We report on our irradiation experiments on different types of fiber-optic sensors, including three types of commercially available temperature sensors, a multimode extrinsic Fabry-Perot cavity strain sensor and fiber Bragg-gratings. For the temperature sensors, results show that gamma radiation does not interfere with the basic sensing mechanism and that the most critical component turns out to be the optical fiber itself. Semiconductor absorption temperature sensors showed no degradation up to total doses of 250 kGy, whereas the specifications of Fabry-Perot type sensors and fluorescence temperature sensors were already dramatically influenced below the kGy-level. Replacing the optical fiber by a more radiation resistant version allowed to increase the radiation hardness of the fluorescence sensor system by orders of magnitude. The use of fiber-optic sensors in the presence of neutron radiation remains compromised. Similar conclusions are valid for the Fabry-Perot type fiber-optic strain sensors. We finally show that the Bragg-grating resonance wavelength can shift with radiation dose, but that the temperature sensitivity remains unaltered.
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.
Automatic target detection and tracking for an infrared imaging seeker is a complex topic. The JR sensor images are processed in real time to detect and discriminate the targets in the seeker's field of view (FOV). A multitude of image processing and target discrimination algorithms can be implemented in the seeker. The complexity ofthese algorithms must take into account the potential processing limitation ofthe seeker on-board processor. Tunable band-pass spatial filters have been considered to optimize the target detection in the input image ofthe seeker. The spatial frequency features are extracted from the target which is selected by the tracking algorithm. The center spatial frequency of the filter is then automatically tuned to that of the target. Various techniques to derive two-dimensional tunable band-pass filters are presented in this paper. The convolution windows must be characterized by a limited size for real-time application. The sensitivity ofthe filters to an amplitude threshold variation in the target detection will be evaluated.
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