# Advance and Refinement: The Evolution of UAV Detection and Classification Technologies

arXiv: https://arxiv.org/abs/2409.05985 · HTML: https://mlsanity.com/paper/2409.05985/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 74 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors](https://mlsanity.com/paper/2007.07396/) (2020)
- [A dataset for multi-sensor drone detection](https://mlsanity.com/paper/2111.01888/) (2021)
- [Drone classification from RF fingerprints using deep residual nets](https://mlsanity.com/paper/2011.13663/) (2020)
- [Machine Learning Framework for RF-Based Drone Detection and Identification System](https://mlsanity.com/paper/2003.02656/) (2020)
- [Drone Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2107.01435/) (2021)
- [Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies](https://mlsanity.com/paper/2102.11954/) (2021)
- [Comparative Analysis of Radar Cross Section Based UAV Classification Techniques](https://mlsanity.com/paper/2112.09774/) (2021)
- [Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach](https://mlsanity.com/paper/2402.17987/) (2024)

## Built on by (most cited first, 2 of 2)

- [15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning](https://mlsanity.com/paper/2506.11049/) (2025)
- [The Unbearable Weight: Scaling Models and Methods for UAV Audio Classification](https://mlsanity.com/paper/2609.17884/) (2026)

## 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.