Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends
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Abstract (arXiv)
Unmanned aircraft systems (UAS), or unmanned aerial vehicles (UAVs), often referred to as drones, have been experiencing healthy growth in the United States and around the world. The positive uses of UAS have the potential to save lives, increase safety and efficiency, and enable more effective science and engineering research. However, UAS are subject to threats stemming from increasing reliance on computer and communication technologies, which place public safety, national security, and individual privacy at risk. To promote safe, secure and privacy-respecting UAS operations, there is an urgent need for innovative technologies for detecting, tracking, identifying and mitigating UAS. A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or unmanned aircraft system. Over the past 5 years, significant research efforts have been made to detect, and mitigate UAS: detection technologies are based on acoustic, vision, passive radio frequency, radar, and data fusion; and mitigation technologies include physical capture or jamming. In this paper, we provide a comprehensive survey of existing literature in the area of C-UAS, identify the challenges in countering unauthorized or unsafe UAS, and evaluate the trends of detection and mitigation for protecting against UAS-based threats. The objective of this survey paper is to present a systematic introduction of C-UAS technologies, thus fostering a research community committed to the safe integration of UAS into the airspace system.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Aug 2020.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 3; 2024: 1; 2025: 1; 2026: 3.
11 papers.
- 2021Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Surveycited by 9
- 2021Zero-bias Deep Neural Network for Quickest RF Signal Surveillancecited by 2
- 2023NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attackscited by 2
- 2025Drone Detection Using a Low-Power Neuromorphic Virtual Tripwirecited by 2
- 2021Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Spacecited by 1
- 2023GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configurationcited by 1
- 2023The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraftcited by 1
- 2024Multi-Stage Fusion Architecture for Small-Drone Localization and Identification Using Passive RF and EO Imagery: A Case Studycited by 0
- 2026Toward Maturity-Based Certification of Embodied AI: Quantifying Trustworthiness Through Measurement Mechanismscited by 0
- 2026Classification and detection of multiple UAVs using rational Gaussian wavelet neural networkscited by 0
- 2026AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learningcited by 0
- 2026AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning14 Aug 2026
- 2026Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks25 May 2026
- 2026Toward Maturity-Based Certification of Embodied AI: Quantifying Trustworthiness Through Measurement Mechanisms6 Jan 2026
- 2025Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire16 Sep 2025
- 2024Multi-Stage Fusion Architecture for Small-Drone Localization and Identification Using Passive RF and EO Imagery: A Case Study30 Mar 2024
- 2023The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft10 Nov 2023
- 2023GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration29 Jul 2023
- 2023NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks9 Mar 2023
- 2021Zero-bias Deep Neural Network for Quickest RF Signal Surveillance12 Oct 2021
- 2021Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space18 Aug 2021
- 2021Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey25 Jan 2021