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UAV Detection Using Reinforcement Learning.

Arwa AlKhonaini1,2, Tarek Sheltami1, Ashraf Mahmoud1

  • 1Computer Engineering Department, Interdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

Sensors (Basel, Switzerland)
|March 28, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel method for detecting and identifying Unmanned Aerial Vehicles (UAVs) using radio frequency (RF) signals and hierarchical reinforcement learning. The approach achieves a high 99.7% detection accuracy, enhancing UAV security.

Keywords:
REINFORCEUnmanned Aerial Vehiclesdetection and identificationhierarchical reinforcement learningradio frequency

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Signal Processing

Background:

  • Unmanned Aerial Vehicles (UAVs) are increasingly used in military and civilian sectors.
  • The rise in UAVs necessitates robust methods for detecting unauthorized aerial vehicles.
  • Existing detection methods (RF, visual, acoustic) have limitations.

Purpose of the Study:

  • To propose and evaluate a novel approach for detecting and identifying intruding UAVs.
  • To leverage radio frequency (RF) signals for UAV detection.
  • To advance the application of hierarchical reinforcement learning in UAV security.

Main Methods:

  • Utilized a hierarchical reinforcement learning technique to train a UAV agent.
  • Employed the REINFORCE algorithm with entropy regularization for improved accuracy.
  • Focused on extracting and analyzing features from RF signals for detection and identification.

    • Main Results:

    • Achieved a remarkable detection accuracy of 99.7% for intruding UAVs.
    • Demonstrated improved cumulative return performance and reduced loss in the learning agent.
    • Validated the effectiveness of RF signal analysis combined with hierarchical reinforcement learning.

    Conclusions:

    • The proposed RF-based hierarchical reinforcement learning approach is highly effective for UAV detection and identification.
    • This method significantly enhances UAV security and surveillance capabilities.
    • The study contributes a novel and less-explored approach to UAV detection within reinforcement learning.