Threat Detection In GPR Data Using Autoregressive Modelling

Sahin S., Demir C., Erer I.

28th Signal Processing and Communications Applications Conference (SIU), ELECTR NETWORK, 5 - 07 October 2020 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu49456.2020.9302460
  • Keywords: Autoregressive, AR Model, Landmine, GPR
  • Istanbul Technical University Affiliated: Yes


In this paper we inspect two mine detection algorithms 12,31, suggest modifications and present results on detection of anti-personnel (AP) landmines using methods employing Auto Regressive (AR) modeling algortihms. First method is based on the statistical distance between AR models of the reference and simulated data. In literature, while the statistical distance is calculated only for A-Scan data, in this study we suggest statistical distance to be calculated for both A-Scan and rows of the processed data. The second method is relied on AR modeling of the clutter energy in the B-scan. To decide whether a threat signature is present, it is proposed to utilize the difference between the estimated AR model clutter energy and the energy of real data. It is shown that proposed AR model based algorithms can be utilized to detect threat in GPR data and some advices to improve detection performance are given.