Recognition of Fixed Wing Small Unmanned Aerial Vehicles (SUAVs) from Multirotor Using Statistical Features Derived from Complex Natural Resonances

Farajollahi, Mohammad Mahdi; Babaee, Mohammad Javad; Zarei, Nadali; Feraidooni, Mohammad Mahdi; Azari, Mahmoud

doi:10.22034/joeds.2026.554351.1103

Recognition of Fixed Wing Small Unmanned Aerial Vehicles (SUAVs) from Multirotor Using Statistical Features Derived from Complex Natural Resonances

Document Type : Original Article

Authors

Mohammad Mahdi Farajollahi ¹

Mohammad Javad Babaee ²

Nadali Zarei ³

Mohammad Mahdi Feraidooni ⁴

Mahmoud Azari ²

¹ Electronic Warfare group, Faculty of Electricity, Electronic and Cyber Warfare, Imam Hossein University, Tehran Iran

² Electronic Warfare Group, Faculty of Electricity, Electronic and Cyber Warfare, Imam Hossein University, Tehran, Iran

³ Electronic Warfare group, Faculty of Electricity and Cyber Warfare,Imam Hossein University, Tehran, Iran

⁴ Department of Telecommunications, Faculty of Electrical Engineering, Shahid Beheshti University, G. C., Tehran, Iran

10.22034/joeds.2026.554351.1103

Abstract

The widespread use of SUAVs in modern battlefields poses a serious threat to military forces and equipment and highlighting the need to develop recognition methods. Traditional techniques face major limitations due to their dependence on parameters such as aspect angle, range and polarization, as well as the inherent characteristics of SUAVs, including low altitude flight, low speed, high maneuverability, and small RCS. this study, by using the Singularity Expansion Method, the system identification approach and employing adaptive and fixed RMSE criteria extracts the statistical features of the targets and introduces some multidimensional feature spaces constructed from the zeros and poles of the target’s transfer function in the resonanc. For evaluation, 3D models of a fixed wing SUAV (Skylark) and a multirotor (Matrice 400) were simulated over the 120–600 MHz frequency range and under various SNR levels. The results indicate that the complex natural resonances depending on the target’s geometry, dimensions, and material provide a reliable basis for recognizing SUAVs. Increasing the SNR from 13 to 19 dB improved the adaptive RMSE by 24%, while the fixed mode, despite its lower accuracy, reduced processing time by 99%.

Keywords

Fixed-Wing SUAV

Multi-Rotor SUAV

Complex Natural Resonances

Statistical Features

System Identification Method