DocumentCode :
3055311
Title :
Fast GPR underground shape anomaly detection using the Semi-Analytic Mode Matching (SAMM) algorithm
Author :
Morgenthaler, Ann ; Rappaport, Carey
Author_Institution :
Electr. & Comput. Eng. Dept., Northeastern Univ., Boston, MA, USA
fYear :
2013
fDate :
21-26 July 2013
Firstpage :
1422
Lastpage :
1425
Abstract :
Ground penetrating radar is an effective means of characterizing the subsurface and is an invaluable tool for detecting buried anomalies, including land mines, utility lines, tunnels, and pavement deterioration [1-3]. The conventional strategies of migrating monostatic time domain signals or using synthetic aperture radar (SAR) techniques to produce images are improved in this work by a model-based inversion algorithm which uses the Semi-Analytic Mode Matching (SAMM) method [4,5]. Similar to pattern matching, this approach simulates the scattering from a buried object of specific size and shape and compares it to observed signals. Since scattering is considered from the entirety of the target rather than by analyzing each spatial pixel as in SAR imaging, the inverse-SAMM algorithm is superior at characterizing larger buried objects. Here, we consider a forward-looking, standoff, vehicle-mounted multi-monostatic stepped-frequency radar operating at 0.4-3 GHz searching for buried mines in two dimensions (depth and down-track). Detection and localization of metallic or plastic-cased mines is excellent and size characterization is reasonable, even when fairly substantial ground surface roughness is included.
Keywords :
buried object detection; ground penetrating radar; mode matching; synthetic aperture radar; buried object scattering; fast GPR underground shape anomaly detection; ground penetrating radar; ground surface roughness; metallic mines; model-based inversion algorithm; multimonostatic stepped-frequency radar; plastic-cased mines; semianalytic mode matching algorithm; synthetic aperture radar techniques; Correlation; Ground penetrating radar; Metals; Rough surfaces; Scattering; Shape; Surface roughness; Ground penetrating radar; computational modeling; synthetic aperture radar;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
ISSN :
2153-6996
Print_ISBN :
978-1-4799-1114-1
Type :
conf
DOI :
10.1109/IGARSS.2013.6723051
Filename :
6723051
Link To Document :
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