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A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna. [ABSTRACT FROM AUTHOR]

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. [ABSTRACT FROM AUTHOR]

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes. [ABSTRACT FROM AUTHOR]

Featured Application: Object detection method based on three-dimensional GPR data of railroad tracks using AI, trained solely on simulation data. Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface hydrological dynamics, including lateral flow and soil water distribution. However, during TL-GPR surveys, field conditions often create discrepancies in surface geometry, which introduces mismatches across sequential TL-GPR images. These discrepancies may generate spurious signal variations that impede the accurate interpretation of TL-GPR data when assessing subsurface hydrological processes. In responding to this issue, this study introduces a TL-GPR image alignment method by employing the dynamic time warping (DTW) algorithm. The purpose of the proposed method, namely TLIAM–DTW, is to correct for geometric mismatch in TL-GPR images collected from the identical survey line in the field. We validated the efficacy of the TLIAM–DTW method using both synthetic data from gprMax V3.0 simulations and actual field data collected from a hilly, forested area post-infiltration experiment. Analyses of the aligned TL-GPR images revealed that the TLIAM–DTW method effectively eliminates the influence of geometric mismatch while preserving the integrity of signal variations due to actual subsurface hydrological processes. Quantitative assessments of the proposed methods, measured by mean absolute error (MAE) and root mean square error (RMSE), showed significant improvements. After performing the TLIAM–DTW method, the MAE and RMSE between processed TL-GPR images and background images were reduced by 96% and 78%, respectively, in simple simulation scenarios; in more complex simulations, MAE declined by 27–31% and RMSE by 17–43%. Field data yielded reductions in MAE and RMSE of >82% and 69%, respectively. With these substantial improvements, the processed TL-GPR images successfully depict the spatial and temporal transitions associated with subsurface lateral flows, thereby enhancing the accuracy of monitoring subsurface hydrological processes under field conditions. [ABSTRACT FROM AUTHOR]

Ice thickness has a significant effect on the physical and biogeochemical processes of a lake, and it is an integral focus of research in the field of ice engineering. The Qinghai–Tibetan Plateau, known as the Third Pole of the world, contains numerous lakes. Compared with some information, such as the area, water level, and ice phenology of its lakes, the ice thickness of these lakes remains poorly understood. In this study, we used an unmanned aerial vehicle (UAV) with a 400/900 MHz ice-penetrating radar to detect the ice thickness of Qinghai Lake and Gahai Lake. Two observation fields were established on the western side of Qinghai Lake and Gahai Lake in January 2019 and January 2021, respectively. Based on the in situ ice thickness and the propagation time of the radar, the accuracy of the ice thickness measurements of these two non-freshwater lakes was comprehensively assessed. The results indicate that pre-processed echo images from the UAV-borne ice-penetrating radar identified non-freshwater lake ice, and we were thus able to accurately calculate the propagation time of radar waves through the ice. The average dielectric constants of Qinghai Lake and Gahai Lake were 4.3 and 4.6, respectively. This means that the speed of the radar waves that propagated through the ice of the non-freshwater lake was lower than that of the radio waves that propagated through the freshwater lake. The antenna frequency of the radar also had an impact on the accuracy of ice thickness modeling. The RMSEs were 0.034 m using the 400 MHz radar and 0.010 m using the 900 MHz radar. The radar with a higher antenna frequency was shown to provide greater accuracy in ice thickness monitoring, but the control of the UAV's altitude and speed should be addressed. [ABSTRACT FROM AUTHOR]

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China's first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar. [ABSTRACT FROM AUTHOR]

The planet Mars is the most probable among the terrestrial planets in our solar system to support human settlement or colonization in the future. The detection of water ice or liquid water on the shallow subsurface of Mars is a crucial scientific objective for both the Chinese Tianwen-1 and United States Mars 2020 missions, which were launched in 2020. Both missions were equipped with Rover-mounted ground-penetrating radar (GPR) instruments, specifically the RoPeR on the Zhurong rover and the RIMFAX radar on the Perseverance rover. The in situ radar provides unprecedented opportunities to study the distribution of shallow subsurface water ice on Mars with its unique penetrating capability. The presence of water ice on the shallow surface layers of Mars is one of the most significant indicators of habitability on the extraterrestrial planet. A considerable amount of evidence pointing to the existence of water ice on Mars has been gathered by previous researchers through remote sensing photography, radar, measurements by gamma ray spectroscopy and neutron spectrometers, soil analysis, etc. This paper aims to review the various approaches utilized in detecting shallow subsurface water ice on Mars to date and to sort out the past and current evidence for its presence. This paper also provides a comprehensive overview of the possible clues of shallow subsurface water ice in the landing area of the Perseverance rover, serving as a reference for the RIMFAX radar to detect water ice on Mars in the future. Finally, this paper proposes the future emphasis and direction of rover-mounted radar for water ice exploration on the Martian shallow subsurface. [ABSTRACT FROM AUTHOR]

Planetary radar observations have provided invaluable information on the solar system through both ground-based and space-based observations. In this overview article, we summarize how radar observations have contributed in planetary science, how the radar technology as a remote-sensing method for planetary exploration and the methods to interpret the radar data have advanced in the eight decades of increasing use, where the field stands in the early 2020s, and what are the future prospects of the ground-based facilities conducting planetary radar observations and the planned spacecraft missions equipped with radar instruments. The focus of the paper is on radar as a remote-sensing technique using radar instruments in spacecraft orbiting planetary objects and in Earth-based radio telescopes, whereas ground-penetrating radar systems on landers are mentioned only briefly. The key scientific developments are focused on the search for water ice in the subsurface of the Moon, which could be an invaluable in situ resource for crewed missions, dynamical and physical characterization of near-Earth asteroids, which is also crucial for effective planetary defense, and a better understanding of planetary geology. [ABSTRACT FROM AUTHOR]

