Non-destructive inspection of penetrable vertical structures demands innovative investigation paradigms and ad-hoc tools to support timely and efficient maintenance interventions. Although Ground Penetrating Radar (GPR) is an established technology, the inspection of large vertical structures with classical ground-based systems remains challenging when there is limited accessibility, the equipment is difficult to maneuver, the operator’s safety is placed at risk, and the time to perform the survey becomes long. A promising solution to address these limitations suggests the use of mini drones equipped with GPR systems. However, this possibility introduces new challenges in both system design and data processing. A practical approach for drone-based GPR monitoring involves adapting a traditional contact GPR system, combined with appropriate data processing techniques, to perform contactless surveys. This study delves deeper into this approach by comparing the imaging performance achieved when the same ground-coupled GPR system paired with a microwave tomography-based data processing is exploited for contact and contactless surveys. Experimental results related to the inspection of a reinforced concrete wall are presented and discussed.

This letter deals with full 3-D imaging by contactless multimonostatic ground penetrating radar (GPR) data by focusing on the effect of the measurement configuration on reconstruction performance. The imaging is faced as a linear inverse scattering problem and the truncated singular value decomposition (TSVD) is adopted to obtain the regularized solution. A criterion to determine a suitable spacing among the measurement points is provided and the effect of the data under sampling on the imaging results is also investigated. Numerical results based on the system point spread function are provided to verify the validity of the proposed criterion for the estimation of the nonredundant measurement spacing. Finally, reconstruction results relevant to a laboratory controlled experimental test validate the imaging approach as well as the derived data sampling criterion.

Radar imaging algorithms generally exploit linear models of the electromagnetic scattering phenomenon. This assumption leads to qualitative and computationally effective data inversion schemes, which only account for direct scattering from targets, whereas multipath signal contributions are neglected. As a result, multipath ghosts, i.e. false targets reconstructed at positions where no real target exists, affect the radar images thus preventing a reliable interpretation of the observed scene. This paper proposes a fully data-driven deep learning approach based on a convolutional neural network and microwave tomography to face this challenge. The approach achieves multipath ghost suppression for the case of small targets in terms of probing wavelength. In the proposed training scheme, the tomographic image affected by ghosts represents the input of the network while a ghost-free reconstruction is the output. Numerical simulations addressing the detection of metallic rebars via ground penetrating radar are presented. As shown, the proposed ghost removal strategy is effective and robust to variations of the scenario parameters on which the network is trained. Finally, an experimental validation shows the effectiveness of the proposed strategy even in operative conditions.

This paper presents an imaging approach for Multiple Input Multiple Output Ground Penetrating Radar (MIMO GPR) systems working in down-looking contactless mode. The approach exploits a linear approximation of the scattering phenomenon and is based on a ray-based propagation model, which takes into account the presence of the air-soil interface. Accordingly, the Interface Reflection Point concept is extended to the case of MIMO GPR. The proposed approach performs the imaging in the 2D scalar case and applies the Truncated Singular Value Decomposition regularization scheme to perform the inversion. The effectiveness of the approach is assessed by processing synthetic and real data. The real data are referred to the lunar soil and have been collected by means of the Lunar Regolith Penetrating Radar, installed on the Chang’E-5 lander.

This paper proposes a three-dimensional imaging approach for contactless ground penetrating radar surveys. The imaging problem is formulated in the linear inverse scattering context and solved by using the Singular Values Decomposition tool. A ray-based model accounting for the electromagnetic signal propagation into an inhomogeneous medium is developed to accurately evaluate the kernel of the integral equation to be inverted. Under the proposed model, an analysis of the spatial resolution performance is carried out as a function of the geometrical and electromagnetic parameters of the scenario. To this end, theoretical concepts based on diffraction tomography and the Singular Value Decomposition of the scattering operator are exploited. Reconstruction results based on full-wave simulated data assess the feasibility of the imaging approach.

This article investigates the capabilities of 24 GHz frequency-modulated continuous-wave (FMCW) multiple-input–multiple-output (MIMO) radar technology to retrieve sea surface currents and directional wave spectra. A procedure based on the dispersion relation, which was previously applied to process X-band marine radar data, is here exploited. The estimation performance of the radar sensor is first assessed by numerical tests in the case of synthetic sea wave fields with known characteristics in terms of wave direction and surface currents. Finally, the estimation procedure is assessed on real data collected at San Vincenzo quay in the port area of Naples, Italy. The achieved results are encouraging and highlight that 24 GHz FMCW MIMO radar is a viable technology for sea wave monitoring.

