The tutorial focuses on AI applications in radar processing chains, emphasizing advancements in signal detection, resource allocation, tracking accuracy, classification, and sensor fusion. It highlights AI-enhanced detection of low signal-to-noise ratio (SNR) targets using machine learning (ML) alternatives, as well as deep learning applications for robust track-before-detection (TBD) schemes in complex cluttered environments.
The tutorial also addresses challenges in resource allocation and multi-target tracking in modern multi-function phased array radars, discussing deep learning and reinforcement learning–based solutions to these problems. In addition, AI applications in multi-sensor environments are explored. Furthermore, the tutorial highlights hybrid cognitive radar architectures, swarm-based multi-static radars, and the role of explainable AI in ensuring reliability and validation for mission-critical systems. These innovations position AI as a transformative force multiplier in modern military radar systems, enabling enhanced performance against emerging threats while addressing computational complexity.