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

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
Number of pages2
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

Publication series

Series2020 International Symposium on Antennas and Propagation, ISAP 2020

Conference

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

Keywords

  • Detection
  • Propagation
  • Radar
  • RCS
  • UAV

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