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.

We present a two-step strategy to solve an inverse scattering problem in 2-D geometry. The first step approximates the inverse scattering as a convex optimization problem and provides an estimation of the total field inside the domain under investigation withouta prioriknowledge or tuning parameters. In the second step, the previously estimated total field is used to reconstruct the unknown contrast permittivity, which is represented by a superposition of level-1 Haar wavelet transform basis functions. Subject to${\ell _{1}}$-norm constraints of the wavelet coefficients, a least absolute shrinkage and selection operator (LASSO) problem that searches for the global minimum of the${\ell _{2}}$-norm residual is exploited by accounting for the sparsity of the wavelet-based permittivity representation. Numerical results are presented to assess the effectiveness of the proposed formulation against objects with relatively small electric size. Finally, the approach is validated against experimental data.

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.

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.

This document is an errata corrige of the above paper[1].

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 work aims to describe the characteristics and the status of development, including the results of a first preliminary testing campaign, of a low frequency airborne imaging radar developed in Italy for the Italian Space Agency.

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.

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.

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.

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.

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.

In the present paper, a microwave tomographic technique is used in order to analyze the effect of oil & gas platforms located in the Adriatic Sea on the radar returns of the RA-2 sensor, installed onboard the ENVISAT satellite. The study area is located in front of the Italian Adriatic coast of Ravenna harbor. The results from the analysis of waveforms show that hyperbolae are clearly visible. The tomographic reconstructions exhibit signatures corresponding to their presence. Their relationship is thought dependent on the geometric conditions. Some examples are discussed in this paper.

This letter deals with the analysis of a novel data processing approach to estimate the local depth in a shallow coastal area starting from data collected by an in situ X-band radar system. The reconstruction approach is based on the maximization of the normalized scalar product (NSP) between the measured and the theoretical wave dispersion relation, which embeds the dependence on the searched for parameter (local depth). The use of NSP approach allows obtaining a high-resolution spatial map of the investigated area, and a thorough statistical analysis is carried out by comparing the local depth estimation results with the ground-truth data made available by a multi-beam echo-sounder survey. In addition, the improved accuracy of the NSP approach is demonstrated with respect to other methods previously used for the analysis of the same area.

This letter deals with the investigation of the performance of the REMOCEAN wave-radar system for sea state monitoring, with a specific focus to the navigation purposes. In particular, we present the results of the processing of data collected by an X-band wave-radar installed on the Cruise Roma, during the voyage from Civitavecchia (Italy) to Barcelona (Spain) and return, on 9th and 10th March, 2012. The effectiveness of the REMOCEAN data processing is analyzed by comparing the estimated sea state parameters with the expectation provided by the operational forecasting runs of the WaveWatchIII spectral modeling.

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.

This work deals with the analysis of a novel data processing approach to estimate the local depth in a shallow coastal area starting from X-band radar data. The approach is based on the maximization of the Normalized Scalar Product (NSP) between the measured and the theoretical wave dispersion relations, which embed the dependence on the searched for parameters (current vector and depth). In the present work, we presented an improvement version of NSP approach able to estimate the local depth, which allows to obtain a high resolution spatial map (subpixel) of the investigated area. The performances of the NSP approach is evaluated by comparing the estimated bathymetry with the ground truth provided the multi-beam echo sounder reported on the same grid resolution of the bathymetric maps of the X-band radar images.

REMOCEAN is a remote sensing system for the wave and current monitoring, based on a nautical X-Band radar, usually deployed for navigation and ship traffic control. Here, we present the results obtained by the analysis of the data collected by the Remocean system, installed in the port of Giglio Island, during the storm of the 27 November 2012. For this test case, the effectiveness of the Remocean wave height reconstruction has been evaluated by analyzing the spectral slope and the statistical properties of the ocean waves and comparing them with independent measures provided by a buoy installed in the proximity the `Costa Concordia' shipwreck.

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.

This work presents the experimental validation of a novel data processing approach to estimate the local depth in a shallow coastal area starting from X-band radar data. The approach is based on the maximization of the Normalized Scalar Product (NSP) between the measured and the theoretical wave dispersion relations, which embed the dependence on the searched for parameters (current vector and depth). The use of NSP approach allows obtaining a high resolution spatial map of the investigated area and a thorough statistical analysis of this approach is carried out by comparison with the ground truth data collected by a multibeam echo-sounder. Finally, the accuracy of the NSP approach is compared with the one achieved by other methods and prove to provide better results.

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.

