Cognitive Radar

Cognitive radar systems dynamically adapt their sensing strategies based on environmental feedback, aiming to optimize performance in complex and uncertain scenarios. Current research emphasizes the development and analysis of learning-based algorithms, such as reinforcement learning and Bayesian meta-learning, to enable efficient waveform selection and robust target tracking. A key challenge involves understanding and mitigating the vulnerabilities of these intelligent systems to adversarial attacks, leading to investigations into techniques for masking the radar's decision-making process. This field holds significant promise for improving radar performance in diverse applications, particularly those involving contested or dynamic environments.