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Proceedings Paper

Model-based approach to the detection, classification, and characterization of subsurface targets from forward-looking ground penetrating radar data
Author(s): Jie Cheng; Eric L. Miller
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Paper Abstract

Here we consider the use of model-based methods for the detection of buried objects from a sequence of synthetic aperture images obtained by a radar sensor moving linearly down a track. The scattering physics of the underlying sensing modality cause the relevant target signatures to change in a complex yet predictable manner as new images are obtained. To arrive at a tractable processing scheme which exploits these motion-induced changes, we develop a flexible parametric model capable of capturing the full variation of these signatures. A detection method based on a principal components analysis of estimated model vectors is then derived. Results are demonstrated using field data from a forward-looking sensor designed for landmine remediation.

Paper Details

Date Published: 13 August 2002
PDF: 9 pages
Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); doi: 10.1117/12.479102
Show Author Affiliations
Jie Cheng, Northeastern Univ. (United States)
Eric L. Miller, Northeastern Univ. (United States)

Published in SPIE Proceedings Vol. 4742:
Detection and Remediation Technologies for Mines and Minelike Targets VII
J. Thomas Broach; Russell S Harmon; Gerald J. Dobeck, Editor(s)

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