Experimental analysis of micro-Doppler characteristics of drones and birds for classification purposes

Bryan Tsang, Ram M. Narayanan, Ramesh Bharadwaj

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates the use of micro-Doppler signatures of drones and birds for their detection and classification. Assessments made from simulated results are verified by data collected using a 10-GHz continuous wave (CW) radar system. Time/Velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds are used for target identification and movement classification within TensorFlow. Results using Support Vector Machine (SVM) indicate 96% accuracy for drones vs. birds and 85% accuracy among individual drone and bird distinction between 5 classes.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXVI
EditorsKenneth I. Ranney, Ann M. Raynal
PublisherSPIE
ISBN (Electronic)9781510650923
DOIs
StatePublished - 2022
EventRadar Sensor Technology XXVI 2022 - Virtual, Online
Duration: Jun 6 2022Jun 12 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12108
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceRadar Sensor Technology XXVI 2022
CityVirtual, Online
Period6/6/226/12/22

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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