This letter deals with the use of the wave-radar REMOCEAN system for sea-state monitoring starting from images collected in the X-band at two different test sites. In particular, the measurement surveys were carried out at two coastal sites in the Gulf of Naples by means of the installation of the radar on a fixed and on a movable platform, respectively. The effectiveness of the system was also tested by means of a comparison between the REMOCEAN results and the high-frequency coastal radar observations, with emphasis to the sea surface current estimation.

High-resolution radar imaging systems employ wideband signals and large array apertures, resulting in the generation of large amounts of data. This renders both data acquisition and processing challenging. In this letter, we propose a hybrid approach for a stepped-frequency imaging radar system, which is based on the compressive sensing and novel concepts of microwave tomography, to establish a reduced-redundancy spatial and frequency measurement configuration. The proposed approach provides clear advantages in terms of measurement time and algorithm complexity while maintaining the quality of the scene reconstructions. Laboratory experiments are used to validate the hybrid strategy.

Radio-frequency (RF) tomography is extended for imaging underground structures and tunnels assuming rough terrain. The theory of RF tomography described in an earlier paper remains applicable, provided that a numerical Green's function is computed. An FFT-based and intrinsically parallel method for obtaining numerical Green's functions is described. This method is corroborated with explicit formulas and implemented for RF tomography. Simulated data computed using a finite-difference time-domain code are used to demonstrate performance.

In this letter, we propose a system for through-wall imaging based on a bistatic setup and a two-step inversion procedure. Since the adopted configuration is simple and low cost but only delivers a limited amount of data, the data-processing stage is tackled in a stepwise fashion. First, the targets are detected from the estimation of the surface impedance along the wall and then the area behind the identified portion of the wall is imaged by means of a linearized microwave-tomographic algorithm. The overall device is described, and the results of a proof-of-concept experimental validation are reported.

This letter presents experimental verification of a tomographic through-the-wall radar imaging technique by exploiting real data measurements of 3-D scenes carried out under semicontrolled conditions. More specifically, the 3-D reconstructions are obtained by a linear inverse scattering algorithm and a sliced approach. This strategy ensures fast data processing and allows quick investigation of a very large spatial region. The effectiveness of the imaging scheme is shown for both a single-target scene and a scene with multiple targets at different depths.

We used advanced ground-penetrating radar (GPR) inversion techniques for detecting landmines in laboratory conditions. The radar data were acquired with a calibrated vector network analyzer combined with an off-ground monostatic horn antenna, thereby setting up a stepped-frequency continuous-wave radar. Major antenna effects and interactions with the soil and targets were filtered out using frequency-dependent complex antenna transfer functions. The proposed strategy first exploits inversion approaches that are able to give an accurate characterization of the antenna-soil interaction and a reliable estimate of the soil permittivity. The outcomes of this first phase are at the basis of the application of a microwave tomographic approach based on the Born approximation to achieve the imaging of the subsurface. The algorithms were applied for imaging three landmines of different sizes and buried at different depths in sand. Although the radar system was off the ground, the results showed that it was possible to reconstruct all mines, including a shallow plastic mine as small as 5.6 cm in diameter. This last mine was invisible in the raw radar data, and the use of common GPR imaging techniques did not lead to satisfactory results. The proposed integrated method shows great promise for shallow subsurface imaging in a demining context, particularly because it automatically provides accurate information on the shallow soil dielectric permittivity.

A through-wall-imaging problem where the scatterers are located beyond a 1-D nonhomogeneous unknown wall is considered. As is well known, as part of the propagation takes place within the wall, the knowledge of its electromagnetic properties is mandatory in order to obtain properly focused images. Accordingly, this paper's focus is on the wall estimation problem. To this end, a simple procedure to estimate the wall transmission coefficient is introduced and validated by synthetic data. The proposed estimation procedure works for a symmetric lossless wall and does not rely on optimization schemes but requires a multistatic configuration. As a proof of the principle, we address the problem for a 2-D scalar configuration. Moreover, the role of obscured scatterers on the estimation procedure is discussed, and a procedure for mitigating their effect is proposed as well. Finally, the procedure is also checked against a wall with losses.

This letter deals with the sea state monitoring starting from marine radar images in the X-band. For such a topic, one of the key factors affecting the reliability of reconstruction procedure is the determination of the equivalent surface current that also accounts for the velocity of a moving ship. In this letter, we propose a method to evaluate the surface current, particularly for large values. The reliability of the proposed procedure is shown by a numerical analysis with synthetic data. Subsequently, we present some preliminary results with experimental data collected by a radar on a moving ship.

