Development of an infrared imaging seeker emulator for countermeasure studies

Morin, Andre; Lessard, Pierre

doi:10.1117/12.352868

Cited by 6 publications

(5 citation statements)

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“…Morin and Lessard [52] describe the simulator of a focal plane array infrared imaging seeker and tracker that has been developed at the Defense Research and Development Canada establishment at Valcartier. Its purpose is to test and improve the individual components of operational seekers and trackers.…”

Section: Ir Seeker Test Bedmentioning

confidence: 99%

Infrared target-flare discrimination using a ZISC hardware neural network

Labonté

Deck

2009

J Real-Time Image Proc

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The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile's field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint. It is based on an artificial neural network implemented in hardware, as a set of parallel processors on a commercially available silicon chip called a ZISC, for zero instruction set computer. This chip would be integrated in the infrared missile seeker and tracker. We describe the characteristics of the images that the image processing module of this seeker and tracker extracts from the infrared video frames and show how to construct from these translation and rotation invariant features that can be used as input to the neural network. We determine the individual discriminating power of these features by constructing their histograms, which allows us to eliminate some as not being useful for our purpose. Finally, by testing our system on real data, we show that it has a 90% success rate in aircraft-flare identification, and a processing time that during this time, the aircrafts and missiles will have traveled only a few millimeters. Most of the images on which the neural network makes its mistakes are seen to be hard to recognize even by a human expert.

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Section: Ir Seeker Test Bedmentioning

confidence: 99%

Infrared target-flare discrimination using a ZISC hardware neural network

Labonté

Deck

2009

J Real-Time Image Proc

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“…When a target of interest is recognized and selected, tracking commands are generated for the control system that moves the gimbaled platform and keeps it locked on that particular target. A detailed description of this imaging tracker can be found in Morin and Lessard [10]. Figure 1 shows that its basic configuration has the following processing stages.…”

Section: Introductionmentioning

confidence: 99%

“…The spatial filters are adaptive because targets vary considerably in size and perceptible details, according to their distance from the camera. The relative efficiency of various filters has been examined in Morin and Lessard [10] and Morin [9].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic neural networks for infrared imaging target discrimination

2003

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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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“…When a target of interest is recognized and selected, tracking commands are sent to the control system that moves the gimbaled platform and keeps it locked on that particular target. A detailed description of this imaging tracker can be found in Morin and Lessard [9]. Figure 1 shows its basic configuration with the following processing stages.…”

Section: Introductionmentioning

confidence: 99%

“…Each frame is spatially filtered with a convolution mask of dimension 7×7 in order to facilitate the image segmentation by reducing the noise and attenuating the background clutter. The relative efficiency of various filters was examined by Morin and Lessard [9] and Morin [8].…”

Section: Introductionmentioning

confidence: 99%

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

Labonté

Morin

2004

SPIE Proceedings

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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.

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Development of an infrared imaging seeker emulator for countermeasure studies

Cited by 6 publications

References 0 publications

Infrared target-flare discrimination using a ZISC hardware neural network

Infrared target-flare discrimination using a ZISC hardware neural network

Probabilistic neural networks for infrared imaging target discrimination

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

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