Object detection Radar clutter Radar tracking Signal processing Target tracking Tracking
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Beschreibung
Zusammenfassung:	In this letter, we have developed a neural network (NN) based upon modeling fields for improved object tracking. Models for ground moving target indicator (GMTI) tracks have been developed as well as neural architecture incorporating these models. The neural tracker overcomes combinatorial complexity of tracking in highly cluttered scenarios and results in about 20-dB (two orders of magnitude) improvement in signal-to-clutter ratio.
ISSN:	1045-9227 2162-237X 1941-0093 2162-2388
DOI:	10.1109/TNN.2007.903143