  Completely decentralized Multi-Player Bandit models have demonstrated high
localization accuracy at the cost of long convergence times in cognitive radar
networks. Rather than model each radar node as an independent learner, entirely
unable to swap information with other nodes in a network, in this work we
construct a "central coordinator" to facilitate the exchange of information
between radar nodes. We show that in interference-limited spectrum, where the
signal to interference plus noise (SINR) ratio for the available bands may vary
by location, a cognitive radar network (CRN) is able to use information from a
central coordinator to reduce the number of time steps required to attain a
given localization error. Importantly, each node is still able to learn
separately. We provide a description of a network which has hybrid cognition in
both a central coordinator and in each of the cognitive radar nodes, and
examine the online machine learning algorithms which can be implemented in this
structure.