Three extensions to radio-frequency (RF) tomography for imaging of voids under wide areas of regard are presented. These extensions are motivated by three challenges. One challenge is the lateral wave, which propagates in proximity of the air–earth interface and represents the predominant radiation mechanism for wide-area surveillance, sensing of denied terrain, or close-in sensing. A second challenge is the direct-path coupling between transmitters (Txs) and receivers (Rxs), that affects the measurements. A third challenge is the generation of clutter by the unknown distribution of anomalies embedded in the ground. These challenges are addressed and solved using the following strategies: 1) A forward model for RF tomography that accounts for lateral waves expressed in closed form (for fast computation); 2) a strategy that reduces the direct-path coupling between any Tx–Rx pair; and 3) an improved inversion scheme that is robust with respect to noise, clutter, and high attenuation. A finite-difference time domain simulation of a scenario representing close-in sensing of a denied area is performed, and reconstructed images obtained using the improved and the classical models of RF tomography are compared.

Radio frequency (RF) tomography is proposed to detect underground voids, such as tunnels or caches, over relatively wide areas of regard. The RF tomography approach requires a set of low-cost transmitters and receivers arbitrarily deployed on the surface of the ground or slightly buried. Using the principles of inverse scattering and diffraction tomography, a simplified theory for below-ground imaging is developed. In this paper, the principles and motivations in support of RF tomography are introduced. Furthermore, several inversion schemes based on arbitrarily deployed sensors are devised. Then, limitations to performance and system considerations are discussed. Finally, the effectiveness of RF tomography is demonstrated by presenting images reconstructed via the processing of synthetic data.

The problem of imaging 3-D perfect electric conducting objects from scattered field measurements is addressed. Plane waves at a fixed angle of incidence and varying frequency provide the illuminating radiation whereas the scattered field is collected in far-field zone. The physical optics approximation is adopted to simplify the scattering model and the scatterers' shapes (i.e., their contour surfaces) are described as the support of ¿ distributions. This leads to a linear integral relationship between the scattered field and the unknown distributions,the linear distributional model, which is inverted by employing its singular value decomposition. Emphasis is placed on the study of the dyadic operator to be inverted which links the vector unknown to the scattered field vector and on the role of the polarization diversity. In particular, three different approaches are presented and compared from the computational as well as the resolution point of view. The analysis is performed numerically by means of synthetic data generated by a finite-element-method-based forward solver.

The identification of the physical nature of an object or target causing a ground-penetrating radar (GPR) anomaly, as well as the estimation of a target's dimensions and geometry, is rather challenging. To improve target identification, basic studies are still required, and they can be addressed primarily using a laboratory- or field-based physical model. The field model (test site) is usually expensive and difficult to build, but it provides data for controlled target properties and geometry from a natural environment that are essential for testing processing techniques. In this paper, we present the results from a field experiment where GPR data were collected on plastic and metallic pipes. The main objective is the comparison of the classical migration technique with a microwave tomography approach for reconstructing the geometrical target properties. The use of the microwave tomography approach will allow us to obtain more focused and stable images of the buried objects compared to the ones obtained using classical migration techniques.

Athrough-wallimagingproblem for a 3-D geometry is considered. Scatterers are located beyond a wall represented by a dielectric slab whose features are unknown or known with some degree of uncertainty. A two-step imaging procedure is presented. First, the thickness and the dielectric permittivity of the wall are estimated by a simple procedure which takes into account that actual measurements concern the total scattered field (i.e., the field reflected by the wall plus the one scattered by the obscured scatterers). Then, the problem is cast as a linear inverse scattering problem and solved by means of a truncated-singular value decomposition algorithm. In particular, a 2-D sliced approach is employed to obtain the 3-D scene. Numerical examples are shown to assess the effectiveness of the reconstruction procedure.

This paper deals with the effect of the background removal procedure on ground-penetrating radar data in the framework of a Born-based inverse-scattering solution approach for a scalar 2-D geometry. The theoretical investigation is performed by resorting to diffraction tomography reasoning, and then it is numerically confirmed thanks to the singular value decomposition of the operator that connects the unknown contrast function to the scattered field data achieved under background removal. Numerical and experimental tests are presented.

A shape reconstruction algorithm is formulated for the multimonostatic configuration and the 2-D geometry. The imaging algorithm is based on the Kirchhoff approximation, works in the frequency domain, and exploits the singular value decomposition tool to achieve a stable solution. The effectiveness of the reconstruction algorithm is shown by processing synthetic data in the time domain generated via a finite-difference time-domain code. A performance analysis of the solution algorithm is addressed with varying host medium and measurement configurations, also by processing synthetic data for a 3-D geometry. Finally, an experimental validation of the technique is performed due to data collected by a time-domain ground-penetrating radar for buried pipe detection and localization.

