Cognitive Radar Networks, which were popularized by Simon Haykin in 2006, have been proposed to address limitations with legacy radar installations. These limitations include large physical size, power consumption, fixed operating parameters, and single point vulnerabilities. Cognitive radar solves part of this problem through adaptability, using biologically inspired techniques to observe the environment and adjust operation accordingly. Cognitive radar networks (CRNs) extend the capabilities of cognitive radar spatially, providing the opportunity to observe targets from multiple angles to mitigate stealth effects; distribute resources over space and in time; obtain better tracking performance; and gain more information from a scene. Often, problems of cognition in CRNs are viewed through the lens of iterative learning problems - one or multiple cognitive processes are implemented in the network, where each process first observes the environment, then selects operating parameters (from discrete or continuous options) using the history of observations and previous rewards, then repeats the cycle. Further, cognitive radar networks often are modeled with a flexible architecture and wide-bandwidth front-ends, enabling the addition of electronic support measures such as passive signal estimation. In this work we consider questions of the form "How should a cognitive radar network choose when to observe targets?" and "How can a cognitive radar network reduce the amount of energy it uses?". We implement tools from the multi-armed bandit and age of information literature to select modes for the network, choosing either an active radar mode or a passive signal estimation mode. We show that through the use of target classes, the network can determine how often each target should be observed to optimize tracking performance.