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We demonstrated a photonics-based X-band radar system, in which the transmitter generates a linear frequency modulated signal centered at 10GHz with 2GHz bandwidth based on photonic arbitrary waveform generator (PAWG), and the receiver is based on photonic de-chirping. We conducted a field experiment on this radar system and achieved inverse synthetic aperture radar (ISAR) imaging of non-cooperative targets (airplane), verified the possibility of its application in future radar applications.

The vehicle-mounted millimeter-wave radar has been widely used in modern vehicles. The radar needs accurate feedback on the information about the surrounding environment, and the driverless cars also need to have extremely high safety.Therefore, it is of great significance to enhance the ability of the vehicle-mounted millimeter-wave radar to detection of the surrounding targets. Traditional vehicle-mounted millimeter-wave radar usually signals the target echo signal and finally get the target information. In the signal processing process, it is greatly affected by the weather environment, so it is difficult to ensure a sustainable high detection rate. Therefore, a deep learning-based radar spectrum target detection method is proposed to quickly identify the objects present in the echo spectrum. First, the vehiclemounted millimeter radar spectrum mat format data is extracted, make the dataset for training, then adjust and optimize the SSD (Single Shot Multibox Detector) model according to the situation of the data set, and identify the spectral image using the optimized a training model. In the experiment, 11635 pictures were divided into training set and test set at 9:1, and the average mAP of various targets reached 94.35%, thus detecting the radar echo spectrum based on deep learning and achieving good results. The proposed spectral target detection method can be applied to vehicle-mounted millimeterwave radar.

Aiming at the problem that it is difficult to accurately detect and locate pedestrians due to high air dust concentration and low visibility at the construction site lifting equipment, a pedestrian detection and positioning method based on monocular vision combined with millimeter wave radar is proposed. This method inputs the RGB image obtained by the monocular camera into the YOLOv4 algorithm to achieve pedestrian target detection. The distance information of the obstacle obtained by the millimeter wave radar and the pixel coordinate information of the pedestrian obstacle target obtained by the monocular camera are used for information fusion, and then it based on the positioning principle of monocular vision realizes the spatial positioning of pedestrian targets. Experimental results show that the accuracy and recall rates of pedestrian detection reach 94.09% and 93.52%, respectively, which are higher than 90.79% and 89.13% of the YOLOv3 algorithm, and the detection speed reaches 65 FPS; the relative error of pedestrian positioning in the lateral distance is 3.52%. The method is accurate and rapid, and can better realize real-time pedestrian detection and positioning in the hoisting equipment site.

Synthetic Aperture Radar (SAR) imaging systems operate by emitting radar signals from a moving object, such as a satellite, towards the target of interest. Reflected radar echoes are received and later used by image formation algorithms to form a SAR image. There is great interest in using SAR images in computer vision tasks such as classification or automatic target recognition. Today, however, SAR applications consist of multiple operations: image formation followed by image processing. In this work, we train a deep neural network that performs both the image formation and image processing tasks, integrating the SAR processing pipeline. Results show that our integrated pipeline can output accurately classified SAR imagery with image quality comparable to those formed using a traditional algorithm, showing that fully neural network based SAR processing pipeline is feasible.

Often a crucial exploitation of a Synthetic Aperture Radar (SAR) image requires accurate and precise knowledge of its geolocation, or at least the geolocation of a feature of interest in the image. However, SAR, like all radar modes of operation, makes its measurements relative to its own location or position. Consequently, it is crucial to understand how the radar’s own position and motion impacts the ability to geolocate a feature in the SAR image. Furthermore, accuracy and precision of navigation aids like GPS directly impact the goodness of the geolocation solution.

We present an analysis of image reconstruction quality that includes the use of traditional and deep-learning quality metrics for sparse reconstructions of three-dimensionally (3D) focused synthetic aperture radar (SAR) data. A major goal of our analysis is to explore the usefulness of various metrics to demonstrate their utility in 3D focused scenarios. We make use of synthetic prediction to help fully span the large parameter space of a two-dimensional cross-range aperture. The analysis including the synthetic prediction will help guide future measurements of scale models in our compact radar range.1

Frequency modulated continuous wave (FMCW) radar allows for a wide range of research applications. One primary use of this technology which is explored in this paper is the ground penetrating radar. To achieve high sensing performance, wide-band spectral reconstruction and sophisticated image reconstruction algorithm have been developed to overcome hardware limitations. Applications and future work include Synthetic Aperture Radar (SAR) imaging, innovative GPR, and unmanned aerial vehicle (UAV) radar systems.

