Low-Altitude UAV Surveillance System via Highly Sensitive Distributed Acoustic Sensing
In recent years, the unmanned aerial vehicle (UAV) has been widely applied in daily life, but the “black flight” incidents happen frequently and even endanger public safety. Although the electrical microphone can cover the low-altitude and non-line-of-sight (NLOS) limited vision areas of radar or camera system, it is quite difficult to achieve large-scale networking and surveillance. In this article, a low-altitude UAV detection and positioning scheme based on fiber-optic distributed acoustic sensing (DAS) is proposed and demonstrated, which has the superiorities of large-scale networking and antielectromagnetic interference in applications. The DAS system assisted with the highly sensitive sensing cable fabricated by uniformly winding backscattering enhanced optical fiber (BEOF) around sensitization cable can collect the acoustic signals induced by low-altitude UAV, and it can be easily deployed with a large scale. The acoustic signals collected by DAS are further divided into continuous frames and denoised for low-altitude UAV positioning and tracking, in which the time difference of arrival (TDOA) algorithm based on the generalized cross-correlation (GCC) method and least-squares estimation (LSE) method is employed. In the field test, the low-altitude UAV positioning and tracking are both demonstrated and achieved with an ultralow position estimation error of less than 0.5 m in low-altitude airspace. It has exhibited that the proposed surveillance scheme has a great potential for low-altitude airspace early warning in large-scale applications such as border and airport. Then, reporting and monitoring intrusive low-altitude UAV instantaneously allow the controller to take quick action to prevent potential danger and damage from happening in key areas.
Authors
- Zhijun Yan (ORCID: https://orcid.org/0000-0002-3506-7446)
- Xiangpeng Xiao (ORCID: https://orcid.org/0000-0002-1407-9149)
- Perry Ping Shum (ORCID: https://orcid.org/0000-0003-1391-4736)
- Hao Li (ORCID: https://orcid.org/0000-0002-8103-5904)
- Junfeng Chen (ORCID: https://orcid.org/0009-0009-3779-0572)
- Qizhen Sun (ORCID: https://orcid.org/0000-0002-2410-6470)
- Deming Liu (ORCID: https://orcid.org/0000-0001-5591-2929)
- Zhengxuan Shi (ORCID: https://orcid.org/0000-0003-3825-6290)
- Ke Ai (ORCID: https://orcid.org/0009-0000-8430-2089)
Institutions
- Jinyintan Hospital (CN)
- Southern University of Science and Technology (CN)
- Wuhan National Laboratory for Optoelectronics (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2024-09-05
- DOI
- https://doi.org/10.1109/jsen.2024.3452072
- Citations
- 13
- Primary Topic
- Fire Detection and Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 4.74
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- National Key Research and Development Program of China