Statistical Properties and Airspace Capacity for Unmanned Aerial Vehicle Networks Subject to Sense-and-Avoid Safety Protocols
Statistical Properties and Airspace Capacity for Unmanned Aerial Vehicle Networks Subject to Sense-and-Avoid Safety Protocols
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DOI：
10.1109/tits.2020.3040520
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发表时间：
2021-09
影响因子：
8.5
通讯作者：
Mushuang Liu;Yan Wan;F. Lewis;E. Atkins;D. Wu
中科院分区：
文献类型：
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作者：
Mushuang Liu;Yan Wan;F. Lewis;E. Atkins;D. Wu
随机移动模型（Random Mobility Models，RMM）描述了移动的Agent的随机移动模式，已被广泛应用于移动的网络的评估和设计。文献中所有现有的Rendezvous假设独立运动的移动的代理，这并不适用于无人驾驶飞机系统（UAS）。特别是，无人机必须保持安全的间隔距离，以避免碰撞。在本文中，我们提出了一个新的建模框架，随机移动模型配备了物理感测和避免协议，以捕捉灵活的，可变的，和不确定的运动模式的UAS分离的安全约束。对于随机方向（RD）RMM配备了一个常用的感知和避免（S&A）协议，命名为感知和停止（S&S），我们提供了它的统计特性，包括平稳的位置分布和平稳的车辆间距离分布，使用马尔可夫分析。这项研究提供了S&A协议对关键UAS网络统计数据的影响的知识。此外，我们定义了碰撞概率和空域容量的概念，无人机之间的车辆距离分布的基础上，并推导出其封闭形式的表达式。该分析框架在数学上将局部自治与全球空域容量连接起来，并允许对局部自治配置进行影响分析，以实现有效的UAS空域容量管理。
Random mobility models (RMMs) capture the random mobility patterns of mobile agents, and have been widely used as the modeling framework for the evaluation and design of mobile networks. All existing RMMs in the literature assume independent movements of mobile agents, which does not hold for unmanned aircraft systems (UASs). In particular, UASs must maintain a safe separation distance to avoid collision. In this paper, we propose a new modeling framework of random mobility models equipped with physical sense-and-avoid protocols to capture the flexible, variable, and uncertain movement patterns of UASs subject to separation safety constraints. For the random direction (RD) RMM equipped with a commonly used sense-and-avoid (S&A) protocol, named sense-and-stop (S&S), we provide its statistical properties including stationary location distribution and stationary inter-vehicle distance distribution, using the Markov analysis. This study provides knowledge on the impact of S&A protocols to critical UAS networking statistics. In addition, we define collision probabilities and airspace capacity concepts for UASs based on the inter-vehicle distance distribution, and derive their closed-form expressions. This analytical framework mathematically bridges local autonomy with global airspace capacity, and allows the impact analysis of local autonomy configurations for effective UAS airspace capacity management.