Ultra-wideband (UWB) ground-penetrating radar (GPR) technology has been widely employed for detecting targets that range from buried explosive devices such as landmines, improvised explosive devices (IEDs), to underground utilities and tunnels, etc. However, the backscatter signals from the ground surface pose a critical challenge for downward-looking GPR systems since (i) these ground return signals have significant power compared to the backscatter signal from subsurface targets, and (ii) the ground return and target signals completely overlap in both the time and frequency domains. Many techniques have been proposed to date; however, they all have limitations in mitigating the adverse effects of the very high power ground return interference (GRI) signals. This paper presents a novel technique for reconstructing and extracting the GRI signals from downward-looking UWB GPR signals. Our proposed technique performs an estimation of the return signal from the ground surface. This signal estimation, together with the estimated scatter center of the ground surface, is used to construct a dictionary that represents the ground return signal subspace. Finally, we employ a sparsity-driven optimization algorithm to reconstruct the GRI signals and then extract them from the received radar signals. All information used to construct the dictionary is completely derived from the data. Our technique performs this GRI extraction directly in the phase history data domain prior to synthetic aperture radar (SAR) image formation. Thus, it can be implemented as an additional step, completely independent from all other steps, in the pre-processing stage. Recovery results from simulated data set illustrate the robustness and effectiveness of our proposed technique.

In this paper the results of development and investigation on textile-based radar absorbing materials for protection against battlefield radar are presented. This research has been carried out within the Project “Adaptive Camouflage for the Soldier” (ACAMSII) which has received funding from the European Union’s Preparatory Action for Defence Research—PADR programme under grant agreement No 800871. To develop the fabrics with microwave shielding and absorbing properties samples of woven fabrics were coated with compositions containing inherently conducting polymers (ICPs), carbon-based formulations or their mixtures. For coating screen printing and knife-over-roll techniques were applied, as our aim was to develop the fabrics coated with conductive layer only on the back side of camouflage pattern printed fabric, that it could be integrated in the military camouflage clothing system. In the radar threat evaluation, as a part of ACAMSII project, it was pointed up that a major threat to dismounted soldiers are battlefield radars commonly operating within X and Ku-bands. Consequently, the investigation of reflection and transmission properties of developed textile fabrics was performed in a frequency range of 6–18 GHz, which cover the defined frequencies relevant to the application. It was found that shielding effectiveness (SE) as well as absorption properties depend not only on the amount and type of conductive paste topped on the fabric, but also resides in the construction parameters of fabrics and their finishing before coating.

The paper is focused on investigation of microwave backscattering from wind waves on a clean water surface. Field experiments were carried out in the coastal zone of the Black Sea using dual co-polarized Doppler X-band scatterometer and a three-band Doppler dual co-polarized radar (X-, С-, S-bands). The radar incidence angles were about 50 - 60 degrees, the wind changed in a wide range of speeds. We assumed that microwave backscattering at VV and HH polarizations is composed by a Bragg (polarized) component associated with Bragg waves and a non-polarized component (NBR). Analysis of Doppler spectra of NBR allowed us to remove the effect of strong wave breaking (overturning wave crests) from the time series and to study the backscatter associated only with dm-scale waves. Measurements of wind waves with a wire gauge were carried out simultaneously with the radar monitoring. It is shown that the velocities of non-Bragg scatterers not associated with strong wave breaking in X-, С-, S-bands correspond to the velocities of short dm waves and weakly depend on radar wavelength. The speeds of the scatterers in X-, С-, S-bands associated with overturning wave crests are also close to each other (within the measurement error). The intensity of NBR in X-, С-, S-bands grows with wind speed as well as with the intensity of dm-waves measured by the wire gauge. Strong suppression of NBR and simultaneously measured decrease of short dm-wave intensity are demonstrated, thus confirming the assumption that the intensity of the NBR in X-, С-, S-bands is determined by dm waves.

Nowadays the importance of Unmanned Aerial Vehicle (UAV) based sensing technologies is globally recognized. Indeed, thanks to the ability of investigating large areas in a very short time and at very reduced cost, the UAV sensing technology has been widely used in multiple application contexts, including security and surveillance inspections, environmental monitoring, geology, agriculture, archeology and cultural heritage. Actually, the widespread remote sensing technologies mounted on-board UAVs are mainly optical, thermal and multi-spectral sensors, which are passive technologies designed to measure the signals emitted into the optical and (near and far) infrared portions of the electromagnetic spectrum thus providing useful 2D and 3D information about the observed scene. Radar systems represent an important complementary solution. Indeed, radar system is an active system which transmits and receives electromagnetic signals at microwave frequencies, thus offering the advantages of performing inspections in free space and through-obstacle scenarios. However, UAV based radar imaging is not yet a well consolidated technology due to the significant challenges related to the acquisition modality and data processing strategies. Since both transmitting and receiving radar units must be installed on-board the UAV, this introduces not trivial issues related to payload and assets constrains. Moreover, in order to obtain reliable and easily interpretable images, a high precision UAV trajectory reconstruction must be deployed. As a contribution to this topic, an UAV imaging system prototype based on a microwave tomographic approach was recently proposed. Experimental tests at the Archaeological Park of Paestum (SA) has been recently carried out. During the survey, the UAV platform was piloted in path-planning mode, i.e. “autonomous flight” on a predefined rectangular grid and a novel imaging strategy which exploits multiple measurement lines has been developed.

