Department of Mechanical Engineering, Ariel University, Ariel 4070000, Israel.
Department of Industrial Engineering and Management, Ariel University, Ariel 4070000, Israel.
Sensors (Basel). 2020 Dec 17;20(24):7239. doi: 10.3390/s20247239.
Many tasks performed by swarms of unmanned aerial vehicles require localization. In many cases, the sensors that take part in the localization process suffer from inherent measurement errors. This problem is amplified when disruptions are added, either endogenously through Byzantine failures of agents within the swarm, or exogenously by some external source, such as a GNSS jammer. In this paper, we first introduce an improved localization method based on distance observation. Then, we devise schemes for detecting Byzantine agents, in scenarios of endogenous disruptions, and for detecting a disrupted area, in case the source of the problem is exogenous. Finally, we apply pool testing techniques to reduce the communication traffic and the computation time of our schemes. The optimal pool size should be chosen carefully, as very small or very large pools may impair the ability to identify the source/s of disruption. A set of simulated experiments demonstrates the effectiveness of our proposed methods, which enable reliable error estimation even amid disruptions. This work is the first, to the best of our knowledge, that embeds identification of endogenous and exogenous disruptions into the localization process.
许多由无人飞行器群执行的任务都需要进行本地化。在许多情况下，参与本地化过程的传感器会受到固有测量误差的影响。当干扰因素被引入时，这个问题会被放大，无论是通过群内代理的拜占庭故障（内在干扰），还是通过某些外部源（如 GNSS 干扰器）（外在干扰）。在本文中，我们首先介绍了一种基于距离观测的改进本地化方法。然后，我们设计了在内在干扰情况下检测拜占庭代理的方案，以及在外部干扰情况下检测干扰区域的方案。最后，我们应用池测试技术来减少我们方案的通信流量和计算时间。最优池大小应该谨慎选择，因为非常小或非常大的池可能会影响识别干扰源的能力。一组模拟实验证明了我们提出的方法的有效性，即使在干扰存在的情况下，这些方法也能实现可靠的误差估计。据我们所知，这是首次将内在和外在干扰的识别嵌入到本地化过程中的工作。
