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

Vasilii Semkin (Corresponding Author), Jaakko Haarla, Thomas Pairon, Christopher Slezak, Sundeep Rangan, Ville Viikari, Claude Oestges

    Research output: Contribution to journalArticleScientificpeer-review

    45 Citations (Scopus)

    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 languageEnglish
    Article number9032332
    Pages (from-to)48958-48969
    JournalIEEE Access
    Volume8
    DOIs
    Publication statusPublished - 11 Mar 2020
    MoE publication typeA1 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.

    Keywords

    • Drone detection
    • millimeter-wave
    • radar cross section
    • unmanned aerial vehicle

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