In classical radar imaging, such as in Earth remote sensing, electromagnetic waves are usually assumed to propagate in free space. However, in numerous applications, such as ground penetrating radar or non-destructive testing, this assumption no longer holds. When there is a multi-material background, the subsurface image reconstruction becomes considerably more complex. Imaging can be performed in the spatial domain or, equivalently, in the wavenumber domain (k-space). In subsurface imaging, to date, objects with a non-planar surface are commonly reconstructed in the spatial domain, by the Backprojection algorithm combined with ray tracing, which is computationally demanding. On the other hand, objects with a planar surface can be reconstructed more efficiently in k-space. However, many non-planar surfaces are partly planar. Therefore, in this paper, a novel concept is introduced that makes use of the efficient k-space-based reconstruction algorithms for partly planar scenarios, too. The proposed algorithm forms an image from superposing sub-images where as many image parts as possible are reconstructed in the wavenumber domain, and only as many as necessary are reconstructed in the spatial domain. For this, a segmentation scheme is developed to determine which parts of the image volume can be reconstructed in the wavenumber domain. The novel concept is verified by measurements, both from monostatic synthetic aperture radar data and multiple-input–multiple-output radar data. It is shown that the computational efficiency for imaging irregularly shaped geometries can be significantly augmented when applying the proposed concept. [ABSTRACT FROM AUTHOR]

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques. [ABSTRACT FROM AUTHOR]

China accomplished a historic milestone in 2020 when the mission Chang'e-5 (CE-5) to the Lunar's surface was successfully launched. An extraordinary component of this mission is the "Lunar Regolith Penetrating Radar" (LRPR) housed within its lander, which currently stands as the most advanced payload in terms of vertical resolution among all penetrating radars employed in lunar exploration. This provides an unprecedented opportunity for high-precision research into the interior structure of the shallow lunar regolith. Previous studies have achieved fruitful research results based on the data from LRPR, updating our perception of the shallow-level regolith of the Moon. This paper provides an overview of the new advancements achieved by the LRPR in observing the basic structure of the shallow regolith of the Moon. It places special emphasis on the role played by the LRPR in revealing details about the shallow lunar regolith's structure, its estimated dielectric properties, the provenance of the regolith materials from the landing area, and its interpretation of the geological stratification at the landing site. Lastly, it envisions the application and developmental trends of in situ radar technology in future lunar exploration. [ABSTRACT FROM AUTHOR]

China has successfully carried out five lunar exploration missions since 2007. These missions indicate that China has successfully implemented a three-step lunar exploration program of "orbiting, landing, and returning". Among them, the Lunar Penetrating Radar (LPR) carried by the Yutu-2 rover in the Chang'E-4 (CE-4) mission is the only one still operating on the far side of the Moon. Up to now, the Yutu-2 radar has measured a large amount of scientific data, and its observations are of great significance to human cognition of the geological evolution of the lunar surface and the exploration of possible lunar in situ resources. This paper reviews the scientific results obtained by previous researchers based on the radar exploration data of Yutu-2, focusing mainly on three aspects, e.g., the geological structure of the shallow surface at the CE-4 landing site, the dielectric properties of the shallow subsurface materials and the special geological features. Finally, the prospects of Yutu-2 radar research priorities and future exploration, and the application trend of Moon-based ground-penetrating radar are given. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel low-complexity hand gesture recognition framework via a multiple Frequency Modulation Continuous Wave (FMCW) radar-based sensing system. In this considered system, two radars are deployed distributively to acquire motion vectors from different observation perspectives. We first independently extract reflection points of the interested target from different radars by applying the proposed neighboring reflection points detection method after processing the traditional 2-dimensional Fast Fourier Transform (2D-FFT). The obtained sufficient corresponding information of detected reflection points, e.g., distances, velocities, and angle information, can be exploited to synthesize motion velocity vectors to achieve a high signal-to-noise ratio (SNR) performance, which does not require knowledge of the relative position of the two radars. Furthermore, we utilize a long short-term memory (LSTM) network as well as the synthesized motion velocity vectors to classify different gestures, which can achieve a significantly high accuracy of gesture recognition with a 1600-sample data set, e.g., 98.0 % . The experimental results also illustrate the robustness of the proposed gesture recognition systems, e.g., changing the environment background and adding new gesture performers. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a wide variety of radar imaging modes, such as nadir-looking B-scan, or side-looking synthetic aperture radar tomographic acquisitions, performed in both back- and forward-scattering geometries, for the inspection and characterization of roadways. Nadir-looking B-scan corresponds to a low-complexity mode exploiting the direct return from the response, whereas side-looking configurations allow the utilization of angular and polarimetric diversity in order to analyze advanced features. The main objective of this paper is to evaluate the ability of each configuration, independently of aspects related to operational implementation, to discriminate and localize shallow underground defects in the wearing course of roadways, and to estimate key geophysical parameters, such as roughness and dielectric permittivity. Campaign measurements are conducted using short-range radar stepped-frequency continuous-waveform (SFCW) devices operated in the C and X bands, at the pavement fatigue carousel of Université Gustave Eiffel, over debonded areas with artificial defects. The results indicate the great potential of the newly proposed forward-scattering tomographic configuration for detecting slight defects and characterizing roadways. Case studies, performed in the presence of narrow horizontal heterogeneities which cannot be detected using classical B-scan, show that both the coherent integration along an aperture using the back-projection algorithm, and the exploitation of scattering mechanisms specific to the forward-looking bistatic geometry, allows anomalous echoes to be detected and further characterized, confirming the efficacy of radar imaging techniques in such applications. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has been extensively utilized in deep-space exploration. However, GPR modeling commonly employs simplified antenna models and carrier-free impulse signals, resulting in reduced accuracy and interpretability. In this paper, we addressed these limitations by combining a tilted monopole antenna and linear frequency modulation continuous wave (LFMCW) to simulate real conditions. Additionally, a radiation-pattern-compensation back-propagation (RPC-BP) algorithm was developed to improve the illumination of the right-inclined structure. We first introduced the LFMCW used by the Mars Rover Penetrating Radar (RoPeR) onboard the Zhurong rover, where frequencies range from 15 to 95 MHz. Although the LFMCW signal improves radiation efficiency, it increases data processing complexity. Then, the radiation patterns and response of the tilted monopole antenna were analyzed, where the radiated signal amplitude varies with frequency. Finally, a series of numerical and laboratory experiments were conducted to interpret the real RoPeR data. The results indicate that hyperbolic echoes tilt in the opposite direction of the survey direction. This study demonstrates that forward modeling considering real transmit signals and complex antenna models can improve modeling accuracy and prevent misleading interpretations on deep-space exploration missions. Moreover, the migration process can improve imaging quality by considering radiation pattern compensation. [ABSTRACT FROM AUTHOR]

