Drone Detection and Classification Using Physical-Layer Protocol Statistical Fingerprint is a paper by Louis Morge-Rollet, Denis Le Jeune, Frédéric Le Roy, Charles Canaff, and Roland Gautier.

In this paper, the authors propose a novel approach for drone detection and classification based on RF communication link analysis. Our approach analyses large signal records including several packets and can be decomposed into two successive steps: signal detection and drone classification. On the one hand, the signal detection step is based on Power Spectral Entropy (PSE), a measure of the energy distribution uniformity in the frequency domain. It consists of detecting a structured signal, such as a communication signal with a lower PSE than a noise one. On the other hand, the classification step is based on a so-called physical-layer protocol statistical fingerprint (PLSPF). This method extracts the packets at the physical layer using hysteresis thresholding, then computes statistical features for classification based on extracted packets.

Publication Date: September 2022

Drone Detection and Classification Using Physical-Layer Protocol Statistical Fingerprint contains the following major sections:

  • Introduction
  • State of the Art
  • Methodology
  • Experimentations
  • Discussions and Perspectives
  • Conclusions

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Authors: Louis Morge-Rollet, Denis Le Jeune, Frédéric Le Roy, Charles Canaff, and Roland Gautier

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