  This work addresses the coexistence problem for radar networks. Specifically,
we model a network of cooperative, independent, and non-communicating radar
nodes which must share resources within the network as well as with
non-cooperative nearby emitters. We approach this problem using online Machine
Learning (ML) techniques. Online learning approaches are specifically preferred
due to the fact that each radar node has no prior knowledge of the environment
nor of the positions of the other radar nodes, and due to the sequential nature
of the problem. For this task we specifically select the multi-player
multi-armed bandit (MMAB) model, which poses the problem as a sequential game,
where each radar node in a network makes independent selections of center
frequency and waveform with the same goal of improving tracking performance for
the network as a whole. For accurate tracking, each radar node communicates
observations to a fusion center on set intervals. The fusion center has
knowledge of the radar node placement, but cannot communicate to the individual
nodes fast enough for waveform control. Every radar node in the network must
learn the behavior of the environment, which includes rewards, interferer
behavior, and target behavior. Our contributions include a mathematical
description of the MMAB framework adapted to the radar network scenario. We
conclude with a simulation study of several different network configurations.
Experimental results show that iterative, online learning using MMAB
outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation
approaches.
