A UAV-enabled Dynamic Multi-Target Tracking and Sensing Framework

被引：38

作者：

Patrizi, Nathan ^{[1
]}

Fragkos, Georgios ^{[1
]}

Ortiz, Kendric ^{[1
]}

Oishi, Meeko ^{[1
]}

Tsiropoulou, Eirini Eleni ^{[1
]}

机构：

[1] Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA

来源：

2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) | 2020年

关键词：

Unmanned Aerial Vehicles; Matching Theory; Reputation Model; Game Theory;

D O I：

10.1109/GLOBECOM42002.2020.9322567

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：6

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