A UAV-enabled Dynamic Multi-Target Tracking and Sensing Framework
A UAV-enabled Dynamic Multi-Target Tracking and Sensing Framework
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DOI：
10.1109/globecom42002.2020.9322567
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Nathan Patrizi;G. Fragkos;Kendric R. Ortiz;Meeko Oishi;E. Tsiropoulou
中科院分区：
文献类型：
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作者：
Nathan Patrizi;G. Fragkos;Kendric R. Ortiz;Meeko Oishi;E. Tsiropoulou
提出了一种基于无人机的动态多目标跟踪与数据采集框架。首先，引入一个整体信誉模型来评估目标将有用数据卸载到无人机的潜力。在该模型的基础上，结合无人机和目标跟踪感知的特点，实现了无人机与目标的动态智能匹配。在这样的设置中，目标执行数据卸载的激励是基于无人机向目标提供的基于努力的定价。对于确定每个目标的最佳卸载数据量和无人机向目标提供的相应的基于努力的价格的新出现的优化问题，被视为每个目标和相关无人机之间的Stackelberg博弈。证明了Stackelberg均衡解的存在唯一性和收敛性质。文中给出了详细的数值结果，突出了所提议的框架的主要操作特征和性能优势。
In this paper an Unmanned Aerial Vehicles (UAVs) - enabled dynamic multi-target tracking and data collection framework is presented. Initially, a holistic reputation model is introduced to evaluate the targets’ potential in offloading useful data to the UAVs. Based on this model, and taking into account UAVs and targets tracking and sensing characteristics, a dynamic intelligent matching between the UAVs and the targets is performed. In such a setting, the incentivization of the targets to perform the data offloading is based on an effort-based pricing that the UAVs offer to the targets. The emerging optimization problem towards determining each target’s optimal amount of offloaded data and the corresponding effort-based price that the UAV offers to the target, is treated as a Stackelberg game between each target and the associated UAV. The properties of existence, uniqueness and convergence to the Stackelberg Equilibrium are proven. Detailed numerical results are presented highlighting the key operational features and the performance benefits of the proposed framework.