# Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques

arXiv: https://arxiv.org/abs/1901.07703 · HTML: https://mlsanity.com/paper/1901.07703/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [DronePose: The identification, segmentation, and orientation detection of drones via neural networks](https://mlsanity.com/paper/2112.05488/) (2021)
- [Unauthorized Drone Detection: Experiments and Prototypes](https://mlsanity.com/paper/2212.01436/) (2022)
- [CrossRF: A Domain-Invariant Deep Learning Approach for RF Fingerprinting](https://mlsanity.com/paper/2505.18200/) (2025)
- [A Generic Machine Learning Framework for Radio Frequency Fingerprinting](https://mlsanity.com/paper/2510.09775/) (2025)
- [HSCP: A Two-Stage Spectral Clustering Framework for Resource-Constrained UAV Identification](https://mlsanity.com/paper/2512.08983/) (2025)

## Abstract (arXiv)

This paper focuses on the detection and classification of micro-unmanned aerial vehicles (UAVs) using radio frequency (RF) fingerprints of the signals transmitted from the controller to the micro-UAV. In the detection phase, raw signals are split into frames and transformed into the wavelet domain. A Markov models-based naive Bayes approach is used to check for the presence of a UAV in each frame. In the classification phase, unlike the traditional approaches that rely solely on time-domain signals and corresponding features, the proposed technique uses the energy transient signal. This approach is more robust to noise and can cope with different modulation techniques. First, the normalized energy trajectory is generated from the energy-time-frequency distribution of the raw control signal. Next, the start and end points of the energy transient are detected by searching for the most abrupt changes in the mean of the energy trajectory. Then, a set of statistical features is extracted from the energy transient. Significant features are selected by performing neighborhood component analysis (NCA) to keep the computational cost of the algorithm low. Finally, selected features are fed to several machine learning algorithms for classification. The algorithms are evaluated experimentally using a database containing 100 RF signals from each of 14 different UAV controllers. The signals are recorded wirelessly using a high-frequency oscilloscope. The data set is randomly partitioned into training and test sets for validation with the ratio 4:1. Ten Monte Carlo simulations are run and results are averaged to assess the performance of the methods. All the micro-UAVs are detected correctly and an average accuracy of 96.3% is achieved using the k-nearest neighbor (kNN) classification. Proposed methods are also tested for different signal-to-noise ratio (SNR) levels and results are reported.