This paper presents measurements of Radar Cross Section (RCS) of five Unmanned Aerial Vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semi-controlled environment as a function of azimuth aspect angle, polarization and frequency in the range 8.2-18 GHz. The experimental setup and the data pre-processing, which include coherent background subtraction and range gating procedures, are illustrated in detail. Furthermore, a thorough description of the calibration process, which is based on the substitution method, is discussed. Then, a first-order statistical analysis of the measured RCSs is provided by means of the Cramér-von Mises (CVM) distance and the Kolmogorov-Smirnov (KS) test. Finally, radar detection performance is assessed on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for non-fluctuating and Rayleigh fluctuating targets.

Radar Detection Performance Prediction using Measured UAVs RCS Data / Rosamilia, M.; Balleri, A.; De Maio, A.; Aubry, A.; Carotenuto, V.. - In: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. - ISSN 0018-9251. - (2022), pp. 1-16. [10.1109/TAES.2022.3227224]

Radar Detection Performance Prediction using Measured UAVs RCS Data

Rosamilia M.;De Maio A.
;
Aubry A.;Carotenuto V.
2022

Abstract

This paper presents measurements of Radar Cross Section (RCS) of five Unmanned Aerial Vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semi-controlled environment as a function of azimuth aspect angle, polarization and frequency in the range 8.2-18 GHz. The experimental setup and the data pre-processing, which include coherent background subtraction and range gating procedures, are illustrated in detail. Furthermore, a thorough description of the calibration process, which is based on the substitution method, is discussed. Then, a first-order statistical analysis of the measured RCSs is provided by means of the Cramér-von Mises (CVM) distance and the Kolmogorov-Smirnov (KS) test. Finally, radar detection performance is assessed on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for non-fluctuating and Rayleigh fluctuating targets.
2022
Radar Detection Performance Prediction using Measured UAVs RCS Data / Rosamilia, M.; Balleri, A.; De Maio, A.; Aubry, A.; Carotenuto, V.. - In: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. - ISSN 0018-9251. - (2022), pp. 1-16. [10.1109/TAES.2022.3227224]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/923288
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