Machine Learning-Enhanced Radar Signal Classification for Real-Time Object Detection and Tracking

Modern radar systems generate complex signal measurements that must be interpreted rapidly to identify objects, estimate their characteristics, and maintain reliable tracks. Conventional radar signal-processing techniques can encounter difficulties when signals are affected by noise, clutter, interference, low signal-to-noise ratios, and multiple simultaneously observed objects. Machine learning provides an opportunity to enhance radar signal interpretation by learning discriminative representations from radar measurements and using them for signal classification, object detection, and tracking. This study examines machine learning-enhanced radar signal interpretation for real-time object detection and tracking through a structured literature-based analysis. The study considers deep learning, convolutional neural networks, Transformer-based signal representation, range-Doppler processing, radar sensor fusion, target classification, and multi-frame tracking. The analysis establishes a conceptual framework in which radar measurements are transformed into informative signal representations, classified using machine-learning models, and subsequently used to support object localization and temporal tracking. The framework emphasizes the integration of signal classification and detection rather than treating them as independent processing stages. The findings indicate that machine-learning methods can enhance the interpretation of complex radar observations by learning nonlinear signal characteristics and exploiting temporal and spatial information. The study further identifies real-time computational efficiency, robustness to clutter and low signal-to-noise conditions, dataset quality, and model generalization as important considerations for practical deployment.