Implementation of a real-time radar application for a software defined radio (SDR) application presents serious challenges to the software engineer. All of the critical tasks, such as pulse compression and target detection, must be performed within a specified time interval. The efficiency of this implementation determines the amount of downstream processing that can be performed while adhering to strict timing requirements. Researchers at ARL have implemented SDR-based radar applications in the past; however, they have never described the software engineering required to implement such a system. This paper documents, in some detail, the issues associated with implementation of real-time radar processing code. It begins with a bird’s eye view of the software functions, outlined using high-level block diagrams. It then touches upon some of the lower-level coding issues associated with realization of the high-level functionality.

This paper presents research on the use of penetrating radar combined with 3-D computer vision for real-time augmented reality enabled target sensing. Small scale radar systems face the issue that positioning systems are inaccurate, non-portable or challenged by poor GPS signals. The addition of modern computer vision to current cutting-edge penetrating radar technology expands the common 2-D imaging plane to 6 degrees of freedom. Applying the fact that the radar scan itself is a vector with length equivalent to depth from the transmitting and receiving antennae, these technologies used in conjunction can generate an accurate 3-D model of the internal structure of any material for which radar can penetrate. The same computer vision device that localizes the radar data can also be used as the basis for an augmented reality system. Augmented reality radar technology has applications in threat detection (human through-wall, IED, landmine) as well as civil (wall and floor structure, buried item detection). For this project, the goal is to create a data registration pipeline and display the radar scan data visually in a 3-D environment using localization from a computer vision tracking device. Processed radar traces are overlayed in real time to an augmented reality screen where the user can view the radar signal intensity to identify and classify targets.

Linear transport infrastructures, as bridges and viaducts, are exposed to natural hazards or endogenous events, which can affect their operation and structural integrity. Recent unpredicted bridge failures and collapses highlight the need for effective structural monitoring operations, especially for aged concrete structures. Non-Destructive Testing (NDT) methods, such as Ground Penetrating Radar (GPR) and Mobile Laser Scanner (MLS), have been used for the assessing and monitoring of such structures in recent years. Our paper reports on the outcomes of the integrated monitoring method based on the use of GPR and MLS technologies for the structural assessment of bridges and the prevention of damages induced by structural subsidence. The analyses we made aim to assess the structural integrity of the Olivieri Viaduct, located in Salerno, Italy. Designed for this, a GPR inspection was made using multifrequency GPR systems equipped with both ground-coupled and air-launched antennas. In addition, MLS surveys were carried out to analyze and quantify pavement surface irregularities. The surface and structural condition of the pavement as measured by MLS has been integrated with GPR outcomes to identify and classify potential damage sources likely responsible for layer deterioration and surface decay. This study confirms that an integrated nondestructive monitoring approach based on GPR and MLS technologies can be successfully implemented to assess the health-condition of critical assets, clearing the way for integrated approaches in continuous monitoring of transport infrastructure.

In this paper we describe the first realization of a combined radar and lidar system based on integrated photonic technology, developed within the project “RODI-RF/OPTICAL Combined coherent Transceiver for RADAR/LIDAR and RF/Optical communications in space” funded by the Italian Space Agency for the technological validation of photonic systems for space applications.

Global climate warming is rapidly reducing Arctic sea ice volume and extent. The associated perennial sea ice loss has economic and global security implications associated with Arctic Ocean navigability, since sea ice cover dictates whether an Arctic route is open to shipping. Thus, understanding changes in sea ice thickness, concentration and drift is essential for operation planning and routing. However, changes in sea ice cover on scales up to a few days and kilometers are challenging to detect and forecast; current sea ice models may not capture quickly-changing conditions on short timescales needed for navigation. Assimilating these predictive models requires frequent, high-resolution morphological information about the pack, which is operationally difficult. We suggest an approach to mitigate this challenge by using machine learning (ML) to interpret satellite-based synthetic aperture radar (SAR) imagery. In this study, we derive ML models for the analysis of SAR data to improve short-term local sea ice monitoring at high spatial resolutions, enabling more accurate analysis of Arctic navigability. We develop an algorithm/classifier that can analyze Sentinel-1 SAR imagery with the potential to inform operational sea ice forecasting models. We focus on detecting two sea ice features of interest to Arctic navigability: ridges and leads (fractures in the ice shelf). These can be considered local extremes in terms of ice thickness, a crucial parameter for navigation. We build models to detect these ice features using machine learning techniques. Both our ridge and lead detection models perform as well as, if not better than, state-of-the- art methods. These models demonstrate Sentinel-1's ability to capture sea ice conditions, suggesting the potential for Sentinel-1 global coverage imagery to inform sea ice forecasting models.

