Yere Nufuz Eden Radar Verisinde Ozbaglanimli Modelleme ile Tehdit Tespiti

Selim Sahin, Cagri Demir, Isin Erer

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Özet

In this paper we inspect two mine detection algorithms [2,3], 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.

Tercüme edilen katkı başlığıThreat Detection in GPR Data Using Autoregressive Modelling
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781728172064
DOI'lar
Yayın durumuYayınlandı - 5 Eki 2020
Harici olarak yayınlandıEvet
Etkinlik28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey
Süre: 5 Eki 20207 Eki 2020

Yayın serisi

Adı2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

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???event.eventtypes.event.conference???28th Signal Processing and Communications Applications Conference, SIU 2020
Ülke/BölgeTurkey
ŞehirGaziantep
Periyot5/10/207/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Keywords

  • AR Model
  • Autoregressive
  • GPR
  • Landmine

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