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

The detection of lava tubes beneath the surfaces of the Moon and Mars has been a popular research topic and challenge in planetary radar observation. In recent years, the Moon–based ground penetrating radar (GPR) carried by the Chinese Chang'e–3/–4 mission, the RIMFAX radar carried by the Mars mission Perseverance, and the RoSPR radar and MOSIR radar carried by China's Tianwen–1 orbiter have extensively promoted the exploration of the underground space of extraterrestrial bodies, which is crucial for the future utilization and development of these spaces. This paper expounds on the principles, methods, and detection results of using GPR to detect lava tubes on the Moon and Mars. First, lava tubes' formation mechanism and morphological characteristics are outlined, followed by an introduction to GPR's working principles and classification. The advantages, disadvantages, and prospects of different types of radar in detecting the lava tubes are analyzed. Finally, the distribution of lava tubes on the Moon and Mars is briefly summarized, and the potential utilization of lava tubes is discussed. We believe that the GPR technique is an effective geophysical method for exploring the underground structures of the Moon and Mars, and the lava tubes beneath the surface of extraterrestrial bodies can provide important references for selecting future Moon and Mars bases. [ABSTRACT FROM AUTHOR]

For the investigation of moisture and salt content in historic masonry, destructive drilling samples followed by a gravimetric investigation is still the preferred method. In order to prevent the destructive intrusion into the building substance and to enable a large-area measurement, a nondestructive and easy-to-use measuring principle is needed. Previous systems for moisture measurement usually fail due to a strong dependence on contained salts. In this work, a ground penetrating radar (GPR) system was used to determine the frequency-dependent complex permittivity in the range between 1 and 3 GHz on salt-loaded samples of historical building materials. By choosing this frequency range, it was possible to determine the moisture in the samples independently of the salt content. In addition, it was possible to make a quantitative statement about the salt level. The applied method demonstrates that with ground penetrating radar measurements in the frequency range selected here, a salt-independent moisture determination can be carried out. [ABSTRACT FROM AUTHOR]

The detection and quantification of fractures in rocks, as well as the detection of lithological changes, are of particular interest in scientific fields, such as construction materials, geotechnics, reservoirs and the diagnostics of dielectric composite materials and cultural heritage objects. Therefore, different methods and techniques have been developed and improved over the years to provide solutions, e.g., seismic, ground-penetrating radar and X-ray microtomography. However, there are always trade-offs, such as spatial resolution, investigated volume and rock penetration depth. At present, high-frequency radars (>60 GHz) are available on the market, which are compact in size and capable of imaging large areas in short periods of time. However, the few rock applications that have been carried out have not provided any information on whether these radars would be useful for detecting fractures and lithological changes in rocks. Therefore, in this work, we performed different experiments on construction and reservoir rocks using a frequency-modulated continuous wave radar working at 300 GHz to evaluate its viability in this type of application. The results showed that the radar quantified millimeter fractures at a 1 cm rock penetration depth with a sensitivity of 500 μm. Furthermore, lithological changes were identified, even when detecting interfaces generated by the artificial union of two samples from the same rock. [ABSTRACT FROM AUTHOR]

The Rover-mounted Subsurface Penetrating Radar (RoSPR) is one of the scientific payloads onboard China's first independent Mars exploration mission, Tianwen-1. The radar aims to characterize the thickness of the upper Martian soil and investigate the subsurface stratigraphy by collecting and processing the data. This article is mainly divided into two parts, the introduction of data pre-processing and analysis of pre-processed radar signals, aiming at helping scientists make more effective use of radar data. The first part describes the operating principle of the RoSPR and the procedure of radar data pre-processing at all levels. Data pre-processing is mainly designed to transfer the raw data format to a common PDS (Planetary Data System) and eliminate the influence of the instrument. In the signal analysis part, the performances of both self-check signals and echo signals of low- and high-frequency channels are analyzed, which indicate a stable radar system and are useful for background removal. Phase and time calibration is of great importance for improving data quality and making the radar data more accurate. Moreover, further processing is required to obtain clear radar images, such as filtering, background removal and gain setting. [ABSTRACT FROM AUTHOR]

