1 May 2000 Relative performance of correlation-based and neural-network-based classifiers of aircraft using synthetic radar range profiles
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Abstract
Target recognition can greatly enhance the usefulness of radar systems. For speed, optical architectures are the preferred method of implementation for the classifiers. Both correlators and feature-based neural networks have elegant optical implementations. The choice of which recognition system to use is problem-dependent. We evaluate the relative performance of correlation- and feature-based (neural network) classifiers on a set of four simulated radar targets over a wide range of target orientations. Experiments are performed for a range of radar bandwidths to determine the effect of radar bandwidth on the relative classification performance. Only 1-D radar range profiles are considered since it is assumed that the targets are classified using few profiles and the orientation of the target in each profile is known only approximately. The results suggest that feature-based classifiers outperform correlationbased classifiers and that classification performance is highly dependent on the orientation of the aircraft, but that accurate classification of approaching targets is possible
Christophe Nieuwoudt, Christophe Nieuwoudt, Elizabeth C. Botha, Elizabeth C. Botha, } "Relative performance of correlation-based and neural-network-based classifiers of aircraft using synthetic radar range profiles," Optical Engineering 39(5), (1 May 2000). https://doi.org/10.1117/1.602476 . Submission:
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