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Intelligent Counter-UAV Threat Detection Using Hierarchical Fuzzy Decision-Making and Sensor Fusion.

Fani Arapoglou1, Paraskevi Zacharia1, Michail Papoutsidakis1

  • 1Department of Industrial Design and Production Engineering, University of West Attica, Egaleo, 12241 Athens, Greece.

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Summary
This summary is machine-generated.

This study introduces an intelligent framework for Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, enhancing threat detection by fusing sensor data. The hierarchical fuzzy logic approach optimizes sensor selection for improved reliability and cost-effectiveness.

Keywords:
Counter-UAV systemsUAV threat detectiondecision-makinghierarchical fuzzy logicsensor fusionsurveillance systems

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Defense Technology

Background:

  • Increasing complexity and ambiguity of modern Unmanned Aerial Vehicle (UAV) threats necessitate advanced detection and identification methods.
  • Existing Counter-UAV systems face challenges in effectively fusing heterogeneous sensor data and optimizing sensor deployment.
  • Need for intelligent decision-making frameworks that can adapt to dynamic threat environments and sensor uncertainties.

Purpose of the Study:

  • To propose an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-UAV systems.
  • To develop a novel three-stage fuzzy inference architecture for adaptive sensor evaluation and optimal pairing.
  • To enhance the reliability and cost-effectiveness of sensor deployment in Counter-UAV operations.

Main Methods:

  • A three-layered Fuzzy Inference System (FIS) architecture was developed: FIS-A for sensor effectiveness (altitude, detection probability), FIS-B for operational suitability (range, cost), and FIS-C for composite sensor pair suitability.
  • Fusion of heterogeneous sensor data including Electro-Optical/Infrared (EO/IR), Radar, Acoustic, and Radio Frequency (RF) modalities.
  • Simulation-based evaluation using empirical data and literature to validate the framework's performance.

Main Results:

  • The proposed framework effectively handles uncertainty in sensor data and threat identification.
  • Demonstrated enhancement in detection reliability and accuracy within Counter-UAV scenarios.
  • The hierarchical structure facilitates detailed analysis, system-level optimization, and cost-effective sensor deployment.

Conclusions:

  • The intelligent hierarchical fuzzy decision-making framework offers significant advancements for Counter-UAV systems.
  • The modular, scalable, and interpretable methodology is transferable to other dynamic threat environments.
  • The framework supports adaptive sensor evaluation and optimal pairing, improving overall system performance and decision-making.