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

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Název: Coordinated Radio Emitter Detection Process Using Group of Unmanned Aerial Vehicles.
Autoři: Mazuro, Maciej, Skokowski, Paweł, Kelner, Jan M.
Zdroj: Sensors (14248220); Dec2025, Vol. 25 Issue 23, p7298, 25p
Témata: MULTISENSOR data fusion, DEMPSTER-Shafer theory, COGNITIVE radio, SPECTRUM analysis, COOPERATIVE control systems, WIRELESS communications, DRONE aircraft, REAL-time computing
Abstrakt: Highlights: What are the main findings? Utilizing unmanned aerial vehicles (UAVs) in spectrum monitoring increases sensor range and freedom of movement compared to ground-based systems, and when deployed based on a coordinated group or swarm of UAVs allow simultaneous spectrum monitoring over a larger area, translating into increased range and coverage of the system. Implementation of data fusion algorithms enables real-time sensing and increases the reliability of multi-source data-based decision-making. What are the implications of the main findings? Groups of UAVs are becoming increasingly effective tools for spectrum monitoring and building situational awareness in the electromagnetic environment. The proposed system architecture enables scalability and adaptive solutions for electromagnetic spectrum monitoring in cognitive radio applications. The rapid expansion of wireless communications has led to increasing demand and interference in the electromagnetic spectrum, raising the question of how to achieve reliable and adaptive monitoring in complex and dynamic environments. This study aims to investigate whether groups of unmanned aerial vehicles (UAVs) can provide an effective alternative to conventional, static spectrum monitoring systems. We propose a cooperative monitoring system in which multiple UAVs, integrated with software-defined radios (SDRs), conduct energy measurements and share their observations with a data fusion center. The fusion process is based on Dempster–Shafer theory (DST), which models uncertainty and combines partial or conflicting data from spatially distributed sensors. A simulation environment developed in MATLAB emulates UAV mobility, communication delays, and propagation effects in various swarm formations and environmental conditions. The results confirm that cooperative spectrum monitoring using UAVs with DST data fusion improves detection robustness and reduces susceptibility to noise and interference compared to single-sensor approaches. Even under challenging propagation conditions, the system maintains reliable performance, and DST fusion provides decision-supporting results. The proposed methodology demonstrates that UAV groups can serve as scalable, adaptive tools for real-time spectrum monitoring and contributes to the development of intelligent monitoring architectures in cognitive radio networks. [ABSTRACT FROM AUTHOR]
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Abstrakt:Highlights: What are the main findings? Utilizing unmanned aerial vehicles (UAVs) in spectrum monitoring increases sensor range and freedom of movement compared to ground-based systems, and when deployed based on a coordinated group or swarm of UAVs allow simultaneous spectrum monitoring over a larger area, translating into increased range and coverage of the system. Implementation of data fusion algorithms enables real-time sensing and increases the reliability of multi-source data-based decision-making. What are the implications of the main findings? Groups of UAVs are becoming increasingly effective tools for spectrum monitoring and building situational awareness in the electromagnetic environment. The proposed system architecture enables scalability and adaptive solutions for electromagnetic spectrum monitoring in cognitive radio applications. The rapid expansion of wireless communications has led to increasing demand and interference in the electromagnetic spectrum, raising the question of how to achieve reliable and adaptive monitoring in complex and dynamic environments. This study aims to investigate whether groups of unmanned aerial vehicles (UAVs) can provide an effective alternative to conventional, static spectrum monitoring systems. We propose a cooperative monitoring system in which multiple UAVs, integrated with software-defined radios (SDRs), conduct energy measurements and share their observations with a data fusion center. The fusion process is based on Dempster–Shafer theory (DST), which models uncertainty and combines partial or conflicting data from spatially distributed sensors. A simulation environment developed in MATLAB emulates UAV mobility, communication delays, and propagation effects in various swarm formations and environmental conditions. The results confirm that cooperative spectrum monitoring using UAVs with DST data fusion improves detection robustness and reduces susceptibility to noise and interference compared to single-sensor approaches. Even under challenging propagation conditions, the system maintains reliable performance, and DST fusion provides decision-supporting results. The proposed methodology demonstrates that UAV groups can serve as scalable, adaptive tools for real-time spectrum monitoring and contributes to the development of intelligent monitoring architectures in cognitive radio networks. [ABSTRACT FROM AUTHOR]
ISSN:14248220
DOI:10.3390/s25237298