Proceedings of the XIII Internarional Conference on Ground Penetrating Radar 2010
DOI: 10.1109/icgpr.2010.5550141
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Detection and classification of landmines using AR modeling of GPR data

Abstract: Abstract-In this paper we present some results on detection and classification of low metal content anti personnel (AP) landmines using a modified version of the Auto Regressive (AR) modeling algorithm presented in [1,2]. A statistical distance is computed between the AR coefficients of the measured GPR time signal and the AR coefficients of a reference database (containing the AR models of the mines of interest) and a detection is declared if this distance is below a given threshold.

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Cited by 10 publications
(5 citation statements)
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“…Numerical simulation is currently being applied in some works, to facilitate the complex understanding of the electromagnetic waves propagation phenomena through the media. Thus, some authors (Balsi et al 2009;Deiana and Anitori 2010;Nabelek and Ho 2013) use the FiniteDifference Time-Domain (FDTD) method to assist in the characterization of the reflections patterns produced in the real data.…”
Section: Landmine and Uxo Detectionmentioning
confidence: 99%
“…Numerical simulation is currently being applied in some works, to facilitate the complex understanding of the electromagnetic waves propagation phenomena through the media. Thus, some authors (Balsi et al 2009;Deiana and Anitori 2010;Nabelek and Ho 2013) use the FiniteDifference Time-Domain (FDTD) method to assist in the characterization of the reflections patterns produced in the real data.…”
Section: Landmine and Uxo Detectionmentioning
confidence: 99%
“…Within this category, early works explored statistical machine learning, techniques that gained prominence in landmine detection, providing advanced approaches to handle sensor data. Deiana et al [41] discussed the use of statistical approaches, while Shi et al [42] explored AdaBoost classifiers. Gader et al [43] applied hidden Markov models in their work.…”
Section: Neural Networkmentioning
confidence: 99%
“…Commonly used techniques in landmine detection include correlation functions [23], cross-correlation with simulated samples [24] and least mean squares (LMS) methods or their variants, 2D LMS or 3D LMS [25]. Many researchers have begun to use statistical approaches [26], AdaBoost classifiers [27], hidden Markov models [4] and other techniques for landmine detection. Although these methods can provide significant improvements, most GPR signal processing in the literature involves classical static methods.…”
Section: Automated Detection Toolmentioning
confidence: 99%