A neural network solution for identification and classification of cylindrical targets above perfectly conducting flat surfaces

This paper evaluates the radar target identification and classification performance of neural networks. A set of features are derived from scattered fields calculated by using the image technique formulation and Moment Method (MoM). An RBF (Radial Basis Function) network that utilizes the feature set is proposed for target identification and classification. The database contains a finite number of samples of cylindrical targets at certain angles. A portion of the database is used to train the network and the rest is used to test the performance of the neural network for target identification and classification. This work aims to find the heights measured from the surface and radiuses of the targets for identification of targets and determine the right target for classification of targets from the scattered field values.