Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies

Vasilii Semkin; Jaakko Haarla; Thomas Pairon; Christopher Slezak; Sundeep Rangan; Ville Viikari; Claude Oestges

doi:10.1109/ACCESS.2020.2979339

Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies

Vasilii Semkin^*, Jaakko Haarla, Thomas Pairon, Christopher Slezak, Sundeep Rangan, Ville Viikari, Claude Oestges

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

83 Citations (Scopus)

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Abstract

In this work, we present quasi-monostatic Radar Cross Section measurements of different Unmanned Aerial Vehicles at 26-40 GHz. We study the Radar Cross Section signatures of nine different multi-rotor platforms as well as a single Lithium-ion Polymer battery. These results are useful in the design and testing of radar systems which employ millimeter-wave frequencies for superior drone detection. The data shows how radio waves are scattered by drones of various sizes and what impact the primary construction material has on the received Radar Cross Section signatures. Matching our intuition, the measurements confirm that larger drones made of carbon fiber are easier to detect, whereas drones made from plastic and styrofoam materials are less visible to the radar systems. The measurement results are published as an open database, creating an invaluable reference for engineers working on drone detection.

Original language	English
Article number	9032332
Pages (from-to)	48958-48969
Number of pages	12
Journal	IEEE Access
Volume	8
DOIs	https://doi.org/10.1109/ACCESS.2020.2979339
Publication status	Published - 1 Jan 2020
MoE publication type	A1 Journal article-refereed

Funding

This work was supported in part by the MACHAON Project funded by the Belgian Science Foundation FRS-FNRS (Fonds de la Recherche Scientifique), in part by the MUSEWINET Project through the Belgian Science Foundation EOS Program, and in part by the Framework of COST Action CA15104 IRACON. The work of Christopher Slezak and Sundeep Rangan was supported in part by NSF under Grant 1302336, Grant 1564142, Grant 1547332, and Grant 1824434, in part by NIST, in part by SRC, and in part by the industrial affiliates of NYU WIRELESS. The authors are particularly grateful to Viktor Nässi from the Department of Communications and Networking, Aalto University, assistant Professor Themistoklis Charalambous from the Department of Electrical Engineering and Automation, Aalto University, and Antti Lipasti from the ‘‘Vertical Hobby’’ for providing drone models for the measurements. The authors acknowledge the fruitful discussions in COST Action CA15104 IRACON. V. Semkin would like to acknowledge the support from Nokia Foundation, Jorma Ollila grant.

Keywords

Drone detection
millimeter-wave
radar cross section
unmanned aerial vehicle

Access to Document

10.1109/ACCESS.2020.2979339Licence: CC BY

Semkin_Analyzing_Radar_IEEEAccessFinal published version, 4.21 MBLicence: CC BY

Cite this

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Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies

Abstract

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Drone RCS measurements (26-40 GHz)

Drones could still be a threat to public safety -- New research improves drone detection

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Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Datasets

Drone RCS measurements (26-40 GHz)

Press/Media

Drones could still be a threat to public safety -- New research improves drone detection

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