Radar target recognition via 2-D sparse linear prediction in missing data case

Bahar Ozen, Işin Erer

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

2 Citations (Scopus)

Abstract

Classical linear prediction methods based on leastsquare estimation yields radar images with high side lobes and many spurious scattering centers while singular value decomposition (SVD) truncation used to address these issues decreases the dynamic range of the image. So, radar images provided by these methods are not appropriate for classification purposes. In this work, sparsity constraints are induced on the prediction coefficients. The classification results demonstrate that the proposed sparse linear prediction methods give better accuracy rates compared to Multiple Signal Classification (MUSIC) method conventionally used for limited bandwidthobservation angle data. Classification performances of proposed methods are also investigated in case of the missing backscattered data. It is shown that the proposed methods are not affected from the missing data unlike the MUSIC method whose performance decreases with the increase in the percentage of the missing data.

Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1936-1940
Number of pages5
ISBN (Electronic)9780992862657
DOIs
Publication statusPublished - 28 Nov 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: 28 Aug 20162 Sept 2016

Publication series

NameEuropean Signal Processing Conference
Volume2016-November
ISSN (Print)2219-5491

Conference

Conference24th European Signal Processing Conference, EUSIPCO 2016
Country/TerritoryHungary
CityBudapest
Period28/08/162/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • BPDN
  • BPDN with regularization term
  • LASSO
  • Missing data, classification
  • Radar imaging
  • Sparse linear prediction

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