Scanning strategy learning for electronic support receivers by robust principal component analysis

Ismail Gul, Isın Erer

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

Abstract

Narrow-band Electronic Support receivers cannot detect radar signals in broad frequency ranges of the electromagnetic spectrum simultaneously. Hence, a frequency spectrum scanning strategy has to be planned. Commonly, this strategy is determined based on prior knowledge about possible threats. However, in an environment where the parameters of the radars are unconfirmed, it could be planned via learning-based representations. In previous researches, this sensor scheduling problem was modeled as a dynamical system by Predictive State Representations. Moreover, Singular Value Thresholding (SVT) algorithm is used in the subspace identification part to cope with the complexity of the system. In this work, We propose a scanning strategy learning method based on Robust Principal Component Analysis (RPCA).

Original languageEnglish
Title of host publicationArtificial Intelligence and Machine Learning in Defense Applications III
EditorsJudith Dijk
PublisherSPIE
ISBN (Electronic)9781510645844
DOIs
Publication statusPublished - 2021
EventArtificial Intelligence and Machine Learning in Defense Applications III 2021 - Virtual, Online, Spain
Duration: 13 Sept 202117 Sept 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11870
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceArtificial Intelligence and Machine Learning in Defense Applications III 2021
Country/TerritorySpain
CityVirtual, Online
Period13/09/2117/09/21

Bibliographical note

Publisher Copyright:
© 2021 SPIE

Keywords

  • Electronic Support Measures
  • Electronic Support Receiver
  • Electronic Warfare
  • Frequency Search Strategy
  • Robust Principal Component Analysis (RPCA)
  • Scanning Regime Learning

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