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Ground moving radartarget classification is one of the recent research issues that has arisen inan airborne ground moving target indicator (GMTI) scenario. This work presentsa novel technique for classifying individual targets depending on their radarcross section (RCS) values. The RCS feature is evaluated using the Chebyshevpolynomial. The radar captured target usually provides an imbalanced solutionfor classes that have lower numbers of pixels and that have similarcharacteristics. In this classification technique, the Chebyshev polynomial?sfeatures have overcome the problem of confusion between target classes withsimilar characteristics. The Chebyshev polynomial highlights the RCS featureand is able to suppress the jammer signal. Classification has been performed byusing the probability neural network (PNN) model. Finally, the classifier withthe Chebyshev polynomial feature has been tested with an unknown RCS value. Theproposed classification method can be used for classifying targets in a GMTIsystem under the warfield condition. |