In radar target tracking, knowledge of the true dynamics of target motion is paramount for accurate state estimates. In this paper, we propose a method of target maneuver detection utilizing symbolic dynamics. We demonstrate its ability to compete with other commonly used maneuver detectors. This is done through simulations performing target maneuver detection.

Synthetic aperture radar (SAR) imaging has been widely used for various nondestructive evaluation (NDE) applications. The sampling strategy used to collect imaging data has great implications on the resulting image quality. The most widely-used strategies include uniform sampling and nonuniform sampling. While the former can provide relatively higher resolution and lower noise level, the latter can provide faster scanning time. However, applying uniform sampling for high resolution can be a critical issue when scanning a relatively large area. Moreover, neither of them takes target properties (e.g., depth, spatial distribution, etc.) directly into account. It has been verified that the optimum SAR resolution is target depth dependent, which means SAR intrinsically has lower resolution for targets at larger depths. This indicates that the sampling step can be accordingly increased for targets at large depths with little resolution degradation. Meanwhile, if the scene under test is relatively large and the flaws (usually just a few) are located in a relatively small region, then optimum uniform sampling over the entire large aperture, rather than a smaller area directly above the targets, may be unnecessary. Thus, first estimating target distribution density can help reduce the time in collecting imaging data. Consequently, an intelligent sampling strategy, with considerations of targets properties, is highly desired and investigated in this paper.

Detecting targets with long distance and high resolution is the goal of radar techniques. Traditional electrical radar which has a long working distance always work at low frequency and thus has a limited bandwidth. We demonstrate a microwave photonic radar system which can realize larger bandwidth at low-frequency band based on optical-domain frequency operation. P-band and C-band radio-frequency (RF) signals with 700-MHz and 4-GHz bandwidths, respectively are generated, while the latter is adopted to detect space-separated corner reflectors to demonstrate the effectiveness of the proposed system.

UMass Lowell BTTC researchers are investigating the use of a high resolution 75 GHz radar to identify the internal defects of the fiberglass filaments in wind turbine blades. Developing the acquisition and image analysis software, the team is fabricating a laboratory based test radar and anechoic chamber for performing the preliminary measurements. Central to the team's challenge is the development of an implementable design for a manufacturing plant’s wide area inspection system. The measurements and system design will be presented.

Abdominal MRI is susceptible to respiratory motion artifacts. The existing clinical solution is using breathing belt to track the movement of the abdomen and trigger MRI acquisition during the end-expiration phase. Attaching respiratory belt to patients often slows down clinical workflow and affects patient comfort especially for those with surgical wounds and respiratory disorders. Herein we, for the first, propose a novel MRI compatible frequency modulated continuous wave (FMCW) radar to track respiratory motion within MRI bore in a non-contact fashion. The electromagnetic wave from FMCW radar can penetrate clothing and MRI RF coils to achieve continuous monitoring of patient’s vital signs. The system consists of a front-end FMCW radar sensor and a FPGA based power management/communication board that interface with a clinical MRI scanner. This design fully integrates the FMCW radar signal with MRI control console to enable real time respiratory triggered MRI acquisition. Consistent respiratory waveform was validated by comparing FMCW signal with traditional breathing belt measurement. Superior image quality from clinical MRI pulse sequence was achieved using the developed system in healthy volunteers.

Doppler radar is the main remote sensing equipment to monitor severe convective weather which has significant threats to social and economic activities. It is important to accurately predict the time and location of severe weather events. In this study, we use a deep learning technique to predict severe weather events based on radar images. Firstly, we transform the prediction problem into a binary classification problem and use Generative Adversarial Networks (GANs) to construct a classifier. Then Doppler radar images are used to train the model. The critical success index, probability of detection, and false alarm ratio are used to evaluate the prediction results. The experimental results show that the GANs model provides satisfactory results.

In this paper, a new approach for classifying targets captured by low-resolution Ground Surveillance Radar is proposed. Radar target is detected by the Doppler effect in radar echo signal. Those signals can be disposed in various domains to gain unique features of targets which can be used in radar target classification and enhance its effectiveness. The proposed approach consists of two steps, transforming original signals from 1D to 2D and constructing deep 2D convolution neural networks(CNN). In first step, Toeplitz matrix is made use of reconstructing Radar signal, to build a 2D plane of data. Reconstruction does not change the characteristic distribution of the signal but maps the signal from one to two dimensions in a rearranged method. Whilst,it makes possible of using 2D CNN to train the data. In second step, we take advantage of the “bottleneck” block to create 2D CNN, which guarantee the depth of CNN and ease the problem of vanishing/exploding gradients in back propagation process. method was tested on actual collected database including human and car, which achieve 99.7% accuracy on the original test set and 97% accuracy after adding noise.

