Neural Networks for Improved Tracking

Authors:
L. I. Perlovsky;R. W. Deming
Affiliations:
Harvard Univ., Cambridge;-
Venue:
IEEE Transactions on Neural Networks
Year:
2007

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Abstract

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.