By computer simulation, the main consequences of the "slice" approach to assessing the power of a radar return signal from an atmospheric water cloud were verified. The cloud was modeled as a set of slices, which are thin layers coinciding with the wavefront of the probing radar radiation. The return signal was calculated as the convolution of the probe pulse with the cloud. The simulation was based on the previously derived "slice" radar equation, which determines the received power taking into account the statistical characteristics of fluctuations in the number of slice droplets and their water content. Estimates of the magnitude of the simulated return signal were compared with the theoretical formula for various statistical characteristics of the ensemble of droplets that make up the cloud model. The simulation result confirmed the validity of using the slice approach to interpret radar measurements of water content in clouds. [ABSTRACT FROM AUTHOR]

With the widespread use of automatic identification systems (AISs), some ships use deceptive information or intentionally close their AISs to conceal their illegal activities or evade the supervision of maritime departments. Although radar measurements can be effectively utilized to evaluate the credibility of received AIS data, the propagation of non-line-of-sight (NLOS) signal conditions is an important factor that affects location accuracy. This study addresses the NLOS problem in a special geometric dilution of precision (GDOP) scenario on a coast and several base stations. We employed data augmentation and a deep residual shrinkage network in order to alleviate the adverse effects of NLOS errors. The results of our simulations demonstrate that the proposed method outperforms other range-based localization algorithms in a mixed LOS/NLOS environment. For a special GDOP scenario with four radars, our algorithm's root-mean-square error (RMSE) was lower than 180 m. [ABSTRACT FROM AUTHOR]

This work analyzes the observations from the Lunar Regolith Penetrating Radar (LRPR) onboard Chang'E-5 to reconstruct the subsurface structure of the regolith under the lander at the drilling site. This is the first stationary Ground-Penetrating Radar (GPR) array to operate on the Moon. Imaging results of pre-drilling and post-drilling measurements show that the thickness of local regolith is larger than 2 m. Within the LRPR's detection range, we do not find any continuous layer. Instead, irregular, high-density zones are identified in the regolith. Two of these zones are on the drilling trajectory at ~30 cm and ~70 cm, consistent with the recorded drilling process. We speculate a rock fragment from the deeper, high-density zone obstructed the drill, which led to an early termination of the drilling. Based on our interpretation of subsurface structure, we modeled the LRPR echoes using a finite-difference time-domain method. The same imaging algorithm was also applied to the simulation data. The modeled data verify our inference of the regolith structure under the lander. [ABSTRACT FROM AUTHOR]

Radar imaging from unmanned aerial vehicles (UAVs) is a dynamic research topic attracting huge interest due to its practical fallouts. In this context, this article provides a comprehensive review of the current state of the art and challenges related to UAV-based ground-penetrating radar (GPR) imaging systems. First, a description of the available prototypes is provided in terms of radar technology, UAV platforms, and navigation control devices. Afterward, the paper addresses the main issues affecting the performance of UAV-based GPR imaging systems. such as the control of the UAV platform during the flight to collect high-quality data, the necessity to provide accurate platform position information in terms of probing wavelength, and the mitigation of clutter and other electromagnetic disturbances. A description of the major applicative areas for UAV GPR systems is reported with the aim to show their potential. Furthermore, the main signal-processing approaches currently adopted are detailed and two experimental tests are also reported to prove the actual imaging capabilities. Finally, open challenges and future perspectives regarding this promising technology are discussed. [ABSTRACT FROM AUTHOR]

Soil Moisture (SM) is one of the most critical factors for a crop's growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR's output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner. [ABSTRACT FROM AUTHOR]

Currently, ground penetrating radar is the major technology for the detection of urban road collapses and disaster sources. Vehicle-mounted GPR collects tens of GB of data on site every day, but the present interpretation of abnormal regions detected by radar relies on manual interpretation with low process efficiency. The abnormal region images of GPR are different from the surrounding normal images. In terms of the features of abnormal regions in GPR images with an obvious brightness change and obvious directional characteristics, an abnormal region detection algorithm based on visual attention mechanism is proposed. Firstly, the complex background noise in the GPR images is suppressed by wavelet denoising and gamma transform, and the brightness and directional characteristics of the abnormal regions are enhanced. Secondly, by building a multi-scale image brightness and orientation feature pyramid model, the features of abnormal regions of interest are continuously enhanced, and the rapid screening of abnormal regions has been achieved. The effectiveness of the algorithm has been verified by actual tests on different types of abnormal radar image data. [ABSTRACT FROM AUTHOR]

In order to overcome the difficulty in rapid detection for expressway tunnels, the coherence calculation of dual-frequency radar signal in the time domain is proposed to suppress the interference. A dual-frequency (400 MHz and 900 MHz) GPR and a manipulator are developed. In the horizontal direction, it has a −90°~90° range, a vertical rotation range from 0 to 90°, a length range from 5~9.5 m, and an antenna rotation between −40°~40°. The mobile scanning has been realized in the expressway tunnel, taking the vehicle as the mobile carrier. The research shows that: This equipment realizes the rapid detection of the tunnel without affecting the expressway's opening. The imaging algorithm recognizes the structural thickness, reinforcement distribution characteristics, and structural diseases. According to the detection carried out in Zhejiang, China, the spacing between reinforcement in the second lining is 4 cm, and the thickness of the structure is about 0.55 m. However, the reinforcement has deformed badly, and the defects are also discerned in the structure. Compared with the traditional handheld radar detection, the equipment dramatically reduces the labor demand, time, and cost and can meet engineering needs. With the proposed method, the detection time is reduced to 12 min/km from 0.5 d/km, and the cost is reduced by more than two times. Furthermore, during the detection, the traffic can be maintained normally. [ABSTRACT FROM AUTHOR]

