RF-Based UAV Detection and Identification Using Hierarchical Learning Approach is a paper by Ibrahim Nemer, Tarek Sheltami, Ifran Ahmad, Ansar El-Haque Yasar, and Mohammad A. R. Abdeen.

This research paper introduces an innovative machine learning-based method for efficiently identifying and detecting Unmanned Aerial Vehicles (UAVs). The proposed approach enhances UAV identification and detection by employing ensemble learning based on a hierarchical concept, accompanied by pre-processing and feature extraction stages for Radio Frequency (RF) data. A filtering technique is applied to the RF signals during the detection process to enhance the output. The approach consists of four classifiers that operate hierarchically.

In this approach, a sample undergoes the first classifier to determine the presence of a UAV. Subsequently, the second classifier is employed to identify the type of the detected UAV. Finally, the last two classifiers are responsible for determining the flight mode of the sample, specifically for Bebop and AR UAVs. Through evaluation using a publicly available dataset, the proposed approach demonstrates superior efficiency compared to existing detection systems in the literature. It can determine whether a UAV is currently flying within a given area, accurately identifying the UAV type and subsequently identifying the flight mode of the detected UAV with an accuracy of approximately 99%.

Publication Date: March 2021

RF-Based UAV Detection and Identification Using Hierarchical Learning Approach contains the following major sections:

  • Introduction
  • Related Works
  • Methodology
  • Proposed Detection Approach
  • Results and Discussions
  • Comparison with Other Approaches

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Authors: Ibrahim Nemer, Tarek Sheltami, Ifran Ahmad, Ansar El-Haque Yasar, and Mohammad A. R. Abdeen

Post Image- Average spectra of the RF activities for 2, 4, and 10 classes scenarios (Image Credit: RF-Based UAV Detection and Identification Using Hierarchical Learning Approach authors)

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