Waveform Selection

Waveform selection aims to optimize the radar signals used for target detection and tracking by adapting them to the specific environment and task. Current research heavily focuses on online learning methods, particularly reinforcement learning and Bayesian meta-learning, to enable adaptive waveform selection in dynamic scenarios. These approaches leverage algorithms like Thompson sampling and Context-Tree Weighting to improve generalization and efficiency across diverse environments, addressing limitations of traditional rule-based and estimation-theoretic methods. This research is significant for enhancing the performance and robustness of radar systems in applications such as autonomous driving and surveillance.