  Cognitive Radar Networks were proposed by Simon Haykin in 2006 to address
problems with large legacy radar implementations - primarily, single-point
vulnerabilities and lack of adaptability. This work proposes to leverage the
adaptability of cognitive radar networks to trade between active radar
observation, which uses high power and risks interception, and passive signal
parameter estimation, which uses target emissions to gain side information and
lower the power necessary to accurately track multiple targets. The goal of the
network is to learn over many target tracks both the characteristics of the
targets as well as the optimal action choices for each type of target. In order
to select between the available actions, we utilize a multi-armed bandit model,
using current class information as prior information. When the active radar
action is selected, the node estimates the physical behavior of targets through
the radar emissions. When the passive action is selected, the node estimates
the radio behavior of targets through passive sensing. Over many target tracks,
the network collects the observed behavior of targets and forms clusters of
similarly-behaved targets. In this way, the network meta-learns the target
class distributions while learning the optimal mode selections for each target
class.
