Automatic Target Recognition |
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The main objective of this project is to develop a highly efficient automatic target recognition (ATR) system by exploiting model based image processing and neural networks. Our proposed technique offers the unique capability for both real-time realization and adaptive processing within the context of a reliable, noise-robust and invariant ATR system. In this work, a generalized image processing technique will be developed for real time ATR applications where it is necessary to rapidly update the reference image in order to detect one or more static or moving targets as well as tracking applications. The neuro-computing technique, on the other hand, possesses the capability of fast and adaptive distortion-invariant pattern recognition for rapidly changing targets. Thus, we implement adaptive neuro-computing to perform distortion invariant pattern recognition task. A set of metrics is developed in order to estimate the reliability of signal detection in the input scene with respect to clutter, noise and other associated distortions. The performance of the proposed technique is investigated via extensive simulation and experimentation.
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