Adaptive Gaussian mixture models for pre-screening in GPR data

Peter Torrione; Kenneth Morton Jr.; Lance E. Besaw

doi:10.1117/12.884136

23 May 2011 Adaptive Gaussian mixture models for pre-screening in GPR data

Peter Torrione, Kenneth Morton Jr., Lance E. Besaw

Proceedings Volume 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI; 80171P (2011) https://doi.org/10.1117/12.884136
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Peter Torrione, Kenneth Morton Jr., and Lance E. Besaw "Adaptive Gaussian mixture models for pre-screening in GPR data", Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80171P (23 May 2011); https://doi.org/10.1117/12.884136

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