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

Sensor Allocation and Online-Learning-based Path Planning for Maritime Situational Awareness Enhancement: A Multi-Agent Approach