Title :
Information extraction from ultrawideband ground penetrating radar data: A machine learning approach
Author :
Seyfried, Daniel ; Busche, André ; Janning, Ruth ; Schmidt-Thieme, Lars ; Schoebel, Joerg
Author_Institution :
Inst. for High-Freq. Technol. (IHF), Tech. Univ. Braunschweig, Braunschweig, Germany
Abstract :
To detect and characterize pipes and cables buried in the ground and to track their course we propose a new approach, which consists of an ultrawideband radar system employed as Ground Penetrating Radar (GPR) and a machine learning algorithm for the objects´ hyperbola identification and evaluation directly in the recorded radargram.
Keywords :
ground penetrating radar; learning (artificial intelligence); radar detection; ultra wideband radar; cable detection; information extraction; machine learning algorithm; object hyperbola identification; pipe detection; radargram; ultrawideband ground penetrating radar data; Antenna measurements; Generators; Ground penetrating radar; Machine learning; Machine learning algorithms; Noise; Buried Object Detection; Ground Penetrating Radar; Machine Learning Algorithms; Ultrawideband Radar;
Conference_Titel :
Microwave Conference (GeMiC), 2012 The 7th German
Conference_Location :
Ilmenau
Print_ISBN :
978-1-4577-2096-3
Electronic_ISBN :
978-3-9812668-4-9
