Spatial sensor arrangement in Through-the Wall radar imaging: Numerical results

Maisto, Maria Antonia; Masoodi, Mehdi; Solimene, Raffaele

doi:10.23919/at-ap-rasc54737.2022.9814268

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…For the subsurface scenario, the third layer in Figure 1 is assumed equal to the second one with relative permittivity e r . Also, in this case, we need to solve (11) for Dx, but the dependence of g in terms of…”

Section: Resolution Estimationmentioning

confidence: 99%

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Transverse Resolution in Through-the-Wall Radar Imaging

2022

RSL

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In this article, the estimation of transverse resolution in a typical through-the-wall radar imaging scenario under a linearized scattering approximation is addressed. The geometry is scalar and twodimensional, and the data are collected at a single frequency with a monostatic configuration. The measurement domain is located near the no-reactive zone, so that the evanescent contribution can be neglected, and the resolution is limited mainly to the configuration parameters. In particular, we succeed in estimating a closed formula that highlights the role of the configuration parameters on the achievable resolution.

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Section: Resolution Estimationmentioning

confidence: 99%

“…Finally, the achievable transverse resolution for TWRI is compared to the ones obtainable for subsurface and homogeneous scenarios. This article can be considered a companion of [11], where the focus was on determining a sampling strategy for collecting a scattered field.…”

Section: Introductionmentioning

confidence: 99%

Transverse Resolution in Through-the-Wall Radar Imaging

2022

RSL

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“…The first dataset is generated through a CUS strategy for collecting the intensity of radiated fields. The second dataset employs an ONUS approach, designed to discard redundant data [31,32,33]. Our primary objective is to assess the impact of these datasets on the U-Net model's performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Deep Learning-Based Antenna Array Coefficient Reconstruction through Optimized Data Management

Masoodi,

Omrani,

Catapano

et al. 2023

Preprint

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Complex array coefficients are essential parameters for designing planar antennas with desired radiation patterns. However, reconstructing these coefficients from nearfield phaseless measurements is a challenging inverse problem that requires a large amount of data and a robust optimization algorithm. In this paper, we propose a novel method that combines an optimal non-uniform sampling strategy and a U-Net model, a convolutional neural network (CNN) that has shown high accuracy in image segmentation, to achieve this purpose. In this scheme, the most informative measurements are collected and discards the redundant ones, reducing the data size and enhancing the data quality. Furthermore, we exploit a U-Net model to learn the mapping from the measurements to the array coefficients, avoiding the difficulties of solving a non-linear inverse problem. We demonstrate the effectiveness of our method through numerical simulations, showing that our method can accurately reconstruct the array coefficients with a reduced number of measurements and outperform existing classic methods.

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Diagnostics of conformal arrays by phaseless data and non-uniform sampling

Masoodi,

Leone

2023

2023 IEEE Conference on Antenna Measurements and Applications (CAMA)

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Spatial sensor arrangement in Through-the Wall radar imaging: Numerical results

Cited by 3 publications

References 15 publications

Transverse Resolution in Through-the-Wall Radar Imaging

Transverse Resolution in Through-the-Wall Radar Imaging

Enhancing Deep Learning-Based Antenna Array Coefficient Reconstruction through Optimized Data Management

Diagnostics of conformal arrays by phaseless data and non-uniform sampling

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