Yere Nufuz Eden Radar Verisinde Ozbaglanimli Modelleme ile Tehdit Tespiti

Translated title of the contribution: Threat Detection in GPR Data Using Autoregressive Modelling

Selim Sahin, Cagri Demir, Isin Erer

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

Abstract

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.

Translated title of the contributionThreat Detection in GPR Data Using Autoregressive Modelling
Original languageTurkish
Title of host publication2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728172064
DOIs
Publication statusPublished - 5 Oct 2020
Externally publishedYes
Event28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey
Duration: 5 Oct 20207 Oct 2020

Publication series

Name2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

Conference

Conference28th Signal Processing and Communications Applications Conference, SIU 2020
Country/TerritoryTurkey
CityGaziantep
Period5/10/207/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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