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Proceedings Paper

Fusion of KLMS and blob based pre-screener for buried landmine detection using ground penetrating radar
Author(s): Bora Baydar; Gözde Bozdaği Akar; Seniha Esen Yüksel; Serhat Öztürk
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Paper Abstract

In this paper, a decision level fusion using multiple pre-screener algorithms is proposed for the detection of buried landmines from Ground Penetrating Radar (GPR) data. The Kernel Least Mean Square (KLMS) and the Blob Filter pre-screeners are fused together to work in real time with less false alarms and higher true detection rates. The effect of the kernel variance is investigated for the KLMS algorithm. Also, the results of the KLMS and KLMS+Blob filter algorithms are compared to the LMS method in terms of processing time and false alarm rates. Proposed algorithm is tested on both simulated data and real data collected at the field of IPA Defence at METU, Ankara, Turkey.

Paper Details

Date Published: 3 May 2016
PDF: 7 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231D (3 May 2016); doi: 10.1117/12.2223743
Show Author Affiliations
Bora Baydar, Middle East Technical Univ. (Turkey)
IPA Defense Systems (Turkey)
Gözde Bozdaği Akar, Middle East Technical Univ. (Turkey)
Seniha Esen Yüksel, Hacettepe Univ. (Turkey)
Serhat Öztürk, TFO, LLC (United States)

Published in SPIE Proceedings Vol. 9823:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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