Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems
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集成通信和传感器阵列来建模和导航自主无人机系统

DOI:
10.3390/electronics11193023
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发表时间:
2022-09
期刊:
影响因子:
2.9
通讯作者:
Sirani M. Perera;Rodman J. Myers;Killian Sullivan;Kyle Byassee;H. Song;A. Madanayake
Sirani M. Perera;Rodman J. Myers;Killian Sullivan;Kyle Byassee;H. Song;A. Madanayake
中科院分区:
工程技术3区
文献类型:
--
作者:
Sirani M. Perera;Rodman J. Myers;Killian Sullivan;Kyle Byassee;H. Song;A. Madanayake

文献摘要

相似文献

无人机群的新兴概念创造了具有重大社会影响的新机会。然而,未来的无人机群应用和服务带来了新的通信和传感挑战,特别是对于协作任务。为了应对这些挑战,在本文中,我们将传感器阵列和通信相结合,提出了一个数学模型来路由一系列自主无人机系统(AUAS),即所谓的无人机群或AUAS群,这些系统没有通信基站,但使用多个时空数据进行相互通信。利用结构化矩阵理论、多波束赋形的概念和传感器阵列,提出了一种群路由算法。我们解决了路由算法的计算和算术复杂性、精确度和可靠性。该算法根据传感器阵列中的阵元个数和群组成员的波束形成输出来测量误码率,以验证和保证分布式AUAS组网的路由安全。拟议的模型有可能使未来的无人机群应用和服务成为可能。最后,讨论了基于机器学习的低代价无人机群路径选择算法的进一步研究工作。
The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm.