Naval surface vessels, both manned and unmanned, are equipped with multi-
modal sensor systems that must process and make sense of complex operating
environments for optimal mission performance and survivability. Autonomous
sensor resource management increases the effectiveness of a group of sensors and
the efficiency of each individual sensor. In this research we focus on optimizing
autonomous sensor resource management for an individual radar system. Radar
resource management is the process by which a radar system will allocate its
resources between various tasks, viz., scanning in order to detect previously un-
detected targets, as well as tracking previously detected targets. A sub-problem
of the resource management problem is radar time allocation, where the focus
is on determining how much time is to be spent on scanning for new targets,
and how much time is to be spent on tracking each target that has already been
detected. Because of the sheer volume of parameters in a radar system, tra-
ditional methods of solving these problems often involve heuristic algorithms,
or they utilize simple techniques that may achieve unsatisfactory performance.
However, as recent works have shown, advances in reinforcement learning (RL)
have opened up solutions in this space that can create significant improvements
in performance. Our ongoing joint industry-academia collaboration between
Syracuse University and Saab, Inc., which is a supplier of U.S. Navy and U.S.
Marine Corps radar systems, demonstrates the capabilities of RL to address the
time allocation problem in both a multi-target tracking setting and a scan while
tracking instance.
In multi-target tracking, we must decide on how to allocate the dwell times
of a particular radar such that tracking performance is maximized across a set
of targets in varying modes of flight operation. In the “scan while tracking”
instance, the problem is to determine the correct time allotment such that a
radar system is capable of discovering all targets in a scene while continuing to
accurately track all detected targets. We explore different RL algorithms along
with carefully designed cost functions to train agents capable of achieving state-of-the-art results on these problems. Further, we present results to demonstrate
the potential of these agents to generalize to other related problems in this
space. Simulation software has been implemented, and simulations are carried
out in near-realistic simulation environments (e.g., with targets whose velocities
change in magnitude and direction), demonstrating the potential for real-world
applications of these RL agents. We compare the RL approach with static and
heuristic time allocation strategies, especially in the context of the possibility
of sudden changes in the environment, with several new targets emerging unex-
pectedly during the radar surveillance operation, necessitating dynamic changes
to the relative magnitudes of time slots allocated to scanning and tracking var-
ious targets.

Arick Grootveld

1

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