<title>Probabilistic neural network, for infrared target discrimination using their temporal behavior</title>

Abstract

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.

Infrared target-flare discrimination using a ZISC hardware neural network

“…In that sense, the aircraft and flare discrimination problem is also the most crucial one. In the present study, we also used only static features of the images, even though dynamic features also have strong discriminating power as shown, as was demonstrated in Labonté and Morin [42]. We shall consider dynamic characteristics in a subsequent study.…”

“…They reported success rates in the 90-95% range. Labonté and Morin [42] then used temporal features of the objects, extracted from a few successive video frames, to discriminate between the aircrafts and the flares. They report a success rate of 90-100%.…”

Multispectral image analysis for object recognition and classification

“…Cayouette, Labonté and Morin [28] [29] continued the work previously started with Cayouette [28] on the use of PNN to discriminate target aircraft and flares in static images. In the follow-up study, Labonté and Morin considered the time evolution of the image features from a series of frames.…”