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.

Ground penetrating radar imaging is mostly tackled by resorting to approximate linear inversion algorithms that provide only qualitative maps of the probed scene in terms of location and approximate geometry of the buried anomalies. Deep learning techniques have recently been proposed to retrieve quantitative information as cost-effective alternatives to nonlinear inversion approaches. Indeed, deep neural networks can effectively learn to map the input data into spatial maps describing the electromagnetic properties of the targets. In this frame, the present paper considers the popular U-NET topology for performing quantitative subsurface imaging. Two different training strategies differing for the type of input data are examined and compared. The first one assumes the radargram in the time domain as the network input; differently, in the second one, the network takes in input a microwave tomographic image of the subsurface scene. Numerical results based on full-wave synthetic data and some experimental tests are reported to assess and compare the reconstruction performance of both training schemes.

This paper addresses the full 3-D subsurface imaging by a contactless ground penetrating radar survey carried out along a circular observation trajectory. The imaging problem is formulated as a linear inverse scattering one and accounts for the electromagnetic wave propagation through the air-soil interface thanks to a ray-based propagation model. The inverse problem is ill-posed and solved in a regularized way by resorting to the Truncated Singular Value Decomposition inversion scheme. The resolution performance achievable with the circular synthetic aperture is investigated in detail. Specifically, the focus is to examine how the filtering properties of the scattering operator are influenced by the geometrical and electromagnetic parameters of the scenario. Reconstruction results confirm the effectiveness of the imaging approach and support the validity of the theoretical resolution analysis.

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.

This letter addresses the classical problem of estimating the achievable resolution in terms of the configuration parameters and features of the background media in microwave imaging problems. In particular, we focus on the 2-D scalar case and a homogeneous medium, while data are collected in the near field by a multimonostatic multifrequency configuration. An analytical formula is derived to estimate the transverse resolution. The formula is determined by resorting to the evaluation of the point-spread function (PSF) using the weighted adjoint method. The effect of the configuration parameters on the transverse resolution is investigated. Numerical results confirm the spatially varying behavior of the transverse resolution and the accuracy of the proposed formula by assessing the capability to distinguish two nearby objects.

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 article deals with the investigation of the Martian subsurface in the Utopia Planitia region using Zhurong’s ground-penetrating radar. The low-frequency data recorded along the overall path followed by the rover are processed thanks to a customized signal processing pipeline aiming on one side to minimize the artifacts in the radargrams and, on the other one, to obtain images of the subsoil that aid the following analysis and interpretation stage. The images reveal a three-layer structure beneath the landing area up to the depth of ~40 m. The general scattering characteristics indicate the presence of$5~\sim ~8$-m regolith,$2~\sim ~3$-m fine materials, and$15~\sim ~30$m embedded with blocks generating a significant electromagnetic scattering. The existence of an upward decrease in grain size is resulted from long-term weathering and repeated impacts or the occurrence of flood events. The radargrams depict a distinctive local enhancement of the scattering at the end of the rover route from Sol 292 to Sol 325. Analysis of the layout of barchan dunes and the distribution of craters suggests that the resurfacing activity is the result of a complex combination of tectonic activities and impact events.

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.

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.

Pillars represent some of the commonest supporting elements of modern and historical buildings. Nondestructive testing methods can be applied to gain information about the status of these structural elements. Among them, ground penetrating radar (GPR) is a popular diagnostic tool for the assessment of concrete structures. Despite several theoretical and experimental studies on concrete structural evaluation by GPR have been reported, little work has been done so far with respect to pillars. Owing to their circular geometry, pillars are complex multiscattering environments, which render the interpretation of the radar images very challenging. This article deals with the application of radio frequency tomography as a nondestructive technique for imaging the inner structure of pillars. The main goal of the study is the assessment of the imaging performance that can be obtained in comparison to conventional GPR exploiting a multimonostatic configuration. Accordingly, potentialities and performance of multimonostatic and multiview/multistatic measurement configurations are herein investigated in the inverse scattering framework. For each measurement configuration, the regularized reconstruction of a point-like target and the spectral content are evaluated. The data inversion is carried out by means of the truncated singular value decomposition scheme. Tomographic reconstructions based on full-wave synthetic data are shown to support the comparative analysis.

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.

Radar imaging is typically based on linear models of the electromagnetic scattering phenomenon. These models are robust and computationally efficient, but do not account for mutual interactions among targets in the scene and between the targets and the surrounding environment. As a result, the radar images are characterized by spurious targets, i.e., multipath ghosts, which appear at positions where no physical targets exist. In this letter, we compare two key approaches for clutter suppression. The first approach applies multiplicative fusion of the images corresponding to subapertures of the deployed array, whereas the second approach is based on coherence factor filtering, which enhances the image quality by suppressing low-coherence features. We assess the performance of these two methods in terms of imaging and detection capabilities. Numerical results based on synthetic data are reported to support the comparative analysis.

This paper addresses the problem of imaging targets using radio signals transmitted by the nodes of a wireless sensor network. The sensors are assumed to be simple wireless communication devices, e.g., Wi-Fi cards, which are capable to measure only the received signal power. A fully incoherent linear inverse scattering approach is herein tackled, and a Rytov-based model is considered. The main contribution of this paper is concerned with the evaluation of the imaging performance achievable when phase information is not exploited in the inversion stage. The analysis is worked out with the singular value decomposition tool, in order to foresee the resolution limits for a given network arrangement and noise level on data. The popular coherent inverse scattering model of Born approximation is also considered as benchmark for comparison purposes. Numerical results based on full-wave data are reported to highlight the actual capabilities of the incoherent tomography.

Imaging of rebars inside a concrete block is investigated using the quadratic forward model of RF Tomography for three different antenna configurations. Reconstructed images are obtained using the method of moments.

