Wavelet transform analytics for RF-based UAV detection and identification system using machine learning

In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm cal...

Full description

Saved in:
Bibliographic Details
Published in:Pervasive and mobile computing 2022-06, Vol.82, p.101569, Article 101569
Main Authors: Medaiyese, Olusiji .O., Ezuma, Martins, Lauf, Adrian P., Guvenc, Ismail
Format: Article
Language:English
Subjects:
Interference
RF fingerprinting
Scalogram
Scattergram
SqueezeNet
UAVs
Wavelet transform
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. In RF signal fingerprinting research, the transient and steady state of the signals can be used to extract a unique signature from an RF signal. By exploiting the RF control signals from unmanned aerial vehicles (UAVs) for DDI, we considered each state of the signals separately for feature extraction and compared the pros and cons for drone detection and identification. Using various categories of wavelet transforms (discrete wavelet transform, continuous wavelet transform, and wavelet scattering transform) for extracting features from the signals, we built different models using these features. We studied the performance of these models under different signal-to-noise ratio (SNR) levels. By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR. •To detect the presence of UAVs in an environment using the RF signals from the UAV.•To compare RF fingerprints from the transient and steady state of an RF signal.•To utilize wavelet transform analytics for the feature extraction.•To evaluate the performance of trained models under varying signal-to-noise ratios.
ISSN:1574-1192
1873-1589
DOI:10.1016/j.pmcj.2022.101569