The next generation of infrared imaging trackers and seekers will allow for the implementation of more sophisticated and smarter tracking algorithms, able to keep a positive lock on a targeted aircraft in the presence of countermeasures such as decoy flares. Pattern recognition algorithms will be able to select targets with the help of features extracted from all possible targets images observed in the missile's field of view. Artificial neural networks provide an important class of such algorithms. In particular, probabilistic neural networks are able to reach performances similar to those of optimal Bayesian classifiers by approximating the probability density functions of the features of the samples used in their training. These neural networ ks also present the advantage of generating an output that indicates the confidence level that it has in its answer. We have endeavored to evaluate the performances and the possibility of integrating such neural networks in an infrared imaging seeker emulator, devised by the Defense Research and Development establishment at Valcartier, for countermeasure studies. We describe, in this article, the characteristics that are extracted from the images and define translation invariant features from these. We then give a mathematical basis for the selection of which features to use as input for the neural network, in order to optimize its discriminating power. Finally, we build the neural network and test it on some real data. The results of recognition tests are shown, which indicate a remarkable efficiency: our neural network has a success rate of over 98%. An examination of the images on which it makes its mistakes shows that, for most, even a human expert would probably have also been mistaken. We then build a reduced version of this neural network, with 82% fewer neurons, but with only a 0.6% less precision. We show that such a neural network could well be used in a real time system by measuring its computing time on a normal Pentium 4 PC and obtaining a rate of over 5,300 patterns per second.
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