# Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends

arXiv: https://arxiv.org/abs/2008.12461 · HTML: https://mlsanity.com/paper/2008.12461/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [Zero-bias Deep Neural Network for Quickest RF Signal Surveillance](https://mlsanity.com/paper/2110.05797/) (2021)
- [NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks](https://mlsanity.com/paper/2303.06151/) (2023)
- [Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire](https://mlsanity.com/paper/2509.12997/) (2025)
- [Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space](https://mlsanity.com/paper/2108.08244/) (2021)
- [GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration](https://mlsanity.com/paper/2307.15876/) (2023)
- [The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft](https://mlsanity.com/paper/2311.06368/) (2023)
- [Multi-Stage Fusion Architecture for Small-Drone Localization and Identification Using Passive RF and EO Imagery: A Case Study](https://mlsanity.com/paper/2406.16875/) (2024)
- [Toward Maturity-Based Certification of Embodied AI: Quantifying Trustworthiness Through Measurement Mechanisms](https://mlsanity.com/paper/2601.03470/) (2026)
- [Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks](https://mlsanity.com/paper/2605.26310/) (2026)
- [AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning](https://mlsanity.com/paper/2608.14135/) (2026)

## 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.