Ground Moving Radar Targets Classification Based on Spectrogram Images Using Convolutional Neural Networks

In this paper, a new approach for classifying ground moving targets captured by Pulsed Doppler Radars is proposed. Radar echo signals express the doppler effect produced by the movement of targets. Those signals can be processed in different domains to attain distinctive characteristics of targets that can be used for target classification. Our proposed approach is based on utilizing a pre-trained Convolutional Neural Network (CNN), VGG16 and VGG19, as feature extractors whereas the output features were used to train a multiclass support vector machine (SVM) classifier. To evaluate our approach, we used RadEch database of 8 ground moving targets classes. Our approach outperformed the state of the art methods, using the same database, with an accuracy of 96.56%.