This letter deals with an imaging strategy able to manage effectively data collected on multiple lines by means of a Mini-Unmanned Aerial Vehicle (M-UAV) radar system operating in Sounder mode. The strategy, named multilines imaging approach (MIA), allows an effective 3-D pseudo-representation of the investigated volume. At the first step, MIA faces the problem of reconstructing 2-D domains (slices) by exploiting data collected on one or more lines. Then, MIA interpolates the 2-D reconstructions to provide the 3-D representation. The imaging of each slice is formulated as a linear inverse scattering problem, which is solved by means of the truncated singular value decomposition (TSVD) regularization scheme. The MIA effectiveness is assessed by processing real data collected at Archeological Park of Paestum and Velia, Paestum, Italy.

Internal decays in trees can rapidly escalate into a full decomposition of the inner structural layer, i.e., the “heartwood” layer, due to the action of aggressive diseases and fungal infections. This process leads to the formation of big cavities and hollows, which remain surrounded by the sapwood layer only. Estimating the thickness of the sapwood layer with a high degree of accuracy is therefore crucial for correct assessment of the structural integrity of hollow trees, as well as an extremely challenging task. In this context, ground-penetrating radar (GPR) has proven effective in providing details of the internal structure of trees. Nevertheless, the existing GPR processing methods still offer limited information on their internal configuration. This study investigates the effectiveness of GPR enhanced by a microwave tomography inversion approach in the assessment of hollow trees. To this aim, a living hollow tree was investigated by performing a set of pseudocircular scans along the bark perimeter with a hand-held common-offset GPR system. The tree was then felled, and sections were cut for testing purposes. A dedicated data processing framework was developed and tested through numerical simulations of hollow tree sections. The internal structure of the real trunk was therefore reconstructed via a tomographic imaging approach and the outcomes were quantitatively analyzed by way of comparison with the real sections’ main geometric features. The tomographic approach has proven very accurate in locating the sapwood–cavity interface and in the evaluation of the sapwood layer thickness, with a centimeter prediction accuracy.

The article presents an imaging approach for ground penetrating radar prospecting over a heterogeneous background soil characterized by a dielectric permittivity variation along depth. The proposed approach is based on a linear model of the electromagnetic scattering phenomenon, where the soil inhomogeneity in the reference scenario is accounted for by introducing an equivalent permittivity function. Such a function notably simplifies the computation of the kernel of the relevant linear integral equation to be inverted. Then, the data inversion is carried out in a regularized way by resorting to the truncated singular value decomposition scheme. A procedure for estimating the equivalent permittivity function, typically unknown in practical situations, is also proposed. Reconstruction results achieved from full-wave simulated data and real data collected in a controlled test site are provided to demonstrate the effectiveness and the reliability of the proposed imaging approach.

Ground-penetrating radar inspection of vertical structures, such as columns or pillars, is relevant in several applicative contexts. Unlike conventional subsurface prospecting, where the medium is accessible only from one side, the columns can be probed from various sides with measurement domains possibly encircling the structure. This makes it possible to retrieve more information about the scene, thanks to an increased view and data collection diversity. This article proposes an imaging approach for structures probed all around via vertical scans. The approach faces the imaging as a full 3-D electromagnetic inverse scattering problem and accounts for the vectorial nature of the scattering phenomenon. Moreover, the imaging approach is based on an approximate model of scattering and the inversion is regularized by means of the truncated singular value decomposition to produce stable and accurate results. The reconstruction capabilities of the proposed imaging approach are evaluated in terms of the achievable spatial resolution. To this end, a numerical analysis exploiting synthetic data allows investigating how the imaging quality depends on the number of vertical scans. Reconstruction results referred to data gathered in controlled conditions provide an experimental assessment of the achievable imaging capabilities.