The increasing interest in the radar detection of low-elevation and small-size targets in complicated ground environments (such as urban, suburban, and mixed country areas) calls for a precise quantification of the radar detection capabilities in those areas. Hence, a set of procedures is devised and tested, both theoretically and experimentally, using a commercial X-band radar, to (i) calibrate the radar sensor (with an online evaluation of its losses) using standard scatterers, (ii) measure the multipath effect and compensate for it, and (iii) create “true radar cross section” maps of the area of interest for both point and distributed clutter. The above methods and the related field results are aimed at future qualification procedures and practical usage of small, cheap, and easily moveable radars for the detection of low-observable air targets, such as unmanned air vehicles/systems (UAV/UAS), in difficult ground areas. A significant set of experimental results as discussed in the paper confirms the great relevance of multipath in ground-based radar detection, with the need for correcting measures. [ABSTRACT FROM AUTHOR]

A Golay-based ultra wideband ground penetrating for underground pipes location is proposed and experimentally demonstrated. Golay complementary codes with the code length of 1024 and frequency of 1 GHz are used as the probe signals. The two-dimensional image of the buried pipes is achieved by a correlation method and a back-projection algorithm. The experimental results show that both the plastic pipe and metallic pipe can be located with a range resolution of 10 cm. Furthermore, as the Golay complementary sequences are a pair of complementary sequences, the sum of their correlation function yields twice the value of the peak at the target position and zero elsewhere. Thus, compared with the stepped frequency signal radar or chaotic signal radar, the Golay-based radar can significantly improve the signal–noise ratio and has the capability of deep detection. [ABSTRACT FROM AUTHOR]

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy LiDAR (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence (i.e., correlation in the frequency of spatial variation) at multiple spatial scales ≤10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more becoming even vertically as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5-4 m, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing LiDAR and radar technologies to quantitatively couple above- and belowground ecosystem structure. [ABSTRACT FROM AUTHOR]

RADARSAT and ERS-2 data collected at multiple incidence angles are used to characterize the seasonal variations in the backscatter of snow-covered landscapes in the northern Hudson Bay Lowlands during the winters of 1997/98 and 1998/99. The study evaluates the usefulness of C-band SAR systems for retrieving the snow water equivalent under dry snow conditions in the forest-tundra ecotone. The backscatter values are compared against ground measurements at six sampling sites, which are taken to be representative of the land-cover types found in the region. The contribution of dry snow to the radar return is evident when frost penetrates the first 20 cm of soil. Only then does the backscatter respond positively to changes in snow water equivalent, at least in the open and forested areas near the coast, where 1-dB increases in backscatter for each approximate 5-10 mm of accumulated water equivalent are observed at 20-31° incidence angles. Further inland, the backscatter shows either no change or a negative change with snow accumulation, which suggests that the radar signal there is dominated by ground surface scattering (e.g., fen) when not attenuated by vegetation (e.g., forested and transition). With high-frequency ground-penetrating radar, we demonstrate the presence of a 10-20-cm layer of black ice underneath the snow cover, which causes the reduced radar returns (-15 dB and less) observed in the inland fen. A correlation between the backscattering and the snow water equivalent cannot be determined due to insufficient observations at similar incidence angles. To establish a relationship between the snow water equivalent and the backscatter, only images acquired with similar incidence angles should be used, and they must be corrected for both vegetation and ground effects. [ABSTRACT FROM AUTHOR]

Most Ground Penetrating Radars (GPR) cover a wide frequency range by emitting very short time wavelets. In this work, we study in detail the wavelet emitted by two bowtie GPR antennas with nominal frequencies of 800 MHz and 1 GHz. Knowledge of this emitted wavelet allows us to extract as much information as possible from recorded signals, using advanced processing techniques and computer simulations. Following previously published methodology used by Rial et al. [1], which ensures system stability and reliability in data acquisition, a thorough analysis of the wavelet in both time and frequency domain is performed. Most of tests were carried out with air as propagation medium, allowing a proper analysis of the geometrical attenuation factor. Furthermore, we attempt to determine, for each antenna, a time zero in the records to allow us to correctly assign a position to the reflectors detected by the radar. Obtained results indicate that the time zero is not a constant value for the evaluated antennas, but instead depends on the characteristics of the material in contact with the antenna. [ABSTRACT FROM AUTHOR]

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) systems are sensors that are able to acquire underground images by scanning the surface of the soil/pavement under investigation. Usually, a GPR system records its own position along the scan line, using a mechanical odometer, i.e., a rolling wheel in contact with the ground. This simple and cheap solution can be ineffective on uneven terrains. In this paper, a positioning system based on an interferometric radar is presented. This kind of radar is able to detect small displacements of the targets in its field of view. Such a capability was used to track the GPR position along a line. The system was validated with simulations and tested in a realistic experimental scenario. [ABSTRACT FROM AUTHOR]

