Version 1
: Received: 20 August 2024 / Approved: 21 August 2024 / Online: 21 August 2024 (09:31:51 CEST)
How to cite:
Yang, C.; Huang, C.; Zhao, Y. The Intellectual Structure and the Future of Counter‐UAS Research: A Bibliometric and Scoping Review. Preprints2024, 2024081552. https://doi.org/10.20944/preprints202408.1552.v1
Yang, C.; Huang, C.; Zhao, Y. The Intellectual Structure and the Future of Counter‐UAS Research: A Bibliometric and Scoping Review. Preprints 2024, 2024081552. https://doi.org/10.20944/preprints202408.1552.v1
Yang, C.; Huang, C.; Zhao, Y. The Intellectual Structure and the Future of Counter‐UAS Research: A Bibliometric and Scoping Review. Preprints2024, 2024081552. https://doi.org/10.20944/preprints202408.1552.v1
APA Style
Yang, C., Huang, C., & Zhao, Y. (2024). The Intellectual Structure and the Future of Counter‐UAS Research: A Bibliometric and Scoping Review. Preprints. https://doi.org/10.20944/preprints202408.1552.v1
Chicago/Turabian Style
Yang, C., Chenyu Huang and Yanhui Zhao. 2024 "The Intellectual Structure and the Future of Counter‐UAS Research: A Bibliometric and Scoping Review" Preprints. https://doi.org/10.20944/preprints202408.1552.v1
Abstract
With advancements in remote sensing technology and affordable design, uncrewed aerial systems (UAS), commonly known as drones, have become prevalent in both civil and military applications, such as agriculture, public safety, and aerial imaging. However, the rise in unlawful UAS activities, such as non-compliance with legal standards and potential terrorist attacks, has raised significant public concern, necessitating effective detection and mitigation solutions. Despite the growing importance of this issue, comprehensive and detailed examinations of existing counter-UAS solutions are lacking. To address this gap, this study conducts a bibliometric analysis and scoping review of the current literature to identify key topics and emerging trends in counter-UAS approaches. Utilizing co-word and social network analyses, the study identifies strong and weak connections between selected keywords from academic articles. This study summarizes the limitations and potential opportunities within counter-UAS research, suggesting an increasing focus on multisensory fusion and machine-learning approaches for drone detection and mitigation. Additionally, areas such as swarm drone operations, UAS traffic management (UTM), and UAS networks are identified as important but promising fields for further investigation. The findings of this study provide a foundation for enhancing air and ground safety through improved counter-UAS applications.
Keywords
aviation safety; Counter‐Uncrewed Aerial Systems (C‐UAS); machine learning
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
