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Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach
arXiv - CS - Multiagent Systems Pub Date : 2020-01-13 , DOI: arxiv-2001.04560
Anna Guerra, Davide Dardari, Petar M. Djuric

Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations.

中文翻译:

无人机的动态雷达网络:联合导航和跟踪方法

如今,人们对利用自主传感和在线导航功能丰富小型飞行机器人的可能性越来越感兴趣。这将使大量应用成为可能,从远程监控到物流、智慧城市和危险环境中的紧急援助。在这种情况下,一个新出现的问题是跟踪隐藏在建筑物后面或隐藏在大型无人机网络中的未经授权的小型无人机 (UAV)。与目前主要基于静态和地面雷达的解决方案相比,本文提出了无人机动态雷达网络的思想,用于实时、高精度跟踪恶意目标。为此,我们描述了无人机实时导航的解决方案,以使用异构感测信息跟踪动态目标。这些信息由无人机通过多跳与其邻居共享,允许通过在每个代理上运行的本地贝叶斯估计器跟踪目标。由于并非所有路径在信息收集观点方面都是相同的,因此无人机通过在无人机运动学和防碰撞约束下最小化目标状态的后验协方差矩阵来规划自己的轨迹。我们的结果表明,与固定配置相比,动态雷达网络如何获得更好的定位结果,以及机载传感器技术如何影响跟踪具有不同雷达截面的目标的准确性,尤其是在非视距 (NLOS) 中情况。由于并非所有路径在信息收集观点方面都是相同的,因此无人机通过在无人机运动学和防碰撞约束下最小化目标状态的后验协方差矩阵来规划自己的轨迹。我们的结果表明,与固定配置相比,动态雷达网络如何获得更好的定位结果,以及机载传感器技术如何影响跟踪具有不同雷达截面的目标的准确性,尤其是在非视距 (NLOS) 中情况。由于并非所有路径在信息收集观点方面都是相同的,因此无人机通过在无人机运动学和防碰撞约束下最小化目标状态的后验协方差矩阵来规划自己的轨迹。我们的结果表明,与固定配置相比,动态雷达网络如何获得更好的定位结果,以及机载传感器技术如何影响跟踪具有不同雷达截面的目标的准确性,尤其是在非视距 (NLOS) 中情况。
更新日期:2020-07-23
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