2018
DOI: 10.1002/2017rs006402
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Automatic Detection and Classification of Buried Objects Using Ground‐Penetrating Radar for Counter‐Improvised Explosive Devices

Abstract: In this paper, a technique that can automatically detect and classify objects buried under the ground is proposed. The technique employs a ground‐penetrating radar that transmits electromagnetic waves in order to strike the objects and then receives the backscattering electromagnetic wave to perform signal processing. This signal processing is divided into four main steps as follows. First, preprocessing is used to reduce the clutter due to the effect of the media layer interface. Second, the late time of the … Show more

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Cited by 18 publications
(10 citation statements)
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“…Second, the Euclidean distance is generalized and then referred to as the Minkowski distance, whose equation is expressed as (13).…”
Section: K-nearest Neighbor Algorithmmentioning
confidence: 99%
See 2 more Smart Citations
“…Second, the Euclidean distance is generalized and then referred to as the Minkowski distance, whose equation is expressed as (13).…”
Section: K-nearest Neighbor Algorithmmentioning
confidence: 99%
“…In this approach, the principle of the singularity expansion method (SEM) was applied to encode the tag ID into the tag's structure with the CNR, generally called the pole, and to retrieve the tag ID from the late-time portion of the tag response by using the matrix pencil method (MPM). The main drawback of the MPM is that is requires prior knowledge of the commencement of the late time, which relies upon the geometry and orientation of objects [12], [13]. However, this knowledge is unknown for the non-cooperative scenario.…”
Section: Introductionmentioning
confidence: 99%
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“…1 in which its peak is not notable. We also proposed the use of poles extracted from the GPR frequency response by using the short-time matrix pencil method (STMPM) in order to automatically detect and classify buried improvised explosive devices (IEDs) [22]. This paper proposes the use of poles to map the physical and dielectric properties of the layered soil, instead of directly using the frequency response whose Q-factor is generally low.…”
Section: Introductionmentioning
confidence: 99%
“…The solid lines that denote the separating hyperplanes being employed as decision boundaries were expressed as fn1 = -14.2308αn1 -5.7384 and fn2 = -18.8611αn2 -9.4898 GHz. The margin perpendiculars to these hyperplanes were 2/||w1|| = 0.2925 × 10 8 and 2/||w2|| = 0.1391 × 10 8 [22]. One can map the poles to soil type strongly related to the bulk density by using these decision boundaries.…”
mentioning
confidence: 99%