Neural Network for polarimetric radar target classification

In this paper, the Artificial Neural Network (ANN) paradigm is applied to radar target classification. Radar returns are simulated via an e.m code and time-domain polarimetric target features are extracted by means of Prony's algorithm. Two different type of feedforward neural network has been adopted in order to classify the target echo, namely the Multi Layer Perceptron (MLP) and the Self Organizing Maps (SOM). The above-mentioned network have been tested on two type of simulated targets: a small tonnage ship with a low level of detail and medium tonnage ship with higher details. Each network has been trained on a wide range of signal-to-noise ratio, and with different data records number in order to assess the training invariant properties of each network. Finally, in the validation phase a fixed number of records has been considered to evaluate networks performances, which are given in terms of classification error.