Combining Clutter Learning with LS for Improved Buried Target Detection in GPR

Kumlu D., Erer I.

9th International Conference on Recent Advances in Space Technologies (RAST), İstanbul, Turkey, 11 - 14 June 2019, pp.607-611 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/rast.2019.8767861
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.607-611
  • Istanbul Technical University Affiliated: Yes


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.