Continuous Wave (CW) radars systems, especially air-coupled Ground-Penetrating Radar (GPR) or Through-Wall Imaging Radar (TWIR) systems, echo signals reflected from a stationary target with high energy, which may cause receiver saturation. Another effect caused by reflection of stationary targets is noticeable as background within a radargram. Nowadays, radar systems use automatic gain control to prevent receiver saturation. This paper proposes a method to remove stationary targets automatically from the received signal. The method was designed for a radar system with a moving platform, with an assumption that the distance between the surface and target is constant. The design is proposed of an SFCW radar with an integrated system for real-time multiple static target Echo Cancellation (EC). The proposed EC system removes the static target using active Integrated Circuit (IC) components, which generate the corresponding EC signal for each frequency step of the SFCW radar and sum it with the received echo signal. This has the main advantage of removing even multiple echoes at any distance, and excludes the need for a high-dynamic-range receiver. Additionally, the proposed system has minimal impact on the radar size and power consumption. Besides static target removal, the antenna coupling can be removed if the signal appears to be constant. The operating frequency was selected between 500 MHz and 2.5 GHz, due to the limitation of the used electronic components. The experimental results show that the simulated target's echo using a cable with a known length could be suppressed to up to 38 dB. Experimental results using a moving radar platform and the real environment scenario with static and dynamic targets, show that the proposed EC system could achieve up to 20 dB attenuation of the static target. The system does not affect any other target of interest, which can even move at any distance during the measurement. Therefore, this could be a promising method for further compact implementation into SFCW radars, or any other radar type that generates CW single frequencies. [ABSTRACT FROM AUTHOR]

This paper focuses on high-resolution coastal stratigraphy data, which were revealed by the Ground Penetrating Radar (GPR) system. Surveys performed with GPR on the surface of prograded barriers reveal patterns of reflections that allow the interpretation of the geometry and stratigraphy of coastal deposits in a continuous mode. At the Curumim prograded barrier in southern Brazil (29°30′ S–49°53′ W), a two-dimensional transverse GPR survey revealed, with high precision, a serial of contacts between aeolian deposits of relict foredunes and relict beach deposits that have a strong correlation with sea level. In a 4 km GPR profile, a total of 24 of these contacts were identified. The high accurate spatial positioning of the contacts combined with Optical Stimulated Luminescence dating resulted in the first confident sea-level curve that tells the history of sea-level changes during the last 7 ka on the southernmost sector of the Brazilian coast. The curve shows that sea-level was still rising before 6 ka BP, with a maximum level of 1.9 m reached close to 5 ka BP; after that, sea-level started to falling slowly until around 4 ka BP when fall accelerated. [ABSTRACT FROM AUTHOR]

Inspections of engineered structures below water level are essential to ensure the long-term serviceability of bridge infrastructure and to avoid major damage or failure. This research aimed to investigate integrated geophysical technologies for the underwater inspection of bridge foundation-related scour and erodible scour-based infill. Survey methods focused on Water-Penetrating Radar (WPR), supplemented by sonar. Whilst the survey benefits of the sonar imaging water–sediment interface and structures are well known, those of WPR are not. However, it is ideally suited to the survey of the water base and sub-sediment in shallow (>10 m) freshwater, especially where suspended sediment, weed infestation or methane impede sonar results. Our work produced good WPR imagery acquired from small, manoeuvrable boats that allowed bathymetric profiles to be plotted, as well as the likely locations of soft-sediment scour in future high-water flow events. This study provides clear benefits for integrated sonar and WPR surveys in the quantitative assessment of engineered structures within freshwater. [ABSTRACT FROM AUTHOR]

Automotive millimeter-wave (MMW) radar is essential in autonomous vehicles due to its robustness in all weather conditions. Traditional commercial automotive radars are limited by their resolution, which makes the object classification task difficult. Thus, the concept of a new generation of four-dimensional (4D) imaging radar was proposed. It has high azimuth and elevation resolution and contains Doppler information to produce a high-quality point cloud. In this paper, we propose an object classification network named Radar Transformer. The algorithm takes the attention mechanism as the core and adopts the combination of vector attention and scalar attention to make full use of the spatial information, Doppler information, and reflection intensity information of the radar point cloud to realize the deep fusion of local attention features and global attention features. We generated an imaging radar classification dataset and completed manual annotation. The experimental results show that our proposed method achieved an overall classification accuracy of 94.9%, which is more suitable for processing radar point clouds than the popular deep learning frameworks and shows promising performance. [ABSTRACT FROM AUTHOR]

Wave energy resource assessment is crucial for the development of the marine renewable industry. High-frequency radars (HF radars) have been demonstrated to be a useful wave measuring tool. Therefore, in this work, we evaluated the accuracy of two CODAR Seasonde HF radars for describing the wave energy resource of two offshore areas in the west Galician coast, Spain (Vilán and Silleiro capes). The resulting wave characterization was used to estimate the electricity production of two wave energy converters. Results were validated against wave data from two buoys and two numerical models (SIMAR, (Marine Simulation) and WaveWatch III). The statistical validation revealed that the radar of Silleiro cape significantly overestimates the wave power, mainly due to a large overestimation of the wave energy period. The effect of the radars' data loss during low wave energy periods on the mean wave energy is partially compensated with the overestimation of wave height and energy period. The theoretical electrical energy production of the wave energy converters was also affected by these differences. Energy period estimation was found to be highly conditioned to the unimodal interpretation of the wave spectrum, and it is expected that new releases of the radar software will be able to characterize different sea states independently. [ABSTRACT FROM AUTHOR]

