Dr. David Anthony Coppeta Profile

Dr. David Anthony Coppeta

at Laser Light Engines

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Proceedings Article | 1 November 1991 Paper

Hybrid digital/optical ATR system

David Goodwin, Gregory Cappiello, David Coppeta, Jacques Govignon

Proceedings Volume 1564, (1991) https://doi.org/10.1117/12.49740

KEYWORDS: Fourier transforms, Optical correlators, Spatial light modulators, Automatic target recognition, Optical signal processing, Optical filters, Neural networks, Sensors, Phase only filters, Cameras

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At Draper Laboratory we are investigating applications of optical processing architectures as they apply to guidance and navigation. In doing so, we have developed a breadboard automatic target recognition (ATR) system that classifies and locates an object regardless of orientation. This system contains a neural network (NN) for object classification and filter selection, and a correlator for extraction of positional information. Correlators suffer from the fact that a given filter is fairly sensitive to variations in scale and rotation of the object under scrutiny. For ATR systems that must locate an object regardless of its orientation, a method of choosing the right filter must be implemented. We have built a system that uses a neural network, implemented in a PC, to classify an object based on the magnitude of its Fourier Transform--a shift invariant function. The result of this classification is then used to select a filter to be displayed on the filter plane spatial light modulator (SLM) in our correlator. The correlator uses a commercial liquid crystal television for gray-scale input and a magneto-optic SLM to display a binary phase-only filter. This architecture not only gives us a non-sequential means of choosing the filter but the resulting correlation can be seen as a confidence factor on the neural network classification. In developing our system we have studied various aspects of NN and correlator performance such as sensitivity to clutter, rotation, and phase aberrations. We have shown this system to be capable of classifying and locating an object regardless of its position and rotation.

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