The large diffusion of wireless infrastructures in public and private areas is currently stimulating research on surveillance radar systems capable of exploiting network transmissions as potential sources of opportunity. Since these sources are generally narrowband, we propose in this letter a single-frequency approach for imaging targets by using passive arrays deployed around the scattering scene. Single-frequency data allow casting the imaging as an inverse source problem, which avoids the need to retrieve information about the sources prior to imaging. The drawbacks of the highly coarse resolution and blinding effects due to the sources are overcome by employing a multiarray image fusion strategy in conjunction with a change detection scheme for imaging moving targets. The proposed approach is tested via numerical experiments based on full-wave synthetic data corresponding to an indoor scenario.

An effective way to solve the inverse scattering from dielectric objects relies on the Born approximation, which allows to linearize the problem and retrieve a qualitative reconstruction of the targets in terms of location and extent. The limits of the validity of the linear model can be extended by considering a quadratic approximation of the operator relating the scattered field data to the unknown object function. The use of the quadratic operator allows on the one hand to recover additional spatial variations of the object profile and on the other hand to mitigate the local minima (false solution) problem typically affecting nonlinear inversion methods. In this letter, we present an experimental validation of the quadratic inverse model for dielectric objects in free space. The data processing confirms that the tomographic images based on the quadratic model are better resolved compared to the ones provided by the inversion of the linear Born model.

Tracking moving targets hidden behind visually opaque structures as building walls is a crucial issue in many surveillance, rescue, and security applications. The electromagnetic waves at the low microwave frequency range penetrate into common building materials and thereby enable the radar to expose behind the wall scene. However, due to complexity of the scattering scenario, the radar signal undergoes multipath propagation phenomena. These typically manifest themselves as environmental clutter which may impair detection and tracking of true targets. In this paper, a signal processing strategy is proposed to track multiple extended targets in a scene by means of a wide-band monostatic through-wall radar. The system collects data sets at regular time steps which are first processed by a microwave tomographic technique. Then, a detection/tracking stage is implemented in order to track the position and dynamics of targets in real time. An extended target-tracking approach is applied to properly exploit at the tracking stage the information related to extended nature of targets. The effectiveness of the proposed signal processing chain is assessed by numerical tests based on full-wave data pertaining to an indoor scenario.

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.

Through-wall imaging (TWI) requires dealing with targets embedded in a complex obscuring environment such as the walls of a building. This obscuring layout is often composed by many simple elements (possibly interacting) such as slabs, corners, and T-like structures. Most of the existing literature on TWI has focused on slab-like walls, which is reasonable when the targets are relatively far from corners. This paper instead concerns the TWI in the more challenging situation where the targets are in close proximity (inside and/or outside) of a building corner. The aim is to gain insight into how propagation through the corner impacts on the imaging problem. To keep the study simple, a preliminary analysis is presented for a 2-D geometry under the linearized Born approximation. First, the Green's function, as well as the kernel of the relevant scattering operator, is evaluated by using a high-frequency analytical approach based on the geometrical optics and the uniform theory of diffraction. This allows one to take into account the multipath propagation phenomena and provide thus an expression of the scattering operator more accurate than that viable under the assumption of a simple slab wall. Then, the imaging is achieved by solving the relevant linear inverse scattering problem with a regularizing truncated-singular-value-decomposition algorithm. The filtering introduced by the inversion procedure, which is dependent on the considered background scenario, is highlighted and linked to the achievable performance while imaging targets both internal and external with respect to the corner. Finally, reconstruction results obtained from synthetic data are reported to assess the approach.

We address the problem of imaging targets located behind an inhomogeneous wall made with cinderblocks. The problem, which has relevance in through-wall-imaging applications, is characterized by the presence of multipath propagation phenomena usually producing artifacts and distortions in the retrieved images, if not suitably accounted for in the scattering model. The strategy here adopted to mitigate this issue is to employ a linearized scattering model based on the Born approximation, where the kernel of the relevant integral equation is evaluated numerically by means of the finite-difference time-domain method. In this way, the complexity of the background scenario is accurately taken into account. The inversion is successfully performed by the truncated singular value decomposition algorithm so as to regularize the inverse problem. The achievable imaging capabilities are analyzed in terms of resolution limits, and most notably, resolution can be effectively enhanced, owing to multipath exploitation. Numerical tests based on synthetic data are reported to assess the reconstruction performance in the case of canonical objects.

The analytical results reported in this paper refer to the evaluation of the field strength in and around an acute-angled dielectric structure, which is modeled by a penetrable wedge. The proposed approach is in the UTD framework, so that the field level is obtained by adding the GO field and the diffraction contribution. This last is determined by considering a Physical Optics approximation for the equivalent electric and magnetic surface currents involved in the radiation integrals used to represent the fields scattered in the inner region of the wedge and the surrounding space. Uniform asymptotic evaluations of such integrals allow one to obtain closed form expressions for the diffracted field, thus resulting easy to handle and compute.

This letter deals with the electromagnetic imaging in the presence of multipath propagation of interest for through-wall and urban sensing scenarios. The 2-D tomographic approach here presented combines a linear inverse scattering model, based on the Kirchhoff approximation, with the finite-difference time-domain (FDTD) technique. In particular, FDTD is exploited to evaluate the incident field and Green's function in noncanonical scenarios, so that the kernel of the linear integral equation is completely built. After, an inversion scheme based on the truncated singular value decomposition is applied to obtain a regularized solution of the problem. Numerical results demonstrate that the proposed approach yields well-focused images free of multipath ghosts, thus allowing to discriminate the actual target position. Moreover, it permits to highlight the capabilities offered by multipath exploitation such as improved crossrange resolution and detection of targets in the non-line-of-sight region of the radar.