Detection of snow clutter in ATC ground radar signal

The use of adaptive techniques may prove useful in the processing of radar signals. The proposed radar clutter classificator is aimed to improve the detection of snow clutter presence in data acquired by a ground radar system in an air traffic control environment. The classifier receives as input a set of features which describe the appearance of the same plot in two consecutive scans of the ground radar. The feature set includes two group of parameters, which respectively represent the shape of the plot in each scan, and the displacement of the center of mass of the plot between the two scans. The data set used in simulations has been extracted from a measurement campaign carried out in presence of snow in Italian airports, with the help on an expert operator who manually classified a set of plots as target, or clutter. The performance of the classifier, a multilayer perceptron trained with the backpropagation rule, indicates a correct classification rate of about 98%.