The “quantum Radar” seeks to harness quantum entanglement and squeezing to improve the detection of faint targets beyond the best possible classical sensor. The major challenge of the quantum Radar is the very large optical loss of the target beam, which induces vacuum fluctuations and hampers the quantum correlation. In addition, the faint target reflection requires the use of high power beams, which favors high-power stimulated sources of coherent squeezed light over spontaneous low power sources of entangled photons. I will present the lossy SU1,1 nonlinear interferometer with coherent seeding as a candidate for quantum Radar sensing. I will highlight the quantum features of this sensor and report a demonstration of the quantum enhanced interference contrast, even in the presence of high loss.

Cylindrical phased array radar has an important role in low-altitude target surveillance, and signal processing is one of the important component modules of the radar system. Cylindrical radar as one kind of phased-array radar has the characteristics of full azimuth range of multi beam and huge data which makes high demands of signal processing. FPGA occupies an important position in radar signal processing because of its characteristics of high-speed and real-time. In this paper, signal processing scheme based on Xilinx's FPGA Kintex-7 and multi-core digital signal processor (DSP) is proposed, which mainly implements functions such as data reception, pulse compression, and moving target detection (MTD) processing etc. By comparing the actual results with the matlab simulation results, it is shown that this scheme has a good performance in stablility with fast processing speed. Moreover, it has obvious advantages in the design and provides great value for engineering.

This paper presents a practical implementation of a software-defined ground-based synthetic aperture radar. The proposed system uses a PC, a general purpose low-cost SDR platform and a minimum number of external components. The SDR platform is a USRP N200 equipped with a WBX RF daughterboard. The only external components are two offthe- shelf RF switches, an Arduino Uno microcontroller board, a fixed RF attenuator and two Vivaldi antennas. The system implements stepped-frequency radar operation with a bandwidth of 1500 MHz (500 – 2000 MHz) achieving a resolution of 10 cm in air. The proposed system mimics the behavior of Vector Network Analyzers. It works by sweeping its operating frequency over the 1500 MHz frequency band and storing the amplitudes and phases of the scattered waves. These are assembled in a matrix, the so-called raw data matrix, which is processed using an implementation of the backprojection (BP) SAR algorithm to generate the bidimensional focused image. All of the above-mentioned assertions have been validated experimentally, proving the proposed system’s ability to generate high resolution images of the analyzed scene.

Space debris are human-made objects, of variable sizes and shapes, that orbit the Earth or reenter the atmosphere. They represent a serious problem for every active spacecraft and satellite, due to the high risk of collision and consequently the generation of new debris. One of the main segments of the Space Situational Awareness program regards space surveillance and tracking activities, with procedures for tracking resident space objects, using a sensor network composed by radars, telescopes and lasers. In this way, it is possible to collect data in order to catalogue and perform orbit predictions of objects orbiting the Earth, with the aim of avoiding collisions between them. One of the Italian radars for space and surveillance tracking functions is represented by the BIRALET system, an acronym which stands for Bistatic Radar for LEO Tracking. This radar operates in P-band at 410-415 MHz, is a bi-static configuration composed of a transmitting 7-meter antenna and the SRT (Sardinia Radio Telescope) as receiver, with a baseline of about 20 km. The Sardinia Radio Telescope is a 64-meter fully steerable wheel-and-track antenna, located near San Basilio (Cagliari, Sardinia, Italy). It represents a flexible instrument used for radio astronomy and space science studies, developed to work in a wide frequency range between 300 MHz and 110 GHz. In this paper, we present a review of the status of the SRT for space debris observation. In particular, we describe three possible system configurations, in order to perform Doppler shift and range measurements. In particular, we present a simplified solution based on a spectrum analyzer as a back-end that permits only Doppler shift measurements. Another more complex solution for Doppler shift measurements, is based on the electronic Red Pitaya board. For the Red Pitaya we developed also a dedicated signal acquisition chain with a down-conversion circuit, in order to shift the received signal in the frequency range of the board. Finally, a more complex solution that allows range and range rate measurements, based on the National Instrument USRP board as a back-end. For future developments, we present the possibility to improve our system, using a C-band Phased Array Feed as a receiver.

We present experiments to explore the use of deep neural network classification models for estimating the orientation of objects with linear structures from polarimetric radar data. We derive all radar data from two physical model aircraft and their corresponding computerized surface models. We make extensive use of synthetic pre- diction to help fully span the large parameter space as is consistent with best practice. Synthetic predictions are based upon a linear quad-polarized (H: horizontal, V: vertical) Ka-band stepped frequency measurement inverse synthetic aperture radar (ISAR) turntable system located inside the Air Force Research Laboratory (AFRL) Sensor Directorate's Indoor Range. The use of multiple polarimetric channels in a deep learning classification framework are shown to significantly help estimate orientation when the co-polarization channels significantly differ from each other. Future research directions are discussed.