Ground-penetrating radars operating at a stand-off distance from the probed domain are attractive diagnostic tools in several contexts. Despite notable advantages offered in terms of noninvasiveness and flexibility, the usage of stand-off configurations makes the imaging process more difficult. Indeed, an accurate reconstruction of the subsurface/hidden scene requires accounting for the presence of the air-soil interface into the scattering model. This article deals with a comparison of three different linear models for describing the scattering phenomenon under a stand-off configuration, where the presence of the layered medium is explicitly accounted for in the scattering model. The first model is rigorous and based on the spectral domain representation of the incident field and Green's function for the half-space scenario. The second model exploits a ray representation of the wave propagation from the radar to the target and vice versa by accounting for the refraction phenomenon at the interface. The third model is an approximated one, where the layered nature of the scenario is taken into account by means of an equivalent wavenumber, which allows a straightforward evaluation of the incident field and Green's function. The resolution performance of each inversion approach is assessed by means of a numerical analysis. Moreover, data referred to an experiment carried out in controlled conditions are processed and reconstructions are provided to support the comparative analysis among the three imaging approaches.

The Italian Space Agency (ASI) has recently entrusted CO.RI.S.T.A. with the development of a radar system that can be mounted onboard small airplanes or helicopters and may operate, at different frequencies belonging to the P-Band, either as Synthetic Aperture Radar (SAR) or as Sounder. In this work, we present preliminary results of the helicopter-borne desert campaign carried out with this system in 2018 over the Erfoud area, Morocco, in the frame of a project that has involved different public Italian Research Institutes and Universities.

In the last years, an increasing interest is addressed toward subsurface imaging carried out by means of innovative GPR systems, such as radar onboard flying platforms and forward looking devices. Here, innovation is mainly related to the adopted measurement configuration and entails an advancement in terms of imaging approaches. These should be, indeed, able to account for the variations, which arise in the scattering phenomenon underling GPR surveys. In this frame, the authors have previously proposed properly designed microwave tomographic approaches. Moreover, the achievable reconstruction capabilities have been investigated theoretically and assessed against numerical and experimental datasets. This paper aims at summarizing these previous research activities, by pointing out how imaging capabilities depend on the adopted measurement configuration.

This letter aims at comparing the reconstruction capabilities of standard beamforming and a linear inverse scattering approach. The analysis is carried out in terms of resolution limits, which are evaluated by using the Rayleigh criterion based on the investigation of the main lobe of the point spread function. A specific measurement configuration dictated by a through-the-wall radar prototype developed by IDS—Ingegneria dei Sistemi S.p.a. is considered, and both simulated and experimental data are processed.

An inverse scattering approach has been designed to process data gathered by means of forward-looking ground-penetrating radar systems. Such an inverse approach exploits a linear model of the scattering phenomenon and is able to account for the half-space 2-D geometry. In this frame, a theoretical study on the achievable reconstruction capabilities is provided with the aim to estimate the range and cross-range resolution limits and gain indications about the issue of how to design the measurement configuration. Finally, numerical and experimental examples are provided to assess the effectiveness of the approach.

This letter deals with the design, realization, and validation of a multimode/multifrequency airborne radar designed for both surface and subsurface prospections. The system operates in the frequency band from very high frequency (VHF) to ultrahigh frequency (UHF) and works in two different modes: 1) a nadir-looking sounder in the VHF band (carrier frequency of 163 MHz); and 2) a side-looking imager (i.e., synthetic aperture radar) in the UHF band with two channels at 450 and 860 MHz, respectively. The system validation has been carried out for the “sounder” mode due to helicopter-borne surveys carried out over an area in the Campania region, Southern Italy. The surveys have provided a first proof of system capability in obtaining useful information about the surface and shallower subsurface layers over a large scale and in a relatively short time. In particular, the data collected by the sounder have been processed by means of a microwave tomographic reconstruction approach, and features consistent with tunnels buried at a depth of 15 m have been identified.

Airborne ground penetrating radar is a suitable tool to perform cost-effective surveys of the underground of large possibly nonaccessible areas. To tackle the imaging problem underlying this technology, we propose a regularized inverse scattering procedure. The capabilities of the approach to achieve a stable solution and its imaging performance are first assessed against synthetic data. Then, results obtained by processing data collected by means of a gated stepped frequency system mounted on a helicopter are provided as examples of on-field test cases.

