Combining clutter learning with LS for improved buried target detection in GPR

Deniz Kumlu, Isin Erer

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

Abstract

The classical least squares (LS) filtering method is combined with the subspace-based methods for buried target detection in ground penetrating radar (GPR) images. The LS method is used to estimate the next A-scans from previously observed A-scans which are assumed to belong to clutter samples. Generally, A-scans used in the initial step are accepted as clutter for the LS to work correctly. However, this is not guaranteed and if the first observed A-scan samples contain any target information, LS method will fail. To avoid target component presence in previously observed A-scans, the pre-processing step is integrated to keep only the clutter information. This step is based on obtaining clutter information from GPR image by using subspace-based methods. Various subspace-based methods are used to validate the efficiency of the proposed pre-processing step compared to the classical LS method. This additional preprocessing step does not bring any computational burden and is appropriate for real-time target detection.

Original languageEnglish
Title of host publicationProceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019
EditorsS. Menekay, O. Cetin, O. Alparslan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages607-611
Number of pages5
ISBN (Electronic)9781538694480
DOIs
Publication statusPublished - Jun 2019
Event9th International Conference on Recent Advances in Space Technologies, RAST 2019 - Istanbul, Turkey
Duration: 11 Jun 201914 Jun 2019

Publication series

NameProceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019

Conference

Conference9th International Conference on Recent Advances in Space Technologies, RAST 2019
Country/TerritoryTurkey
CityIstanbul
Period11/06/1914/06/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • gprMax
  • ICA
  • least squares
  • NMF
  • PCA
  • SVD

Fingerprint

Dive into the research topics of 'Combining clutter learning with LS for improved buried target detection in GPR'. Together they form a unique fingerprint.

Cite this