# Drone classification from RF fingerprints using deep residual nets

arXiv: https://arxiv.org/abs/2011.13663 · HTML: https://mlsanity.com/paper/2011.13663/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Deep Learning Radio Frequency Signal Classification with Hybrid Images](https://mlsanity.com/paper/2105.09063/) (2021)
- [Advance and Refinement: The Evolution of UAV Detection and Classification Technologies](https://mlsanity.com/paper/2409.05985/) (2024)
- [DronePose: The identification, segmentation, and orientation detection of drones via neural networks](https://mlsanity.com/paper/2112.05488/) (2021)
- [URANUS: Radio Frequency Tracking, Classification and Identification of Unmanned Aircraft Vehicles](https://mlsanity.com/paper/2207.06025/) (2022)
- [Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses](https://mlsanity.com/paper/2211.01131/) (2022)
- [Unauthorized Drone Detection: Experiments and Prototypes](https://mlsanity.com/paper/2212.01436/) (2022)
- [CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception](https://mlsanity.com/paper/2601.03302/) (2026)

## Abstract (arXiv)

Detecting UAVs is becoming more crucial for various industries such as airports and nuclear power plants for improving surveillance and security measures. Exploiting radio frequency (RF) based drone control and communication enables a passive way of drone detection for a wide range of environments and even without favourable line of sight (LOS) conditions. In this paper, we evaluate RF based drone classification performance of various state-of-the-art (SoA) models on a new realistic drone RF dataset. With the help of a newly proposed residual Convolutional Neural Network (CNN) model, we show that the drone RF frequency signatures can be used for effective classification. The robustness of the classifier is evaluated in a multipath environment considering varying Doppler frequencies that may be introduced from a flying drone. We also show that the model achieves better generalization capabilities under different wireless channel and drone speed scenarios. Furthermore, the newly proposed model's classification performance is evaluated on a simultaneous multi-drone scenario. The classifier achieves close to 99 % classification accuracy for signal-to-noise ratio (SNR) 0 dB and at -10 dB SNR it obtains 5 % better classification accuracy compared to the existing framework.