A through-wall imaging problem for a 2-D scalar geometry is addressed. It is cast as an inverse scattering problem and tackled under the linear Born model by means of the truncated singular value decomposition inversion scheme. A multiarray-based inversion strategy is considered. In particular, first the data collected by each single array are processed to obtain different tomographic images of the same scene under test. Then, the different images are suitably combined to obtain the overall image. The inversion scheme is tested for the challenging case of objects located within a complex environment resembling a room in a building.

Multi-Dimensional (MultiD) SAR imaging is a modern technique, based on coherent SAR data combination, aimed to space (full-3D) and space deformation-velocity (4D) analysis. It extends the concept of SAR interferometry and differential interferometry and offers new options for the analysis and monitoring of ground scenes. With this regard, we discuss the current status and the results obtained by processing ERS real data, we investigate perspectives related to the next generation multi-static satellite formations, and we show some sample results regarding 3D and 4D theoretical performance bounds.

A two dimensional shape reconstruction algorithm is applied to experimental GPR in-situ measurement data. The reconstruction algorithm is based on the Kirchhoff approximation and is exploited to process data collected under a multimonostatic configuration by a pulsed GPR. Thus the need to pass from time domain measurements to frequency domain data suitable for the inversion algorithm is also addressed.

A through-wall imaging problem for a 2-D scalar geometry is addressed. It is cast as an inverse scattering problem and tackled under the linear model of the electromagnetic scattering that is provided by the Born approximation. A truncated singular value decomposition inversion scheme is exploited, and the performances that are achievable by such an inversion scheme are assessed by exploiting synthetic data. The cases of weakly and strongly scattering objects are both considered. Finally, an example of reconstruction that is obtained by exploiting experimental data is presented.

This letter deals with the problem of imaging 3-D strong scatterers by means of a 2-D slice tomographic reconstruction algorithm. More in detail, the inversion algorithm is based on two steps. In the first step, 2-D slices of the object are reconstructed; then, they are superimposed and interpolated to achieve the 3-D representation of the object. The Kirchhoff approximation is exploited to develop a linear inversion scheme for reconstructing the 2-D slices for scatterers embedded in the free space. Finally, the inversion algorithm is checked against synthetic data for the interesting case of scatterers resembling the human body.

The localization of a slab embedded within a homogeneous half-space is dealt with. A multifrequency plane wave incidence is assumed, and the locations of the slab interfaces are represented as the support of "distributions." A simplified model of the electromagnetic scattering is adopted, and the problem is cast as the inversion of a linear integral relationship. This inversion is regularized by means of the singular value decomposition tool. Numerical tests allow to analyze the reconstruction capabilities of the reconstruction algorithm, i.e., the maximum depth of investigation and the resolution limits, which depend on the properties of the investigated medium and on the exploited work frequency band. Finally, experimental results are shown.

In this paper, a 1-D inverse-scattering problem laying within the framework of through-wall imaging is addressed. In particular, the problem of localizing the interfaces of a slab hidden behind an obstacle, another slab whose electromagnetic features and thickness are known, is considered. To this end, an approximate linear mathematical relationship between the scattered field and the unknown slab-interface positions is stated. Such an approximate relationship arises from neglecting the multiple-reflections between the two unknown slab's interfaces and between the slab and the obstacle. The unknown locations of the slab's interfaces are represented as the support of Dirac-delta functions, and the problem is cast as the inversion of a linear integral operator whose inversion is achieved by means of the Truncated-Singular-Value-Decomposition (TSVD) inversion scheme. The effect of the parameters of the obstacle on the inversion algorithm and the performances achievable by the solution approach are assessed by exploiting synthetic data. Furthermore, a comparison with the reconstructions obtained under the Born approximation and with the time-backscattered field is achieved. Finally, results obtained by employing experimental data collected owing to a stepped-frequency ground-penetrating radar system are also presented.

This paper deals with the experimental validation of an algorithm, based on the Kirchhoff approximation, for the shape reconstruction of conducting objects from scattered field data. Measured data are collected in a controlled environment under a reflection mode with a finite observation domain and multiview/multistatic/multifrequency configuration. The results show the effectiveness of the approach which takes into account the view diversity by a simple strategy and a threshold procedure.

