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Radio Frequency-based Drone Detection and Classification Using Discrete Fourier Transform and LightGBM

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(10), pp.59-68
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 26, 2024
  • Accepted : September 25, 2024
  • Published : October 31, 2024

Ki-Hyeon Sung 1 Soojin Lee 1

1국방대학교

Accredited

ABSTRACT

In this study, we proposed an efficient model that can detect and classify the drones and related devices based on radio frequency signals. In order to increase the applicability in the battlefield, proposed model was designed to be lightweight, to ensure rapid detection and high detection accuracy. Data preprocessing was performed by applying a Discrete Fourier Transform (DFT) that is faster than Hilbert-Huang Transform (HHT). We adopted the LightGBM model as the learning model, which can be easily used by non-professionals and guarantees excellent performance in terms of classification speed and accuracy. CardRF dataset was used to verify the performance of the proposed model. As a result of the experiment, the accuracy of 3 classes classification for detecting and classifying drones, WiFi, and Bluetooth device was 99.63% when the number of sample points was set to 100k and 99.40% when set to 500k during the data preprocessing with DFT. And, in the 10 classes classification for 6 drones, 2 Bluetooth devices, and 2 WiFi devices, the accuracy was 95.65% for 100k and 96.83% for 500k, confirming significantly improved detection performance compared to previous studies.

Citation status

* References for papers published after 2023 are currently being built.