Comparison of algorithms for finding the air-ground interface in ground penetrating radar signals

Wood, John C.; Bolton, Jeremy; Casella, George; Collins, Leslie M.; Gader, Paul D.; Glenn, Taylor; Ho, Jeffery; Li, Wen; Mueller, R.; Smock, Brandon; Torrione, Peter A.; Watford, Ken; Wilson, Joseph N.

doi:10.1117/12.884201

Cited by 7 publications

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“…A large scale comparison of the performance of various approaches to ground response tracking can be found in = [17]= [17], and the Viterbi algorithm is among the best performers in that comparison as well.…”

Section: Resultsmentioning

confidence: 99%

The Viterbi algorithm as an approach for incorporating spatial information into air/ground interface inference in GPR data

2011

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Rough surfaces present an impediment to the detection of buried threats with ground penetrating radar (GPR). Besides introducing artifacts in the sub-surface due to rough scattering, very rough or uneven surfaces can make inference of the location of the ground response from GPR data difficult. Since many algorithms rely on the accurate localization of the air/ground interface, mistakes in ground location inference can cause significant increases in false alarm rates. Many different approaches to localizing the ground in a particular A-scan have been proposed, but sharing information across multiple A-scans to form a realistic, smoothly varying ground response over many spatial locations is a difficult problem that often requires computationally expensive approaches for adequate solutions. In this work we present an application of the well-known Viterbi algorithm for accurate localization of the air/ground interface based on hypothesized locations from multiple nearby A-scans. Our implementation of the Viterbi algorithm enables principled incorporation of prior information into the ground tracking framework, and provides a solution capable of adapting computational complexity to the severity of the ground localization problem. Furthermore, the Viterbi algorithm can act as a meta-algorithm, allowing the use of different A-scan based ground detectors as input, for example. This work illustrates how the Viterbi algorithm can be incorporated into pre-screening algorithms to provide improved target detection rates at lower false alarm rates.

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Section: Resultsmentioning

confidence: 99%

The Viterbi algorithm as an approach for incorporating spatial information into air/ground interface inference in GPR data

2011

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“…In an evaluation of ground-tracking in challenging terrain, Global Max was found to completely disagree with experts' assessments of the ground location in 16.96% of all A-scans. 1 While this can be thought to represent a worst-case scenario for performance by the algorithm, at more than 8 errors per B-scan, it is fairly significant. If the ground estimate is used to perform alignment for landmine detection, large estimation errors significantly distort landmine signatures and cause an increase in the amount of false alarms.…”

Section: Algorithms For Ground-trackingmentioning

confidence: 98%

DynaMax+ ground-tracking algorithm

2011

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In this paper, we propose a new method for performing ground-tracking using ground-penetrating radar (GPR). Ground-tracking involves identifying the air-ground interface, which is usually the dominant feature in a radar image but frequently is obscured or mimicked by other nearby elements. It is an important problem in landmine detection using vehicle-mounted systems because antenna motion, caused by bumpy ground, can introduce distortions in downtrack radar images, which ground-tracking makes it possible to correct. Because landmine detection is performed in real-time, any algorithm for ground-tracking must be able to run quickly, prior to other, more computationally expensive algorithms for detection. In this investigation, we first describe an efficient algorithm, based on dynamic programming, that can be used in real-time for tracking the ground. We then demonstrate its accuracy through a quantitative comparison with other proposed ground-tracking methods, and a qualitative comparison showing that its ground-tracking is consistent with human observations in challenging terrain.

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“…We then propose and apply a measure of surface roughness based on the frequency content of the contour signal. To infer the ground position and shape, we use an algorithm called DynaMax+, 14,15 which is based on the Viterbi algorithm. The estimated ground contour in a 2D image is represented as a 1D signal, with g(h) being the vertical position of the ground associated with horizontal position h. We then take the discrete Fourier transform (DFT) of the contour signal and compute the surface roughness as the unnormalized spectral centroid, µ, given by…”

Section: Contexts For Landmine Detectionmentioning

confidence: 99%

Improved landmine detection through context-dependent score calibration

2016

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Landmine detection methods face the challenging task of discriminating a wide variety of targets in a diverse array of environmental conditions. As a result, successful approaches often utilize multiple sensing modalities and algorithms in order to overcome the limitations of any one particular approach. The development of complementary approaches is often motivated by a reductionist view, which reduces a detector's overall limitations to its limitations within particular contexts. However, when a detector is evaluated on a diverse data set, a new source of error arises that is not reflected in its performance within the contexts in isolation, which we refer to as mis-calibration error. Mis-calibration occurs when the true likelihood of target presence conditioned on a detector's output significantly varies across different contexts. In this work, we demonstrate the effect of context-dependent mis-calibration first on a hypothetical detector and then on four different landmine detection methods. To eliminate error due to mis-calibration, we propose a strategy called context-dependent score calibration (CDSC), which uses a monotonic calibration approach that maximizes the AUC (area under the ROC curve) of the calibrated output. We then show the gain in observed performance achieved on a diverse dataset when these four landmine detection algorithms are properly calibrated across the different contexts.

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Comparison of algorithms for finding the air-ground interface in ground penetrating radar signals

Cited by 7 publications

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The Viterbi algorithm as an approach for incorporating spatial information into air/ground interface inference in GPR data

The Viterbi algorithm as an approach for incorporating spatial information into air/ground interface inference in GPR data

DynaMax+ ground-tracking algorithm

Improved landmine detection through context-dependent score calibration

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