V.V. Oguenko1, T.R. Aukhadeev2

1,2 Kazan Federal University (Kazan, Russia)

1 oguenkovv@mail.ru; 2 TRAuhadeev@kpfu.ru

Problem Statement. With the rapidly growing use of unmanned aerial vehicles (UAVs) in various fields, including military and civilian, the task of reliably detecting and classifying UAVs is becoming especially relevant. Radar technologies, capable of operating effectively in all weather conditions and over significant distances, are becoming the primary detection tool. The nature of its payload is also crucial. There is currently an urgent need to develop and implement new, highly sensitive approaches based on the analysis of unique target characteristics.

Objective. To conduct a comprehensive study, including modeling and analysis of micro-Doppler signatures (MDS), aimed at developing and verifying methods for identifying UAV types and determining the nature of their payload.

Results. The article presents the results of numerical and experimental analysis, compares spectrograms for various combinations of drone type and payload design, and provides quantitative assessments of the effectiveness of classifying objects based on their MDS. The obtained results confirm the feasibility of drone type identification and payload characteristic detection based on unique micro-Doppler signatures, which significantly improves the reliability of modern critical facility security systems.

Practical Relevance. The results of the study can be used in security systems for automatic recognition of UAV type and payload characteristics, for upgrading radars with intelligent classification capabilities for small targets, and in designing new signal processing algorithms for electronic surveillance systems.

Keywords: micro-Doppler signatures; UAVs; radar; target classification; spectral analysis; payload; digital models; security systems; drone detection; ENGEE modeling.

This work was supported by a grant allocated to Kazan Federal University for the fulfillment of a state assignment in the field of scientific activity, project No. FZSM-2024-0004.

Oguenko V.V., Aukhadeev T.R. Using micro-Doppler (radar) signatures to identify UAV type and payload characteristics // Radiotekhnika. 2026. V. 90. № 6. P. 68−75. DOI: https://doi.org/10.18127/j00338486-202606-07

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