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Unsupervised Drones Swarm Characterization Using RF Signals Analysis and Machine Learning Methods.

Nerya Ashush¹, Shlomo Greenberg^1,2, Erez Manor^1,2

¹School of Electrical and Computer Engineering, Ben Gurion University, Beer-Sheva 84105, Israel.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This study introduces an unsupervised machine learning approach to detect and characterize drone swarms using radio frequency (RF) signal analysis. The method achieves high accuracy, offering a novel solution for identifying malicious drone swarm activities.

Keywords:

dimension reduction drones swarm machine learning radio frequency unsupervised clustering wavelet transform

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Area of Science:

Electrical Engineering
Computer Science
Artificial Intelligence

Background:

Autonomous unmanned aerial vehicles (UAVs), or drones, offer significant benefits across various sectors.
Malicious use of drone technology, particularly drone swarms, poses security risks.
Existing detection methods often focus on single drones and utilize supervised learning.

Purpose of the Study:

To develop an unsupervised approach for characterizing and detecting drone swarms.
To leverage radio frequency (RF) signal analysis for distinguishing drone swarms.
To enhance security against the malicious use of autonomous drone systems.

Main Methods:

Utilizing unique RF signatures from drone transmitters.
Applying frequency transforms (continuous, discrete, wavelet scattering) for feature extraction.
Employing unsupervised dimensionality reduction (PCA, ICA, UMAP, t-SNE) and clustering algorithms (K-means, Mean Shift, X-means).

Main Results:

The proposed unsupervised method successfully characterizes drone swarms.
Achieved approximately 95% classification accuracy under varying levels of additive Gaussian white noise.
Demonstrated effectiveness on both self-built and common drone swarm datasets.

Conclusions:

Unsupervised RF signal analysis provides an effective method for drone swarm detection and characterization.
The approach offers a robust solution for identifying complex drone swarm activities.
This research contributes a novel, unsupervised technique to the field of drone security.