Deep Learning-Based Radar Signal Classification for Automated Target Detection

Radar systems generate complex signal data that must be processed rapidly and accurately to detect and classify potential targets. Conventional radar signal-processing techniques can encounter difficulties when signals are affected by noise, clutter, interference, low signal-to-noise ratios, and changing target characteristics. Deep learning provides an alternative approach by automatically learning discriminative representations from radar signal data and applying them to automated classification and target detection. This study examines deep learning-based radar signal classification for automated target detection through a structured literature-based analysis. The study considers convolutional neural networks, recurrent neural networks, long short-term memory networks, transfer learning, time-frequency representations, and emerging Transformer-based architectures. Particular attention is given to automated radar signal analysis using wavelet preprocessing and LSTM modelling, together with recent research on radar target detection and recognition. The proposed framework conceptualizes radar classification as a sequence of signal preprocessing, feature representation, deep learning classification, target detection, and decision generation. The analysis indicates that deep learning can reduce dependence on manually engineered features while supporting automated extraction of temporal, spectral, and spatial characteristics. The study also identifies challenges involving noisy environments, limited training data, class imbalance, computational requirements, model generalization, and real-time deployment. The resulting framework provides a foundation for automated radar signal classification and target detection in complex sensing environments.