Scattered Far-Field Sampling in Multi-Static Multi-Frequency Configuration

Maisto, Maria Antonia; Masoodi, Mehdi; Leone, Giovanni; Solimene, Raffaele; Pierri, Rocco

doi:10.3390/s21144724

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…In the previous section, we concluded that devising a data sampling strategy is equivalent to finding a proper quadrature role to approximate the integral representation of ps f adj . In the event that the measurement domain is in the far field [47], or even when the Fresnel paraxial approximation works [28], the matter is relatively straightforward, since the scattering operator can be given in terms of a Fourier transform and the data can be sampled according to the Nyquist sampling rate. When far-field conditions or the Fresnel approximations do not hold, the spatial frequency band of the scattered field can be estimated by invoking stationary phase arguments and the Nyquist step set accordingly (as shown in [34,35,48]).…”

Section: The Warping Samplingmentioning

confidence: 99%

“…However, here, we consider the determination of the spatial sampling only. This is because optimizing the frequency sampling as well can lead to cumbersome configurations where the spatial positions change for each selected frequency [47]. Therefore, the sampling of the wavenumber band is achieved by employing standard arguments based on the range extent of the area to be imaged, i.e., ∆k 0 = π/n(z max − z min ).…”

Section: The Warping Samplingmentioning

confidence: 99%

See 1 more Smart Citation

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

Maisto¹,

Bhat

Solimene

2023

Remote Sensing

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In this paper, we are concerned with microwave subsurface imaging achieved by inverting the linearized scattering operator arising from the Born approximation. In particular, we consider the important question of reducing the required data to achieve imaging. This can help to reduce the radar system’s cost and complexity and mitigate the imaging algorithm’s computational burden and the needed storage resources. To cope with these issues, in the framework of a multi-monostatic/multi-frequency configuration, we introduce a new spatial sampling scheme, named the warping method, that allows for a significant reduction in spatial measurements compared to other literature approaches. The basic idea is to introduce some variable transformations that “warp” the measurement space so that the reconstruction point-spread function obtained by adjoint inversion is recast as a Fourier-like transformation, which provides insights into how to achieve the sampling. In our previous contributions, we focused on presenting and checking the theoretical background with simple numerical examples. In this contribution, we briefly review the key components of the warping method and present its experimental validation by considering a realistic subsurface scattering scenario for the case of a buried water pipe. Essentially, we show that the latter succeeds in reducing the number of data compared to other approaches in the literature, without significantly affecting the reconstruction results.

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Section: The Warping Samplingmentioning

confidence: 99%

Section: The Warping Samplingmentioning

confidence: 99%

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

Maisto¹,

Bhat

Solimene

2023

Remote Sensing

Self Cite

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“…This problem is exceptionally challenging because it is both nonlinear and ill-posed [8], demanding a substantial number of measurements to attain a satisfactory level of accuracy. Several methodologies have been devised to tackle such complex challenges, including phase retrieval algorithms [9], and optimization techniques [10,11,12,13]. Nonetheless, there remains plenty room for enhancement in terms of computational efficiency, robustness, and adaptability to diverse scenarios.…”

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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“…Here, we just extend the analysis to a three-layered background medium, which is relevant in TWRI applications. In particular, we focus in reducing the number of spatial measurements (which generally represents the bulk of measurement time), but the method can be applied to reduce both spatial and frequency measurements [27], although the resulting spatial-frequency measurement grid can be not necessarily convenient.…”

Section: Introductionmentioning

confidence: 99%

Sensor Arrangement in Through-the Wall Radar Imaging

Maisto

Masoodi

Pierri

et al. 2022

IEEE Open J. Antennas Propag.

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In this paper, the problem of how to spatially sample the scattered field in microwave through-the wall imaging is addressed. To this end, a two-dimensional scalar configuration for a three-layered back-ground medium is considered under a linearized scattering model. The aim is to collect as low as possible measurements by maintaining the same performance in the reconstructions. Accordingly, the number and the positions of the spatial measurement points are determined so that the point-spread function of the resulting semi-discrete problem approximates well the one of the ideal continuous case (i.e., when data space is not sampled at all). It is shown that the resulting measurement spatial positions must be non-uniformly arranged across the measurement domain and their number is generally much lower than the one returned by some literature sampling criteria. Also, the measurement points can be analytically determined by taking into account the geometrical parameters as well as the wall features. Numerical examples are included to check the theoretical arguments.

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Scattered Far-Field Sampling in Multi-Static Multi-Frequency Configuration

Cited by 8 publications

References 41 publications

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

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

Sensor Arrangement in Through-the Wall Radar Imaging

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