Machine Learning Based Forward Solver: An Automatic Framework in gprMax

Akhaury, Utsav; Giannakis, Iraklis; Warren, Craig; Giannopoulos, Antonios

doi:10.48550/arxiv.2111.12148

Cited by 1 publication

(2 citation statements)

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“…On the other hand, subspace techniques delve into the statistical properties of the data and decompose it into distinct subspaces, separating the useful information from the noise. Examples of subspace techniques include principal component analysis (PCA) [21] and singular value decomposition (SVD) [31]. The extracted hyperbolic signals were used to conduct the migration and MF processing for landmine classification.…”

Section: The Uav-borne Gpr Modelmentioning

confidence: 99%

“…In recent decades, machine learning has attracted significant attention for the automatic detection of underground objects. The interpretation of GPR data tends to rely on supervised deep learning algorithms, such as faster region-based convolutional neural networks (faster R-CNN) [16,17] and you-only-look-once (YOLO) [18,19], and unsupervised algorithms, such as generative adversarial nets (GANs) [20] and principal component analysis (PCA) [21]. Cognitive GPR for subsurface sensing based on edge computing and deep Q-learning networks (DQNs) has been proposed for application to UAV-based GPR systems [22].…”

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confidence: 99%

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Modeling and Implementation of a Joint Airborne Ground Penetrating Radar and Magnetometer System for Landmine Detection

Lee,

et al. 2023

Remote Sensing

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We modeled and implemented a joint airborne system integrating ground penetrating radar (GPR) and magnetometer (MAG) models specifically for landmine detection applications. We conducted both simulations and experimental analyses of the joint airborne GPR and MAG models, with a focus on detecting the metallic components of different types of landmines, including antitank (AT) M15 metallic, antipersonnel (AP) M16 metallic, and AT M19 plastic (minimum-metal) landmines. The GPR model employed the finite-difference time-domain (FDTD) method and was evaluated using a singular value decomposition (SVD) and Kirchhoff migration (KM) with matched filtering (MF). These advanced techniques enabled the automatic identification and precise focusing of the reflected hyperbolic signals emitted by the landmines while considering cross-range resolution. Additionally, the MAGs were utilized based on the magnetic dipole model with a de-trend and a spatial median filtering method to estimate the magnetic anomaly of the landmines while considering various data spatial intervals. The joint airborne GPR and MAG system was implemented by combining and integrating the GPR and MAG models for experimental validation. Through this comprehensive approach, which included experiments, simulations, and data processing, the design parameters of the final system were obtained. These design parameters can be used in the development and application of landmine detection systems based on airborne GPR and MAG technology.

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Section: The Uav-borne Gpr Modelmentioning

confidence: 99%