Advance and Refinement: The Evolution of UAV Detection and Classification Technologies
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Abstract (arXiv)
This review provides a detailed analysis of the advancements in unmanned aerial vehicle (UAV) detection and classification systems from 2020 to today. It covers various detection methodologies such as radar, radio frequency, optical, and acoustic sensors, and emphasizes their integration via sophisticated sensor fusion techniques. The fundamental technologies driving UAV detection and classification are thoroughly examined, with a focus on their accuracy and range. Additionally, the paper discusses the latest innovations in artificial intelligence and machine learning, illustrating their impact on improving the accuracy and efficiency of these systems. The review concludes by predicting further technological developments in UAV detection, which are expected to enhance both performance and reliability.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 74 references that are in the map. First posted on arXiv 9 Sep 2024.
Builds on
8 of 74 references.
References · 8
- 2020Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensorscited by 21
- 2021A dataset for multi-sensor drone detectioncited by 15
- 2020Drone classification from RF fingerprints using deep residual netscited by 7
- 2020Machine Learning Framework for RF-Based Drone Detection and Identification Systemcited by 6
- 2021Drone Detection Using Convolutional Neural Networkscited by 3
- 2021Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequenciescited by 2
- 2021Comparative Analysis of Radar Cross Section Based UAV Classification Techniquescited by 2
- 2024Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approachcited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.