Bridges play a pivotal role in modern society, especially when considering the amount of global automotive transportation; therefore, it is essential to protect these structures from deterioration. Engineers and inspectors are searching for efficient subsurface defect detection methods due to the critical nature of these structures. Ground Penetrating Radar (GPR), a Nondestructive Evaluation (NDE) technique, is a well-established method used to locate subsurface degradation in bridges. The GPR method uses radiofrequency electromagnetic waves to create images of subsurface irregularities by detecting dielectric property differences in bridge decks. Using artificial intelligence (AI) to augment manual GPR data analysis can increase the NDE performance in the field by mitigating data interpretation. One dimensional (1D) Convolutional Neural Networks (CNNs) were employed to evaluate concrete bridge decks to classify the GPR data collected from eight laboratory-created concrete specimens with either defect-free or known artificial subsurface defects. We used 1D-CNNs to classify GPR data for accurate flaw identification. The proposed method’s accuracy was greater than 84%, outperforming existing Machine Learning (ML) based GPR data classifications and demonstrating the proposed method’s effectiveness in detecting subsurface defects.

Concrete compressive strength is important, yet difficult to quantify without direct testing. In particular, it is difficult to obtain the mature concrete strength measurements which are necessary for safe and optimal use of existing structural capacity. Reliable measurements of mature strength using nondestructive testing methods (NDT) like ultrasonic pulse velocities depend on many factors, including the inherent material variability, sampling frequency, and quality of the NDT measurements. Methods like ground penetrating radar (GPR) and concrete maturity relationships are common for investigating the early-age properties of concrete but are rarely used for mature concrete. Using a case study of a concrete pedestrian bridge where both long term temperature data from structural health monitoring (SHM) and recent GPR surveys of the bridge are available, this work compares the predicted 8-year strength using two different indirect methods. The first uses a regression model trained on laboratory GPR attributes and material properties. The second uses the maturity method to predict strength based on 28-day cylinder tests and the temperature history recorded by the bridge's SHM system. The maturity method predicts the correct relative trends in strength between the two phases and overpredicts the cylinder 28-day strength by 12% 􀀀 25%. The GPR predictions do not reliably capture the relative difference between the two phases, but have similar accuracy and underpredict cylinder strength by 4% 􀀀 22%. These strength comparisons from noninvasive methods motivate further improvements in GPR attribute modeling and integrating these methods with other ultrasonic models to improve spatial resolution and reliability.

This paper proposes a low-resolution ground surveillance radar automatic target recognition(ATR) method based on onedimensional convolutional neural network (1D-CNN), which solves the problem of overfitting using complex CNN for data classification. First, the target recognition algorithm combines the time-domain waveform, power spectrum, and power transform spectrum into the three channels of the established 1D-CNN input. After that, the autoencoder is used to reduce the feature dimension and improve the classifier's ability to select parameters autonomously. Finally, the Bayesian hyperparameter optimization method is used to optimize hyperparameters, which not only simplifies the network structure, but also reduces the parameter calculation scale. We tested our method with the collected data to classify people and cars, and the results showed that the recognition accuracy rate has reached 99%. Compared with the traditional artificial feature extraction target recognition method, our model has better recognition performance and adaptability.

Passive radar system is widely used in military, scientific and commercial fields. It faces a great challenge in detecting small slow-moving targets. This paper propose a new Bernoulli track-before-detect filter to deal with the detection difficulty. By estimating the ground clutter parameters, this new method can adapt to the changing of ground clutter. Simulation results prove the efficiency of this new method.

In this paper, we propose a radar cross section analysis for frequency selective surfaces using a meander line antenna as a unit cell. The periodic structure consists of a passive copper antenna backed by a dielectric substrate. Two configurations of constant aperture composed of three and five elements are presented. The radar cross section is evaluated by using a plane wave excitation with linear polarization in order to assess the ratio of the backscattered power.

This paper presents a practical implementation for low-cost, software-defined Ground Penetrating Radar. The proposed system uses solely a general purpose low-cost SDR platform, two antennas and a PC. The main advantage of this implementation compared to similar implementations in the literature is the absence of external components/circuits or custom-built RF boards. The SDR platform is USRP N200 equipped with WBX RF daughterboard. The only external additions are two Vivaldi antennas. The system implements chirp radar operation with a bandwidth of 40 MHz achieving a theoretical resolution of 375 cm in air. Because of the reduction of electromagnetic wave velocity in soil, the resolution improves significantly in practical situations where the wave travels through a high permittivity medium. The resulting system is capable to successfully discover large subterranean voids like caves or tunnels. The paper presents an experimental validation where the proposed system is employed to detect the presence of a tunnel.

