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.

翻译：拥有大型水域的国家通常致力于通过部署海上监视系统来保护其海上运输。然而，可用的传感器（例如摄像头）数量通常远少于待监控目标，且其视场角和感知范围往往有限。这使得提升海上运输的情境感知能力面临挑战。为此，我们提出了一种方法，该方法不仅能够分配多个传感器，还能为其规划路径以观测多个目标，同时最小化达到情境感知所需的时间。具体而言，我们对该传感器分配与路径规划问题进行了形式化建模，该模型考虑了目标状态的局部感知性以及目标轨迹的未知性。为求解该问题，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心算法；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。由于快速收敛是为高动态环境设计的算法的必要条件，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，与相关工作相比，我们的组合方法能更快实现情境感知。