Timely Target Tracking in Cognitive Radar Networks

Abstract

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.