Imaging methods that are able to detect targets located behind a wall and monitor their motion have recently gained an increasing attention. To pursue this aim, we propose a qualitative inverse scattering approach based on the linear sampling method, which is a computationally effective nonapproximated shape-reconstruction strategy. In particular, we describe the formulation of the method for the through-the-wall scenario for both the case of static and dynamic targets. The results of a numerical analysis are reported as a preliminary assessment of the achievable performances.

A novel microwave imaging method to detect and possibly characterize nonaccessible targets concealed into a wall or a floor from the measured backscattered field is proposed. The problem is cast as an inverse scattering one aimed at imaging a piecewise homogeneous target embedded in a layered structure. The proposed method is based on a linear sampling method (LSM), a general and computationally effective shape-reconstruction approach that determines the targets morphology through an indicator defined as the regularized solution of a linear inverse problem. However, LSM cannot discern between homogeneous and inhomogeneous targets; hence, it may retrieve the cache but not characterize its content. To overcome this drawback, we propose a two-step approach in which the cache is first imaged by using the standard LSM indicator. Then, by taking advantage of the physical interpretation of the LSM and of the first step's result, a modified indicator is defined and exploited to detect the inclusions possibly hidden in the retrieved cache. Numerical examples are provided as a proof of concept.

This paper addresses the problem of reconstructing geometrical features of 3-D targets embedded into a nonaccessible region from multiview multistatic scattered field data. Sampling methods (SM) are simple and computationally effective approaches to pursue this task. However, their implementation requires a large number of multipolarization sources and probes. Moreover, their performances are often unsatisfactory for aspect-limited measurement configurations and lossy media. In order to tackle these drawbacks, usually faced in subsurface imaging, we propose a simplified and improved formulation based on the physical interpretation of SM. In particular, such a formulation relies on a small number of single polarization probes and exploits multifrequency data, for the first time in the framework of SM. The performances of the resulting approach are verified by monitoring 3-D regions of large extent.

This paper deals with the problem of reconstructing the geometrical features of 3D targets embedded in a lossy medium. Referring to the Linear Sampling Method, which is attractive due to its computational efficiency, we propose an improved formulation of it, which is able to provide an effective reconstruction of the deeper part of the investigated region. Moreover, the frequency diversity is exploited to obtain accurate shape reconstructions when the transmitting and receiving antennas are located on a narrow grid and a single- polarization of the scattered field is measured.

In this paper we show that a large number of 3D forward scattering problems can be efficiently solved by means of simply series expansions. Theoretical conditions assuring the applicability of the traditional Born series and the one arising from the Contrast Source - Extended Born model are derived discussed. Practical tools to foresee their convergence are also provided. Then, in order to enlarge the range of problems which can be solved exploiting series expansions, a new hybrid series is proposed. Numerical examples are given to asses effectiveness and simplicity of the proposed tools.

Accurate and reliable evaluation of the electromagnetic field scattered by dielectric objects is a canonical problem in the electromagnetic community. In the framework of integral equation formulations, iterative techniques, and in particular conjugate gradient (CG) schemes, are widely used. However, when the number of parameters grows, CG techniques may become too demanding from a computational point of view. In this paper, we show that many forward scattering problems can be conveniently solved by means of very simple series expansions, which allow a lower computational complexity and memory storage with respect to other iterative schemes. In particular, we consider three different series expansions, namely: 1) the traditional Born series; 2) the contrast source-extended Born series, which is recently introduced by rewriting the traditional source-type integral equations; and 3) a new series, which is a hybridization of the previous ones. Theoretical conditions for the applicability of the series expansions are discussed, and practical tools to foresee that a problem can be solved by means of these simple iterative schemes are provided. Numerical examples are reported for the sake of comparison and to assess performance.

A proper extension of the contrast source extended Born model, recently introduced in the two-dimensional scalar case, is proposed to establish a novel full-wave inversion method for nonlinear vectorial three-dimensional scattering problems. In particular, a suitable auxiliary function, embedding the parameters of the unknown targets, is introduced as fundamental unknown of the inverse problem instead of the widely used contrast function. Numerical examples confirm effectiveness and reliability of the proposed inversion approach, which also favorably compares with other methods previously known in the literature