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Microwave Tomography: Advancements and Applications
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Microwave Tomography: Advancements and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 9323

Special Issue Editors

Dr. Giovanni Ludeno

E-Mail Website1 Website2
Guest Editor
Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), Napoli, Italy
Interests: ocean monitoring; radar imaging; surface waves; ocean engineering; inverse problems; coastal bathymetry; microwave tomography
Special Issues, Collections and Topics in MDPI journals
Dr. Livia Lantini

E-Mail Website
Guest Editor
School of Computing and Engineering, University of West London, London, UK
Interests: ground penetrating radar; remote sensing; signal processing; data processing; numerical simulations; civil engineering; forestry engineering; highway engineering; pavement engineering; construction materials
Dr. Ilaria Catapano

E-Mail Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment (IREA-CNR), National Research Council of Italy, 80124 Napoli, Italy
Interests: EM diagnostics; microwave tomography; EM modeling; remote sensing; terrestrial; drone; aerial GPR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microwave tomography (MWT) is a broad research field, wherein the ability of microwaves to penetrate opaque dielectric materials is exploited to perform non-invasive surveys devoted to characterizing the surface and interior of an investigated scenario. Therefore, MWT covers challenges related to the design of sensors/exposures systems and the development of imaging strategies, that can account for complex scenarios and/or be optimized for a certain application. This Special Issue deals with methodological and technological advancements referred to both hardware and software issues, working with signals in the frequency range from some hundred to a few thousand Hertz, and regarding in situ, close, and remote sensing. Specifically, it tackles strategies and technical solutions based on the analysis of microwave–material interactions and their suitable models. Moreover, innovations, possibly exploiting artificial intelligence and designed both for assessed applicative fields, such as subsoil or structure surveys, and innovative ones, i.e., health monitoring, industrial quality control, security, and safety, are welcome.

Dr. Giovanni Ludeno
Dr. Livia Lantini
Dr. Ilaria Catapano
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microwave and radar imaging
  • signal processing
  • microwaves sensors
  • radar systems
  • inverse scattering
  • Artificial Intelligence (AI)

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Published Papers (5 papers)

