  We consider a scenario where a fusion center must decide which updates to
receive during each update period in a communication-limited cognitive radar
network. When each radar node in the network only is able to obtain noisy state
measurements for a subset of the targets, the fusion center may not receive
updates on every target during each update period. The solution for the
selection problem at the fusion center is not well suited for sequential
learning frameworks. We derive an Age of Information-inspired track sensitive
metric to inform node selection in such a network and compare it against
less-informed techniques.