This article is devoted to the problems of radar sensing. Herein, we have considered the tasks of modeling and recognizing radar images. The modeling technology was based on the independent creation of terrain models and objects, which were then integrated into a three-dimensional (3D) scene. This approach enabled the operative creation of a number of image variants of different classes. Recognition methods and algorithms were based on the use of the so-called conjugacy index as a measure of proximity. At the same time, support subspaces of the minimum dimension were formed by vectors, components of which were samples of the radar image. Problems of higher accuracy of recognition due to a division of classes into subclasses and a combination of the support subspace method with the neural convolutional networks were considered.

Control of orbital angular momentum (OAM) offers the potential for increases in control, sensitivity, and security for high-performance microwave systems. OAM is characterized by an integer OAM mode where zero represents the case of a plane wave. Microwaves with a nonzero OAM mode propagate with a helical wavefront. Orthogonal OAM modes can be used to carry distinct information at the same frequency and polarization, increasing the data rate. The OAM waveform may also increase radar detection capability for certain shaped objects. OAM can be induced by broadcasting a plane wave through a spatial phase plate (SPP) dielectric which introduces an azimuthally dependent phase delay. However, SPPs are frequency-specific, which presents an obstacle for harnessing OAM in frequency-modulated communication systems and wide-bandwidth radar. In this study, we develop a circular phased array to synthesize the desired vortex-shaped wavefront. This approach offers a critical advantage: the phases of all antenna elements are easily programmable under different frequencies. As a result, transmission and reception of the OAM beam can be controlled with great flexibility, making it operable over a wide frequency spectrum, which leverages OAM radar functionality and performance. In this paper, we will investigate a wide-bandwidth radar with OAM mode-control and signal processing.

Recently, much of the quantum radar/lidar research is focused on correlating single photon detection events with no delay line on the idler path. In other words, measuring the idler immediately, and correlating these events with later received photon events from the returning signal. This research approach has raised some questions due to the fact that all measurements done are classical, yet researchers are still observing sensor improvement in comparison to classical techniques. This therefore implies that the benefits from quantum radar/lidar using these techniques should be able to be explained entirely classically. This paper explores this concept by asserting that the correlation between the signals used in quantum remote sensing is largely due to the fact that the signal and idler photons are created simultaneously (which is only possible from an entangled source). We show, using very simple computer simulations, that having a single photon correlated (binary) waveform leads to correlation SNR advantages only in the low photon level regime, agreeing with previous literature.

A photonics-based inverse synthetic aperture radar (ISAR) for near-field RCS calculation is proposed. The proposed radar implements the function of radar range profile target recognition, 2D ISAR image and near-field RCS calculation. Firstly, a photonics-based ISAR is proposed with a frequency-quadrupled linear frequency-modulated continuous wave (LFMCW) signal covers 18-26 GHz. A 2D ISAR image with 2 cm*2 cm resolution is obtained. Secondly, a method of calculating the RCS from the ISAR image is also proposed. According to the Parseval theorem, by summing the pixel values of target area 2D ISAR image and multiplying by the scaling factor, the mean RCS value of target in the radar working band can be obtained. In the experimental demonstration, the standard metal spheres with diameters of 10 cm, 15 cm and 20 cm are tested 1.2 m away from the radar. The testing results show that the difference between the RCS test value and the theoretical value is less than 1.31 dB.

We present a concept of software modification of three-dimensional (3D) radar systems, designed to work in the low pulse repetition frequency mode, that equips them with the ability to estimate the radial velocity and to properly measure the range of targets that are detected outside the radar’s instrumented range. Despite the fact that the proposed modifications are designed so as to require only minor changes in software, they offer significant growth in the system capabilities. The modified system may potentially work in the medium pulse repetition frequency mode as a so-called four-dimensional (4D) system. The proposed Doppler velocity estimation algorithm is presented in details as well.

Aiming at the target characteristics and scene generation requirements of radar targets in defense system, a radar target attitude inversion method based on narrow-band radar tracking data is proposed. The measured data of the radar sensor is collected, the dynamic scene is analyzed and excavated, and the target radar line-of-sight angle inversion model is established based on the target electromagnetic scattering characteristic simulation data. Based on the model, the attitude inversion of the radar target is completed, and target dynamic RCS is used to verify the model. The correctness and effectiveness of the radar target attitude inversion method are verified.