The problem of reconstructing the shape of buried metallic objects for multifrequency plane wave incidence in a two-dimensional geometry is dealt with under the Kirchhoff approximation and solved by means of the singular value decomposition approach. Numerical results show the effectiveness of the solution method and outline the role of the parameters of the measurement configuration and of the properties of the ground on the features of the reconstruction.

This paper deals with the problem of determining the shape of unknown scattering voids embedded in a homogeneous half-space from the measurements of the magnetic scattered field over a measurement domain parallel to the air-soil interface. The formulation accommodates the distributional nature of the induced surface equivalent current densities. Thus, as unknown, a single layer distribution whose support accounts for the boundary of the scatterers is chosen and is reconstructed by a singular value decomposition based algorithm. Numerical examples resulting from the application of such algorithm are presented.

The problem of determining the shape of buried objects for multifrequency plane wave incidence in a 2D geometry is dealt with. The problem is tackled by searching for the support of the currents induced on the contour of the objects, is formulated as the inversion of the linear operator arising from the application of the Kirchhoff approximation and is solved by means of the Singular Value Decomposition approach. The presented numerical results show the effectiveness of the proposed inversion algorithm.

Deals with the use of multipass synthetic aperture radar (SAR) data in order to achieve three-dimensional tomography reconstruction in presence of volumetric scattering. Starting from azimuth- and range-focused SAR data relative to the same area, neglecting any mutual interaction between the targets, and assuming the propagation in homogeneous media, we investigate the possibility to focus the data also in the elevation direction. The problem is formulated in the framework of linear inverse problem and the solution makes use of the singular value decomposition of the relevant operator. This allows us to properly take into account nonuniform orbit separation and to exploit a priori knowledge regarding the size of the volume interested by the scattering mechanism, thus leading to superresolution in the elevation direction. Results obtained on simulated data demonstrate the feasibility of the proposed processing technique.

The problem of determining the dielectric permittivity profile of buried objects starting from the knowledge of the scattered field is considered in the two-dimensional geometry when incomplete near-zone data are collected at a single frequency under a multiview/multistatic measurement configuration. In particular, attention is paid to the practical issues of the truncated observation domain and the presence of uncertainties on data. The problem is tackled with reference to the scalar polarization by linearization of the mathematical relationship between the unknown dielectric permittivity profile and the scattered field. A homogeneous, possibly lossy, half-space geometry for the subsurface modeling is adopted, thus leading to the so-called distorted Born approximation (DBA). A thorough investigation of the class of unknown functions that can be reliably retrieved is performed by dealing with singular value decomposition of the relevant linear operator. It results that even if sources and receivers are located at the interface, a very restricted set of profile variations can be reconstructed by a stable inversion algorithm. In particular, reduced vertical features of the buried objects with respect to the horizontal ones can be reconstructed under DBA. Moreover, the truncation of the observation domain further restricts this set, affecting mainly the vertical resolution. Numerical results confirming the validity of the analysis are also provided.

In the area of tomographic synthetic aperture radar processing we present a new technique that makes use of the singular value decomposition method to improve the resolution limits by including a-priori information about the radiation penetration depth.

The effectiveness of a linear inverse scattering algorithm for the shape reconstruction of perfectly conducting objects is experimentally validated by processing measured data. The data are collected via an automatic system for free-space measurements under reflection mode geometry. The amplitude and phase of the scattered field is measured in a multistatic and multifrequency configuration for different locations (views) of the transmitting antenna. The reliability of the measurement setup is shown by a comparison with simulated data. No a priori knowledge of the shape of the scatterers is assumed. The results of inversions of experimental data collected under single-view illumination agree well with those of inversions of synthetic data, so showing the robustness of the algorithm.

Deals with the reconstruction of the contrast function of a dielectric cylinder with rectangular cross section starting from the knowledge of the electric scattered far field produced under the incidence of plane waves. We analyze the set of the reconstructable Fourier harmonics of the unknown permittivity contrast function with linear and quadratic approaches. This set depends on the ranges of the wavenumbers /spl beta/, of the angles of incidence /spl theta//sub i/ of the impinging plane waves, and of the observation angles /spl theta//sub o/. We discuss a simple way to describe such a dependence, which allows us to find out that the set of the retrievable harmonics for the quadratic approach contains that for the linear one. Moreover, our investigation points out how increasing the amount of independent data through a multifrequency/multiview measurement scheme allows us to enlarge the set of the retrievable unknown harmonics with respect to a multifrequency/single-view one. Our analysis is confirmed by numerical results. Memory storage requirements and processing time consumption for the quadratic approach are greatly reduced thanks to the massive use of the fast Fourier transform algorithm.