3 May 2016 Fusion of KLMS and blob based pre-screener for buried landmine detection using ground penetrating radar
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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.
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Bora Baydar, Bora Baydar, Gözde Bozdaği Akar, Gözde Bozdaği Akar, Seniha Esen Yüksel, Seniha Esen Yüksel, Serhat Öztürk, Serhat Öztürk, "Fusion of KLMS and blob based pre-screener for buried landmine detection using ground penetrating radar", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231D (3 May 2016); doi: 10.1117/12.2223743; https://doi.org/10.1117/12.2223743
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