Cognitive 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. / Doctor of Philosophy / Cognitive radar was inspired by biological models, where animals such as dolphins or bats use vocal pulses to form a model of their environment. As these animals seek after prey, they use information they observe to modify their vocal pulses. Cognitive radar networks are an extension of this model to a group of radar devices, which must work together cooperatively to detect and track targets. As the scene changes in time, the radar nodes in the cognitive radar network must change their operating parameters to continue performing well. This networked problem has issues not present in the single-node cognitive radar problem. In particular, as each node in the network changes operating parameters, it risks degrading the performance of the other nodes. In the contribution of this dissertation, we investigate the techniques that a cognitive radar network can use to avoid these cases of mutual performance degradation, and in particular, we investigate how this can be done without advance coordination between the nodes. In the second contribution, we go on to explore what performance improvements are available as central control is introduced. The third and fourth contributions investigate further efficiencies available to a cognitive radar network. The third contribution discusses how a resource-constrained network should communicate updates to a central aggregator. Lastly, the fourth contribution investigates additional estimation tools available to such a network, and how the network should choose between these modes.