Traditional two-dimensional radar images can only reflect the target azimuth and slant range and thus suffer problems of geometric deformation and overlapping. The unique three-dimensional (3D) imaging capability of ground-based real-aperture radar can more accurately and directly achieve correlation between the radar image and the slope monitoring scenarios, thus providing reliable information for the early warning and forecasting of landslides and collapse disasters. The latest method of selecting a slope target from a high-resolution range profile includes two indexes: maximum amplitude and coherence, which will affect the accuracy of displacement measurement when there is an interference target. We present a three-dimensional slope imaging method based on smoothness constraints. On the basis of the latest method, the objective fact of the practically smooth and continuous distribution of slope surfaces is considered. This method can be used for image interpretation on strongly scattered targets within the slope. The independently developed ground-based real-aperture slope radar system was deployed in the Heidaigou Open-Pit Coal Mine in Inner Mongolia to carry out 3D slope imaging experiments. The effectiveness of this method in slope monitoring and imaging was confirmed by comparing the surface roughness and the spatial positions of the targets with the high-density point cloud data in the projective plane obtained during the same time period. We used RMSE function and roughness as two measures. It shows that the method presented in this paper is more suitable for actual three-dimensional slope imaging. [ABSTRACT FROM AUTHOR]

A non-contact heartbeat/respiratory rate monitoring system was designed using narrow beam millimeter wave radar. Equipped with a special low sidelobe and small-sized antenna lens at the front end of the receiving and transmitting antennas in the 120 GHz band of frequency-modulated continuous-wave (FMCW) system, this sensor system realizes the narrow beam control of radar, reduces the interference caused by the reflection of other objects in the measurement background, improves the signal-to-clutter ratio (SCR) of the intermediate frequency signal (IF), and reduces the complexity of the subsequent signal processing. In order to solve the problem that the accuracy of heart rate is easy to be interfered with by respiratory harmonics, an adaptive notch filter was applied to filter respiratory harmonics. Meanwhile, the heart rate obtained by fast Fourier transform (FFT) was modified by using the ratio of adjacent elements, which helped to improve the accuracy of heart rate detection. The experimental results show that when the monitoring system is 1 m away from the human body, the probability of respiratory rate detection error within ±2 times for eight volunteers can reach 90.48%, and the detection accuracy of the heart rate can reach 90.54%. Finally, short-term heart rate measurement was realized by means of improved empirical mode decomposition and fast independent component analysis algorithm. [ABSTRACT FROM AUTHOR]

The high-frequency channel of lunar penetrating radar (LPR) onboard Yutu-2 rover successfully collected high quality data on the far side of the Moon, which provide a chance for us to detect the shallow subsurface structures and thickness of lunar regolith. However, traditional methods cannot obtain reliable dielectric permittivity model, especially in the presence of high mix between diffractions and reflections, which is essential for understanding and interpreting the composition of lunar subsurface materials. In this paper, we introduce an effective method to construct a reliable velocity model by separating diffractions from reflections and perform focusing analysis using separated diffractions. We first used the plane-wave destruction method to extract weak-energy diffractions interfered by strong reflections, and the LPR data are separated into two parts: diffractions and reflections. Then, we construct a macro-velocity model of lunar subsurface by focusing analysis on separated diffractions. Both the synthetic ground penetrating radar (GPR) and LPR data shows that the migration results of separated reflections have much clearer subsurface structures, compared with the migration results of un-separated data. Our results produce accurate velocity estimation, which is vital for high-precision migration; additionally, the accurate velocity estimation directly provides solid constraints on the dielectric permittivity at different depth. [ABSTRACT FROM AUTHOR]

In this study, we further develop the processing of ground-based interferometric radar measurements for the application of bridge monitoring. Applying ground-based radar in such complex setups or long measurement durations requires advanced processing steps to receive accurate measurements. These steps involve removing external influences from the measurement and evaluating the measurement uncertainty during processing. External influences include disturbances caused by objects moving through the signal, static clutter from additional scatterers, and changes in atmospheric properties. After removing these influences, the line-of-sight displacement vectors, measured by multiple ground-based radars, are decomposed into three-dimensional displacement components. The advanced processing steps are applied exemplarily on measurements with two sensors at a prestressed concrete bridge near Coburg (Germany). The external influences are successfully removed, and two components of the three-dimensional displacement vector are determined. A measurement uncertainty of less than 0.1 mm is achieved for the discussed application. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) has proved to be a successful technique for the detection of landmines and Improvised Explosive Devices (IEDs) buried in the ground. In the last years, novel architectures for safe and fast detection, such as those based on GPR systems onboard Unmanned Aerial Vehicles (UAVs), have been proposed. Furthermore, improvements in GPR hardware and signal processing techniques have resulted in a more efficient detection. This contribution presents an experimental validation of a hybrid Forward-Looking–Down-Looking GPR architecture. The main goal of this architecture is to combine advantages of both GPR architectures: reduction of clutter coming from the ground surface in the case of Forward-Looking GPR (FLGPR), and greater dynamic range in the case of Down-Looking GPR (DLGPR). Compact radar modules working in the lower SHF frequency band have been used for the validation of the hybrid architecture, which involved realistic targets. [ABSTRACT FROM AUTHOR]

