Howard, William Waddell2023-12-222023-12-222023-12-21vt_gsexam:39076https://hdl.handle.net/10919/117275Cognitive radar networks (CRNs) were first proposed in 2006 by Simon Haykin, shortly after the introduction of cognitive radar. In order for CRNs to benefit from many of the optimization techniques developed for cognitive radar, they must have some method of coordination and control. Both centralized and distributed architectures have been proposed, and both have drawbacks. This work addresses gaps in the literature by providing the first consideration of the problems that appear when typical cognitive radar tools are extended into networks. This work first examines the online learning techniques available to distributed CRNs, enabling optimal resource allocation without requiring a dedicated communication resource. While this problem has been addressed for single-node cognitive radar, we provide the first consideration of mutual interference in such networks. We go on to propose the first hybrid cognitive radar network structure which takes advantage of central feedback while maintaining the benefits of distributed networks. Then, we go on to investigate a novel problem of timely updating in CRNs, addressing questions of target update frequency and node updating methods. We draw from the Age of Information literature to propose Bellman-optimal solutions. Finally, we introduce the notion of mode control, and develop a way to select between active and passive target observation.ETDenCreative Commons Attribution-NonCommercial 4.0 InternationalCognitive radar networksreinforcement learningDissertation