Probabilistic neural networks for target discrimination using their temporal behavior

摘要

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.