Machine learning-driven advancements in next-generation cognitive radar systems

Cognitive radar systems are an important tool in defense systems and autonomous vehicles because they are able to adapt to changing conditions and constantly enhance performance. Nonetheless, there are usually shortcomings in the efficacies of current radar technologies, their accuracy, and their strength. This paper discusses how it is possible to achieve these challenges to make radar much more effective using Machine Learning (ML). The investigation of significant machine learning methodologies, including supervised learning and deep reinforcement learning, has promise for minimizing clutter, facilitating real-time decision-making, and enhancing radar signal interpretation. Furthermore, we examine the most recent research, methodologies, and technologies that illustrate the application of machine learning in radar systems. This work introduces the Machine Learning-Based Advanced Cognitive Radar System (ML-ACRS), a novel model designed to improve target recognition, environmental adaption, and radar signal processing. We analyze recent advancements and prospective opportunities in cognitive radar technology by evaluating ML-ACRS, aiming to evolve radar systems into more intelligent, adaptive, and resilient entities capable of addressing the intricate operational challenges of the future.