Increasingly inexpensive unmanned aerial vehicles (UAVs) are helpful for searching and tracking moving objects in ground events. Previous works either have assumed that data about the targets are sufficiently available, or they solely rely on on-board electronics (e.g., camera and radar) to chase them. In a searching mission, path planning is essentially preprogrammed before taking off. Meanwhile, a large-scale wireless sensor network (WSN) is a promising means for monitoring events continuously over immense areas. Due to disadvantageous networking conditions, it is nevertheless hard to maintain a centralized database with sufficient data to instantly estimate target positions. In this paper, we therefore propose an online self-navigation strategy for a UAV-WSN integrated system to supervise moving objects. A UAV on duty exploits data collected on the move from ground sensors together with its own sensing information. The UAV autonomously executes edge processing on the available data to find the best direction toward a target. The designed system eliminates the need of any centralized database (fed continuously by ground sensors) in making navigation decisions. We employ a local bivariate regression to formulate acquired sensor data, which lets the UAV optimally adjust its flying direction, synchronously to reported data and object motion. In addition, we also construct a comprehensive searching and tracking framework in which the UAV flexibly sets its operation mode. As a result, least communication and computation overhead is actually induced. Numerical results obtained from NS-3 and Matlab cosimulations have shown that the designed framework is clearly promising in terms of accuracy and overhead costs.

中文翻译：

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自动导航无人机，用于监视大型无线传感器网络上的移动物体

越来越便宜的无人机（UAV）有助于在地面事件中搜索和跟踪运动物体。先前的工作要么假设有关目标的数据足够可用，要么仅依靠机载电子设备（例如，摄像头和雷达）追逐它们。在搜索任务中，在起飞前基本上对路径规划进行了预编程。同时，大规模的无线传感器网络（WSN）是一种在巨大区域连续监视事件的有前途的手段。由于不利的网络条件，仍然很难维护一个具有足够数据的集中式数据库来立即估计目标位置。因此，在本文中，我们为UAV-WSN集成系统提出了一种在线自我导航策略，以对运动物体进行监督。值班无人驾驶飞机利用从地面传感器收集的移动数据及其自身的传感信息。UAV对可用数据自主执行边缘处理，以找到朝向目标的最佳方向。设计的系统消除了进行导航决策时需要任何集中式数据库（由地面传感器连续馈送）的需要。我们采用局部二元回归来制定获取的传感器数据，这使无人机可以最佳地调整其飞行方向，与报告的数据和物体运动同步。此外，我们还构建了一个全面的搜索和跟踪框架，在该框架中，无人机可以灵活地设置其运行模式。结果，实际上引起最少的通信和计算开销。