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Proceedings Paper

Adaptive Gaussian mixture models for pre-screening in GPR data
Author(s): Peter Torrione; Kenneth Morton Jr.; Lance E. Besaw
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Paper Abstract

Due to the large amount of data generated by vehicle-mounted ground penetrating radar (GPR) antennae arrays, advanced feature extraction and classification can only be performed on a small subset of data during real-time operation. As a result, most GPR based landmine detection systems implement "pre-screening" algorithms to processes all of the data generated by the antennae array and identify locations with anomalous signatures for more advanced processing. These pre-screening algorithms must be computationally efficient and obtain high probability of detection, but can permit a false alarm rate which might be higher than the total system requirements. Many approaches to prescreening have previously been proposed, including linear prediction coefficients, the LMS algorithm, and CFAR-based approaches. Similar pre-screening techniques have also been developed in the field of video processing to identify anomalous behavior or anomalous objects. One such algorithm, an online k-means approximation to an adaptive Gaussian mixture model (GMM), is particularly well-suited to application for pre-screening in GPR data due to its computational efficiency, non-linear nature, and relevance of the logic underlying the algorithm to GPR processing. In this work we explore the application of an adaptive GMM-based approach for anomaly detection from the video processing literature to pre-screening in GPR data. Results with the ARA Nemesis landmine detection system demonstrate significant pre-screening performance improvements compared to alternative approaches, and indicate that the proposed algorithm is a complimentary technique to existing methods.

Paper Details

Date Published: 23 May 2011
PDF: 11 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80171P (23 May 2011); doi: 10.1117/12.884136
Show Author Affiliations
Peter Torrione, New Folder Consulting, LLC (United States)
Kenneth Morton Jr., New Folder Consulting, LLC (United States)
Lance E. Besaw, Applied Research Associates, Inc. (United States)

Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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