Mr. Kyle Bradbury Profile

Kyle Bradbury

at Duke Univ

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Proceedings Article | 5 May 2009 Paper

Real-time Gaussian Markov random-field-based ground tracking for ground penetrating radar data

Kyle Bradbury, Peter Torrione, Leslie Collins

Proceedings Volume 7303, 730320 (2009) https://doi.org/10.1117/12.818781

KEYWORDS: Interfaces, General packet radio service, Detection and tracking algorithms, Sensors, Ground penetrating radar, Land mines, Antennas, Target detection, Statistical modeling, Statistical analysis

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Current ground penetrating radar algorithms for landmine detection require accurate estimates of the location of the air/ground interface to maintain high levels of performance. However, the presence of surface clutter, natural soil roughness, and antenna motion lead to uncertainty in these estimates. Previous work on improving estimates of the location of the air/ground interface have focused on one-dimensional filtering techniques to localize the air/ground interface. In this work, we propose an algorithm for interface localization using a 2- D Gaussian Markov random field (GMRF). The GMRF provides a statistical model of the surface structure, which enables the application of statistical optimization techniques. In this work, the ground location is inferred using iterated conditional modes (ICM) optimization which maximizes the conditional pseudo-likelihood of the GMRF at a point, conditioned on its neighbors. To illustrate the efficacy of the proposed interface localization approach, pre-screener performance with and without the proposed ground localization algorithm is compared. We show that accurate localization of the air/ground interface provides the potential for future performance improvements.

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