Target Classification in Radar Using Deep Learning

In radar systems, precise target classification is essential for effective situational awareness. We developed a Convolutional Neural Network (CNN)-based approach to classify radar targets using their signal characteristics automatically. Compared to traditional methods, our solution demonstrates higher accuracy, robustness, and computational efficiency while addressing challenges like data labelling, interpretability, and signal variability. The results highlight the potential of deep learning to transform radar systems, enhancing applications in military surveillance and autonomous navigation. This work marks a significant step toward seamlessly integrating deep learning with radar technologies.