Proceedings PaperGPR preprocessing optimization with signal-to-clutter metrics
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Prior to the calculation of target detection features, ground penetrating radar (GPR) data typically requires extensive preprocessing to suppress clutter artifacts and enhance signals corresponding to weaker targets. Optimization of this GPR signal preprocessing pipeline is necessary to provide the best opportunity at visual detection and automatic target recognition. Manual, independent adjustment of the many configuration parameters in the data preprocessing pipeline is inefficient and not guaranteed to find an optimal result. In this paper, the authors present a new metric for GPR processed data quality and demonstrate its utility in an automated parameter sweep optimization of a large set of algorithm configuration parameters. The observed costs and benefits of using automated preprocessing optimization are presented and discussed.
For preprocessing optimization and evaluation, a cost function was desired that is independent of the target detection features – to enable independent evaluation of the various components of the GPR target detection software. The proposed cost function, JSUM, is a signal-to-clutter ratio (SCR) metric, derived from the known KSUM metric. JSUM was developed to be sensitive to a particular type of noise in GPR data not captured by KSUM. The response of JSUM and KSUM to different common types of noise was explored to qualify the usefulness of the metric.
JSUM was used as the cost function for a parameter sweep optimization across a set of preprocessing parameters. The outcomes of this optimization are presented for discussion.
PDF: 11 pages
Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87091O (7 June 2013);
John W. Roberts, Exponent, Inc. (United States)
Adam D. Mulliken, Exponent, Inc. (United States)
Published in SPIE Proceedings Vol. 8709:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII
J. Thomas Broach; Jason C. Isaacs, Editor(s)