The Zero Instruction Set Computer (ZISC) is an integrated circuit devised by IBM to realize a restricted Coulomb
energy neural network. In our application, it functions as a parallel computer that calculates the correlation
coefficients between an input pattern and patterns stored in its neurons. We explored the possibility of using the
ZISC in a target tracking system by devising algorithms to take advantage of the ZISC's parallelism and testing
them on real video sequences. Our experiments indicate that the ZISC does improve appreciably the computing
time compared to a sequential version of the algorithm.
The next generation of infrared imaging trackers and seekers will incorporate 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. One approach consists in identifying targets with the help of pattern recognition algorithms that use features extracted from all possible target images observed in the missile's field of view. Artificial neural networks are known to be a tool of choice for such pattern classification tasks. For the situation at hand, probabilistic neural networks are particularly interesting because their performances can approach those of optimal Bayesian classifiers and they output an estimate of the actual probability that a target belongs to one class or another. We have endeavoured to evaluate the performances and the possibility of integrating such neural networks in the infrared imaging seeker emulator developed by Defense Research and Development Canada (DRDC) at Valcartier. The results reported here constitute a follow up on a preceding study in which a neural network was used to discriminate between aircrafts and flares from measured properties of their static images. In the present study, we consider the time evolution of image features. In particular, we define temporal characteristics of blob intensities and shapes that can be measured over a few frames and used to differentiate between aircrafts and flares. We build a neural network that uses these characteristics as input and which outputs the probability that an aircraft or a flare is being observed. We show the very positive results we have obtained in tests conducted with some real data.
The next generation of infrared imaging trackers and seekers will allow for the implementation of more smarter tracking algorithms, able to keep a positive lock on a targeted aircraft in the presence of countermeasures. Pattern recognition algorithms will be able to select targets based on features extracted from all possible targets images. Artificial neural networks provide an important class of such algorithms. In particular, probabilistic neural networks perform almost as optimal Bayesian classifiers, by approximating the probability density functions of the features of the objects. Furthermore, these neural networks generate an output that indicates the confidence it has in its answer. We have evaluated the the possibility of integrating such neural networks in an infrared imaging seeker emulator, devised by the Defense Research and Development establishment at Valcartier. We describe the characteristics extracted from the images and define translation invariant features from these. We give a basis for the selection of which features to use as input for the neural network. We build the network and test it on some real data. Results are shown, which indicate a remarkable efficiency of over 98% correct recognition. For most of the images on which the neural network makes its mistakes, even a human expert would probably have been mistaken. We build a reduced version of this network, with 82% fewer neurons, and only a 0.6% less precision. Such a neural network could well be used in a real time system because its computing time on a normal PC gives a rate of over 5,300 patterns per second.