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Coordinated Radio Emitter Detection Process Using Group of Unmanned Aerial Vehicles.

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Summary
This summary is machine-generated.

Groups of unmanned aerial vehicles (UAVs) offer a robust solution for real-time electromagnetic spectrum monitoring. Cooperative UAV systems with Dempster-Shafer theory (DST) data fusion enhance detection accuracy in dynamic environments.

Keywords:
data fusiondrone swarmsensor networkspectrum monitoringunmanned aerial vehicle

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Aerospace Engineering

Background:

  • Increasing demand and interference in the electromagnetic spectrum necessitate advanced monitoring solutions.
  • Conventional static spectrum monitoring systems struggle in complex, dynamic environments.
  • Unmanned Aerial Vehicles (UAVs) present a potential platform for adaptive spectrum sensing.

Purpose of the Study:

  • To investigate the efficacy of cooperative UAV groups for real-time spectrum monitoring.
  • To evaluate a data fusion approach using Dempster-Shafer theory (DST) for integrating UAV sensor data.
  • To assess the system's performance under various environmental and mobility conditions.

Main Methods:

  • Development of a cooperative monitoring system with multiple UAVs equipped with Software-Defined Radios (SDRs).
  • Implementation of Dempster-Shafer theory (DST) for data fusion at a central data fusion center.
  • Simulation of UAV mobility, communication delays, and propagation effects in MATLAB.

Main Results:

  • Cooperative UAV spectrum monitoring with DST data fusion significantly improves detection robustness.
  • The system demonstrates reduced susceptibility to noise and interference compared to single-sensor methods.
  • Reliable performance is maintained even under challenging propagation conditions, with DST fusion providing decision support.

Conclusions:

  • Groups of UAVs are scalable and adaptive tools for real-time spectrum monitoring.
  • The proposed methodology enhances intelligent monitoring architectures for cognitive radio networks.
  • DST-based data fusion effectively handles uncertainty and conflicting data from distributed UAV sensors.