  Countries with access to large bodies of water often aim to protect their
maritime transport by employing maritime surveillance systems. However, the
number of available sensors (e.g., cameras) is typically small compared to the
to-be-monitored targets, and their Field of View (FOV) and range are often
limited. This makes improving the situational awareness of maritime transports
challenging. To this end, we propose a method that not only distributes
multiple sensors but also plans paths for them to observe multiple targets,
while minimizing the time needed to achieve situational awareness. In
particular, we provide a formulation of this sensor allocation and path
planning problem which considers the partial awareness of the targets' state,
as well as the unawareness of the targets' trajectories. To solve the problem
we present two algorithms: 1) a greedy algorithm for assigning sensors to
targets, and 2) a distributed multi-agent path planning algorithm based on
regret-matching learning. Because a quick convergence is a requirement for
algorithms developed for high mobility environments, we employ a forgetting
factor to quickly converge to correlated equilibrium solutions. Experimental
results show that our combined approach achieves situational awareness more
quickly than related work.
