Aerial Radar Target Classification using Artificial Neural Networks

In this paper, we propose a new algorithm for classification of aerial radar targets by using Radar Cross Section (RCS) time-series corresponding to target detections of a given track. RCS values are obtained directly from SNR values, according to the radar equation. The classification is based on analysing the behaviour of the RCS time-series, which is the unique "fingerprint" of an aerial radar target. The classification process proposed in this paper is based on training a fully-connected neural network on features extracted from the RCS time-series and its corresponding Intrinsic Mode Functions (IMFs). The training is based on a database containing RCS signatures of various aerial targets. The algorithm has been tested on a large and diverse set of simulative flight trajectories, and its performance has been compared with that of several different methods. We have found that the proposed neural network-based classifier performed better on our database.