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Research

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15 pages, 2995 KiB  
Article
Multiview Multistatic vs. Multimonostatic Three-Dimensional Ground-Penetrating Radar Imaging: A Comparison
by Mehdi Masoodi, Gianluca Gennarelli, Francesco Soldovieri and Ilaria Catapano
Remote Sens. 2024, 16(17), 3163; https://doi.org/10.3390/rs16173163 - 27 Aug 2024
Viewed by 905
Abstract
The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality [...] Read more.
The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided. Full article
(This article belongs to the Special Issue Microwave Tomography: Advancements and Applications)
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15 pages, 2791 KiB  
Article
Quantitative Inversion of Multiantenna Ground-Penetrating Radar Data with Modeling Error Correction Based on Long Short-Term Memory Cells
by Alessandro Fedeli, Valentina Schenone and Andrea Randazzo
Remote Sens. 2024, 16(12), 2050; https://doi.org/10.3390/rs16122050 - 7 Jun 2024
Viewed by 601
Abstract
Quantitative inversion of GPR data opens the door to precise characterization of underground environments. However, in order to make the inverse scattering problem solution easier from a computational viewpoint, simplifying assumptions are often applied, i.e., two-dimensional approximations or the consideration of idealized field [...] Read more.
Quantitative inversion of GPR data opens the door to precise characterization of underground environments. However, in order to make the inverse scattering problem solution easier from a computational viewpoint, simplifying assumptions are often applied, i.e., two-dimensional approximations or the consideration of idealized field probes and electromagnetic sources. These assumptions usually produce modeling errors, which can degrade the dielectric reconstruction results considerably. In this article, a processing step based on long short-term memory cells is proposed for the first time to correct the modeling error in a multiantenna GPR setting. In particular, time-domain GPR data are fed into a neural network trained with couples of finite-difference time-domain simulations, where a set of sample targets are simulated in both realistic and idealized configurations. Once trained, the neural network outputs an approximation of multiantenna GPR data as they are collected by an ideal two-dimensional measurement setup. The inversion of the processed data is then accomplished by means of a regularizing Newton-based nonlinear scheme with variable exponent Lebesgue space formulation. A numerical study has been conducted to assess the capabilities of the proposed inversion methodology. The results indicate the possibility of effectively compensating for modeling error in the considered test cases. Full article
(This article belongs to the Special Issue Microwave Tomography: Advancements and Applications)
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16 pages, 6763 KiB  
Article
Approximate Evaluation of the Resolution in Near Field Remote Sensing
by Ehsan Akbari Sekehravani and Giovanni Leone
Remote Sens. 2023, 15(14), 3593; https://doi.org/10.3390/rs15143593 - 18 Jul 2023
Cited by 1 | Viewed by 1954
Abstract
In linear inverse scattering, the performance of the imaging system is sometimes evaluated in terms of its resolution, i.e., its capability to reconstruct a point-like scatterer. However, there is still a lack of analytical studies on the achievable resolution. To address this, we [...] Read more.
In linear inverse scattering, the performance of the imaging system is sometimes evaluated in terms of its resolution, i.e., its capability to reconstruct a point-like scatterer. However, there is still a lack of analytical studies on the achievable resolution. To address this, we consider the point spread function (PSF) evaluation of the scattered near field for the single frequency and multi-view/multi-static case in homogeneous medium. Instead of numerically computing the PSF, we propose and discuss an approximate closed form under series expansions according to the angular ranges of both source and receiver location. In order to assess the effectiveness of the proposed approximation, we consider two cases including both full and limited view angles for the incident field and observation ranges. In addition, we provide a localization application to show the usefulness of the theoretical discussion. Numerical results confirmed the analytical investigations. Full article
(This article belongs to the Special Issue Microwave Tomography: Advancements and Applications)
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17 pages, 6742 KiB  
Article
An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation
by Maria Antonia Maisto, Chandan Bhat and Raffaele Solimene
Remote Sens. 2023, 15(12), 3012; https://doi.org/10.3390/rs15123012 - 8 Jun 2023
Cited by 1 | Viewed by 1222
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Microwave Tomography: Advancements and Applications)
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Review

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20 pages, 6499 KiB  
Review
Range Limitations in Microwave Quantum Radar
by Gabriele Pavan and Gaspare Galati
Remote Sens. 2024, 16(14), 2543; https://doi.org/10.3390/rs16142543 - 10 Jul 2024
Viewed by 2975
Abstract
This work, written for engineers or managers with no special knowledge of quantum mechanics, nor deep experience in radar, aims to help the scientific, industrial, and governmental community to better understand the basic limitations of proposed microwave quantum radar (QR) technologies and systems. [...] Read more.
This work, written for engineers or managers with no special knowledge of quantum mechanics, nor deep experience in radar, aims to help the scientific, industrial, and governmental community to better understand the basic limitations of proposed microwave quantum radar (QR) technologies and systems. Detection and ranging capabilities for QR are critically discussed and a comparison with its closest classical radar (CR), i.e., the noise radar (NR), is presented. In particular, it is investigated whether a future fielded and operating QR system might really outperform an “equivalent” classical radar, or not. The main result of this work, coherently with the recent literature, is that the maximum range of a QR for typical aircraft targets is intrinsically limited to less than one km, and in most cases to some tens of meters. Detailed computations show that the detection performance of all the proposed QR types are orders of magnitude below the ones of any much simpler and cheaper equivalent “classical” radar set, in particular of the noise radar type. These limitations do not apply to very-short-range microwave applications, such as microwave tomography and radar monitoring of heart and breathing activity of people (where other figures, such as cost, size, weight, and power, shall be taken into account). Moreover, quantum sensing at much higher frequencies (optical and beyond) is not considered here. Full article
(This article belongs to the Special Issue Microwave Tomography: Advancements and Applications)
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