Drone detection and classification based on radar cross section signatures

Vasilii Semkin, Mingsheng Yin, Yaqi Hu, Marco Mezzavilla, Sundeep Rangan

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    28 Citations (Scopus)

    Abstract

    In this work, we show how drone detection and classification can be enabled by leveraging a database of radar cross section (RCS) signatures. First, we present a set of measurement results of the RCS of a carbon fiber drone model at 28 GHz. The measurements were performed in an anechoic chamber and provide essential information about the RCS signature of the specific drone. Then, we assess the RCS-based detection probability and the range error by running simulations in urban environments. The drones were positioned at different distances, from 30m to 90m, and the RCS signatures used for the detection and classification were obtained experimentally.

    Original languageEnglish
    Title of host publication2020 International Symposium on Antennas and Propagation, ISAP 2020
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages223-224
    ISBN (Electronic)978-4-88552-326-7
    ISBN (Print)978-1-7281-5909-6
    DOIs
    Publication statusPublished - 25 Jan 2021
    MoE publication typeA4 Article in a conference publication
    Event2020 International Symposium on Antennas and Propagation, ISAP 2020: Online - Virtual, Osaka, Japan
    Duration: 25 Jan 202128 Jan 2021

    Conference

    Conference2020 International Symposium on Antennas and Propagation, ISAP 2020
    Country/TerritoryJapan
    CityOsaka
    Period25/01/2128/01/21

    Keywords

    • Detection
    • Propagation
    • Radar
    • RCS
    • UAV

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