Year: 2025
Type: article
Abstract: This paper presents a comprehensive study of the use of software-defined radio (SDR) technology as an effective instrument for detecting radiofrequency activity associated with unmanned aerial vehicles (UAVs). The research is focused not only on the general principles of SDR but also on the practical implementation of algorithms and methods aimed at enhancing the efficiency of UAV signal detection in complex and dynamic radio environments. The architecture of SDR systems was analyzed in detail, highlighting their flexibility, scalability, and adaptability compared to conventional radio monitoring tools. Functional aspects, including wideband signal acquisition, reconfigurable software modules, and multi-channel processing capabilities, were examined as key enablers for UAV detection tasks. Special emphasis was placed on the development and application of mathematical tools and algorithms. Fourier transform, wavelet analysis, and power spectral density estimation were applied to perform spectral analysis and identify characteristic frequency components of UAV transmissions. Furthermore, methods for modulation classification and anomaly detection were implemented to distinguish UAV-related signals from background noise and interference. Synchronization techniques and preamble detection were tested to increase the reliability of identification under low signal-to-noise conditions. Based on the conducted analysis, a detection algorithm was designed and optimized. The algorithm takes into account noise resistance, multi-channel processing, and adaptive real-time data analysis. Its performance demonstrates the capability to reliably identify UAV communication signals even in the presence of intentional jamming and rapidly changing electromagnetic conditions. The results of this work create a methodological foundation for the design of advanced radiofrequency monitoring systems intended for electronic warfare and counter-UAV applications. The proposed SDR-based approach not only improves detection accuracy but also offers adaptability to future challenges in the field of UAV threat mitigation.
Authors: O. Bilyk, O. Martynchuk, Yu. Zhuha
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4414912998