Detection of LSSUAV using hash fingerprint based SVDD

Zhiyuan Shi, Minmin Huang, Caidan Zhao, Lianfen Huang, Xiaojiang Du, Yifeng Zhao

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

25 Scopus citations

Abstract

With the rapid development of science and technology, unmanned aerial vehicles (UAVs) gradually become the worldwide focus of science and technology. Not only the development and application but also the security of UAV is of great significance to modern society. Different from methods using radar, optical or acoustic sensors to detect UAV, this paper proposes a novel distance-based support vector data description (SVDD) algorithm using hash fingerprint as feature. This algorithm does not need large number of training samples and its computation complexity is low. Hash fingerprint is generated by extracting features of signal preamble waveforms. Distance-based SVDD algorithm is employed to efficiently detect and recognize low, slow, small unmanned aerial vehicles (LSSUAVs) using 2.4GHz frequency band.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Communications, ICC 2017
EditorsMerouane Debbah, David Gesbert, Abdelhamid Mellouk
ISBN (Electronic)9781467389990
DOIs
StatePublished - 28 Jul 2017
Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: 21 May 201725 May 2017

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607

Conference

Conference2017 IEEE International Conference on Communications, ICC 2017
Country/TerritoryFrance
CityParis
Period21/05/1725/05/17

Keywords

  • SVDD recognition
  • UAV detection
  • hash fingerprint

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