Autonomous Drone Networks for Sensing, Localizing and Approaching RF Targets
Autonomous Drone Networks for Sensing, Localizing and Approaching RF Targets
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DOI：
10.1109/vnc51378.2020.9318347
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Zhambyl Shaikhanov;Ahmed Boubrima;E. Knightly
中科院分区：
文献类型：
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作者：
Zhambyl Shaikhanov;Ahmed Boubrima;E. Knightly
我们介绍了一种新型的自主无人机网络系统，用于感知，定位和接近RF目标/源，如智能手机设备。我们的系统的潜在应用包括救灾使命，其中联网的无人机感测从受害者的智能手机发出的Wi-Fi信号，并动态导航以准确定位并快速接近受害者，例如，提供时间紧迫的急救包。为此，我们利用Wi-Fi最近的精细时间测量（FTM）协议来实现第一个无人机上的FTM传感器网络，该网络能够在使命中对目标进行准确和动态的测距。我们提出了一个飞行规划策略，适应无人机的轨迹，同时有利于定位和接近目标。也就是说，我们的方法共同优化了无人机的观测多样性，同时也接近目标，同时灵活地权衡潜在冲突目标的强度。我们通过定制设计的多无人机平台实现了MIPCON，与基线群集方法相比，定位精度高达2\times $，同时定位目标的时间减少了30%。
We present FALCON, a novel autonomous drone network system for sensing, localizing, and approaching RF targets/sources such as smartphone devices. A potential application of our system includes a disaster relief mission in which networked drones sense the Wi-Fi signal emitted from a victim's smartphone and dynamically navigate to accurately localize and quickly approach the victim, for instance, to deliver the time-critical first-aid kits. For that, we exploit Wi-Fi‘s recent Fine Time Measurement (FTM) protocol to realize the first on-drone FTM sensor network that enables accurate and dynamic ranging of targets in a mission. We propose a flight planning strategy that adapts the trajectory of the drones to concurrently favor localizing and approaching the target. Namely, our approach jointly optimizes the drones' diversity of observations while also approaching the target, while flexibly trading off the intensities of the potentially conflicting objectives. We implement FALCON via a custom-designed multi-drone platform and demonstrate up to $2\times$ localization accuracy compared to a baseline flocking approach, while spending 30% less time localizing targets.