As one of the main payloads mounted on the Yutu-2 rover of Chang'E-4 probe, lunar penetrating radar (LPR) aims to map the subsurface structure in the Von Kármán crater. The field LPR data are generally masked by clutters and noises of large quantities. To solve the noise interference, dozens of filtering methods have been applied to LPR data. However, these methods have their limitations, so noise suppression is still a tough issue worth studying. In this article, the denoising convolutional neural network (CNN) framework is applied to the noise suppression and weak signal extraction of 500 MHz LPR data. The results verify that the low-frequency clutters embedded in the LPR data mainly came from the instrument system of the Yutu rover. Besides, compared with the classic band-pass filter and the mean filter, the CNN filter has better performance when dealing with noise interference and weak signal extraction; compared with Kirchhoff migration, it can provide original high-quality radargram with diffraction information. Based on the high-quality radargram provided by the CNN filter, the subsurface sandwich structure is revealed and the weak signals from three sub-layers within the paleo-regolith are extracted. [ABSTRACT FROM AUTHOR]

China New Generation Doppler Weather Radar (CINRAD) plans to upgrade its hardware and software to achieve polarimetric function. However, the small-magnitude polarimetric measurements were negatively affected by the scattering characteristics of ground clutter and the filter's response to the ground clutter. This polarimetric contamination was characterized by decreased differential reflectivity (ZDR) and cross-correlation coefficient (ρhv), as well as an increased standard deviation of the differential phase (ΦDP), generating a large-area and long-term observational anomaly for eight polarimetric radars in South China. Considering that outliers simultaneously appeared in the radar mainlobe and sidelobe, the variations in the reflectivity before and after clutter mitigation (ΔZH) and ρhv were used for quantitatively describing the random dispersion caused by mainlobe and sidelobe clutters. The performance of polarimetric algorithms was also reduced by clutter contamination. The deteriorated membership functions in the hydrometeor classification algorithm changed the proportion of classified echoes. The empirical relations of R(ZH, ZDR) and R(KDP) were broken in the quantitative precipitation estimation algorithm and the extra error considerably exceeded the uncertainty caused by the drop-size distribution (DSD) variability of R(ZH). The above results highlighted the negative impact of clutter contamination on polarimetric applications that need to be further investigated. [ABSTRACT FROM AUTHOR]

In light of recent Arctic change, there is a need to better understand sea ice dynamic processes at the floe scale to evaluate sea ice stability, deformation, and fracturing. This work investigates the use of the Gamma portable radar interferometer (GPRI) to characterize sea ice displacement and surface topography. We find that the GPRI is best suited to derive lateral surface deformation due to mm-scale horizontal accuracy. We model interferometric phase signatures from sea ice displacement and evaluate possible errors related to noise and antenna motion. We compare the analysis with observations acquired during a drifting ice camp in the Beaufort Sea. We used repeat-scan and stare-mode interferometry to identify two-dimensional shear and to track continuous uni-directional convergence. This paper demonstrates the capacity of the GPRI to derive surface strain on the order of 10−7 and identify different dynamic regions based on sub-mm changes in displacement. The GPRI is thus a promising tool for sea ice applications due to its high accuracy that can potentially resolve pre- and post-fracture deformation relevant to sea ice stability and modeling. [ABSTRACT FROM AUTHOR]

This research focused on the quantitative assessment of the surface deformation velocities and rates and their natural and man-made controlling factors as the potential risks along the seismically active 70 km section of buried oil and gas pipeline in Azerbaijan using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset (SBAS) remote sensing analysis. Both techniques showed that the continuous subsidence was prevailing in the kilometer range of 13–70 of pipelines crossing two seismic faults. The ground uplift deformations were observed in the pipeline kilometer range of 0–13. Although both PS-InSAR and SBAS measurements were highly consistent in deformation patterns and trends along pipelines, they showed differences in the spatial distribution of ground deformation classes and noisiness of produced results. High dispersion of PS-InSAR measurements caused low regression coefficients with SBAS for the entire pipeline kilometer range of 0–70. SBAS showed better performance than PS-InSAR along buried petroleum and gas pipelines in the following aspects: the complete coverage of the measured points, significantly lower dispersion of the results, continuous and realistic measurements and higher accuracy of ground deformation rates against the GPS historical measurements. As a primary factor of ground deformations, the influence of tectonic movements was observed in the wide scale analysis along 70 km long and 10 km wide section of petroleum and gas pipelines; however, the largest subsidence rates were observed in the areas of agricultural activities which accelerate the deformation rates caused by the tectonic processes. The diverse spatial distribution and variation of ground movement processes along pipelines demonstrated that general geological and geotechnical understanding of the study area is not sufficient to find and mitigate all the critical sites of subsidence and uplifts for the pipeline operators. This means that both techniques outlined in this paper provide a significant improvement for ground deformation monitoring or can significantly contribute to the assessment of geohazards and preventative countermeasures along petroleum and gas pipelines. [ABSTRACT FROM AUTHOR]

This paper deals with the application of geophysical prospections to the study of the Church of Saint Cosma in Helerito inside the Monastery of Saints Cosma and Damiano (Tagliacozzo, L'Aquila, Italy). The research aimed to verify the presence of buried ancient archaeological structures of a construction phase older than the current building. Thus, a grid of ground-penetrating radar (GPR) profiles was carried out inside the building in the available spaces using a radar unit equipped with a multi-frequency antenna of 200–600 MHz. The analysis of 2D radargrams and horizontal slices relative to different temporal ranges led to the identification of significant regular patterns of the amplitude of the electromagnetic signals. The results suggest the presence of a buried structure below the analyzed surface. [ABSTRACT FROM AUTHOR]