Ground moving radar
target classification is one of the recent research issues that has arisen in
an airborne ground moving target indicator (GMTI) scenario. This work presents
a novel technique for classifying individual targets depending on their radar
cross section (RCS) values. The RCS feature is evaluated using the Chebyshev
polynomial. The radar captured target usually provides an imbalanced solution
for classes that have lower numbers of pixels and that have similar
characteristics. In this classification technique, the Chebyshev polynomial?s
features have overcome the problem of confusion between target classes with
similar characteristics. The Chebyshev polynomial highlights the RCS feature
and is able to suppress the jammer signal. Classification has been performed by
using the probability neural network (PNN) model. Finally, the classifier with
the Chebyshev polynomial feature has been tested with an unknown RCS value. The
proposed classification method can be used for classifying targets in a GMTI
system under the warfield condition.
