Software Defined Radio and Wireless Acoustic Networking for Amateur Drone Surveillance
Abstract
The advancement of the micro-electro-mechanical sensory industry and open source autopilot stacks, have dramatically reduced the cost and difficulty of making highly maneuverable UAVs. Such ease in flying drones has caused concerns about privacy, public safety, and security. One of the major threats is inadequate control of flying small drones over sensitive areas. In this research, we address this problem through a dual approach: detection and eviction. We propose a distributed system to identify the appearance and the approximate position of unwelcome drones using a wireless acoustic sensor network and machine learning algorithms. Next, we integrate software defined radio transceivers along with machine learning algorithm into a framework to specify and decode the UAV's telemetry protocols. Using decoded information, we can then send control commands to evict the trespassing drone away before sending aggressive surveillance UAVs.
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Counter-drone technology is widely under study, which for example enables configuration of guarded areas to either permit or block drones.
“To prevent those misuses as in [150-154], counter-drone technology is widely under study, which for example, enables the configuration of guarded areas to either permit or block drones.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [150]
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A review of video surveillance systems
Elharrouss, O., Almaadeed, N., & Al-Maadeed, S. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103116 · Cited by 283 · see citing works
A Survey of Video Surveillance Systems in Smart City
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Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
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- Authors
- Yue, X., Liu, Y., Wang, J., Song, H., & Cao, H.
- Affiliations
- South China Agricultural University (CN)
- South China University of Technology (CN)
- Embry–Riddle Aeronautical University (US)
- Sun Yat-sen University (CN)
- Venue
- IEEE Communications Magazine
- Published
- 2018-04
- Volume
- 56
- Issue
- 4
- Pages
- 90-97
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2798636681
- Cited by
- 111
- References
- 15
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 5 · 2023: 9 · 2022: 26 · 2021: 27 · 2020: 14 · 2019: 9 · 2018: 7
- FWCI
- 11.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Magazine (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Engineering › Aerospace Engineering › Military Technology and Strategies
- Keywords
- software-defined radio, wireless acoustic sensor networks, drone surveillance, unmanned aerial vehicles, drone detection, privacy and security, distributed detection, machine learning
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