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 contribution

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 (US)
Title of host publication2020 International Symposium on Antennas and Propagation, ISAP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages223-224
Number of pages2
ISBN (Electronic)9784885523267
DOIs
StatePublished - Jan 25 2021
Event2020 International Symposium on Antennas and Propagation, ISAP 2020 - Virtual, Osaka, Japan
Duration: Jan 25 2021Jan 28 2021

Publication series

Name2020 International Symposium on Antennas and Propagation, ISAP 2020

Conference

Conference2020 International Symposium on Antennas and Propagation, ISAP 2020
CountryJapan
CityVirtual, Osaka
Period1/25/211/28/21

Keywords

  • Detection
  • Propagation
  • Radar
  • RCS
  • UAV

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Instrumentation

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