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: \emph{1)} a greedy algorithm for assigning sensors to targets, and \emph{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.

翻译：拥有大片水域的国家往往通过部署海上监视系统来保护其海上运输。然而，可用传感器（如相机）的数量通常远小于待监视目标的数量，且其视场和探测范围有限。这使得提升海上运输的态势感知能力充满挑战。为此，我们提出一种方法，不仅分配多个传感器，还为其规划路径以观察多个目标，同时最小化达成态势感知所需的时间。具体而言，我们对该传感器分配与路径规划问题进行了数学建模，该模型考虑了目标状态的局部可观测性以及目标轨迹的未知性。为解决该问题，我们提出了两种算法：1）一种用于将传感器分配给目标的贪婪算法；2）一种基于遗憾匹配学习的分布式多智能体路径规划算法。由于快速收敛是高机动环境算法开发的必要条件，我们引入遗忘因子以快速收敛到相关均衡解。实验结果表明，我们的联合方法比相关工作能更快达成态势感知。