Comparison of data analysis and classification algorithms for automatic target recognition

Radar range-profile classification of targets in clutter (automatic target recognition) is particularly challenging because of the multimodal nature of the data classes. Typically, classical techniques are not adequate, or must be customized to provide good classification performance. The “cut and try” method of model synthesis is time consuming and may never yield the insight necessary for good classifier performance on unseen data. Inductive classification algorithms are appropriate for these challenging problems because they not only synthesize classifiers, but provide critical information about the data itself which can then be used for further refining (or redefining) of classifier inputs and synthesis strategies. This paper describes one problem solution using radar turntable data for classifier training and testing. Classical classifiers (nearest mean and Fischer pairwise) are compared to polynomial neural network (PNN) and multilayer perceptron (MLP) classifiers