The performance of radar automatic target recognition (ATR) highly depends on the quality of training database, the extracted features and classification algorithm. Radar target is detected by the Doppler effect in radar echo signal. Through processing the echo signals in different domains, the distinctive characteristic can be obtained intuitively. Furthermore, we can utilize the extracted features to complete radar target classification. This paper proposes a novel target recognition method based on 1D-convolution neural network (CNN) aiming at the ATR of low-resolution ground surveillance radar. The proposed approach uses 1D-CNN as feature extractor and softmax layer as classifier. We tested our method on actual collected database to classify human and car, which reached an accuracy of 98%. Compared with conventional artificial feature extraction approaches, our model shows better performance and adaptability.

The future of spaceborne systems for Earth Observation is moving towards formations of multiple lightweight spacecrafts, either to enhance the surface temporal coverage or to augment the system performance by increasing the number of simultaneous observation modes. In the case of Synthetic Aperture Radars (SAR), the latter option is of particular relevance and is mainly brought forward by bistatic and multistatic SAR system concepts. In this context, The ESA Earth Explorer 10 Candidate STEREOID (Stereo Thermo-Optically Enhanced Radar for Earth, Ocean, Ice, and land Dynamics) is promoting the launch of 2 sub-500 Kg satellites spacecraft carrying a receive-only radar instrument as main payload, and flying in a re-configurable formation with Sentinel-1 C or D, which will be used as illuminator. From the radar standpoint, the main novelty will be brought by the large orbital (along-track) separation of the 2 companions from the transmitter, with baseline values that exceed 200 km. Such unprecedented line-of-sight diversity, in angular terms, is in fact deemed to significantly boost the sensitivity to 3D motion and deformation of over land, ice and sea surfaces. The system will be designed to operate in two main formation configurations: 1) A ‘stereo’ configuration, where one satellite will fly ahead of Sentinel-1 whereas the other will follow behind, at approximately the same distance; 2) a ‘XTI/ATI’ configuration, where the two satellites fly close to each other, and hence on the same side with respect to Sentinel-1. In the first configuration the system captures the widest angular diversity, in the second configuration the system gains single-pass interferometric capabilities that allow to perform either topography or coherent target velocity retrieval. As the mission acronym suggests, the system carries an additional medium resolution multispectral payload with a VNIR and a TIR component, that shall not be considered less relevant than the microwave one. Although the orbit configuration and the viewing geometry is generally not ideal for an optical payload in isolation, the assimilation of such simultaneous optical and radar observations are indeed considered fundamental for the achievement of several mission goals, such as for the enhanced retrieval and interpretation of the sea surface currents. Several technical and scientific challenges shall be dealt with in the next few years, i.e. during the study phase of the mission. This work will provide an overview of these challenges, focusing in particular on to the coherent and incoherent signal properties of the STEREOID bistatic measurements, on their benefits as well as their negative implications on retrieval performances, and on the possible assimilation strategies for their integration with the optical measurements.

Surface penetrating imaging technology reconstructs target images that conform to visual perception by interpreting radar data, which reflects the target's geometry and electromagnetic scattering characteristics directly. For surface penetration imaging, various sources of clutter cause failure of image interpretation, therefore clutter suppression is the key to obtain target information accurately. Though clutter suppression approaches aiming at the strong clutter make the target image prominent enough for imaging process, the weak clutter with energy close to the target affects the imaging quality. In this paper, a nonlinear gain control method is proposed for the suppression of weak clutter and improving the contrast of the imaging by non-linearly weakening the data below the threshold and enhancing the data above the threshold. Experiments show that the proposed method can effectively improve the imaging quality.

Deep neural networks have become increasingly popular in radar micro-Doppler classification; yet, a key challenge, which has limited potential gains, is the lack of large amounts of measured data that can facilitate the design of deeper networks with greater robustness and performance. Several approaches have been proposed in the literature to address this problem, such as unsupervised pre-training and transfer learning from optical imagery or synthetic RF data. This work investigates an alternative approach to training which involves exploitation of “datasets of opportunity" micro-Doppler datasets collected using other RF sensors, which may be of a different frequency, bandwidth or waveform - for the purposes of training. Specifically, this work compares in detail the cross-frequency training degradation incurred for several different training approaches and deep neural network (DNN) architectures. Results show a 70% drop in classification accuracy when the RF sensors for pre-training, fine-tuning, and testing are different, and a 15% degradation when only the pre-training data is different, but the fine-tuning and test data are from the same sensor. By using generative adversarial networks (GANs), a large amount of synthetic data is generated for pre-training. Results show that cross-frequency performance degradation is reduced by 50% when kinematically-sifted GAN-synthesized signatures are used in pre-training.

A method for synthesizing any radar signal via post-processing is proposed theoretically and demonstrated experimentally for both pulsed and linear frequency modulated signals. The method does not require transmitting the investigated signal, nor does it require any hardware reconfiguration (such as fully programmable gate arrays), in contrast with ordinary software defined radars. Instead, the method is based on transmitting the ‘stepped frequency continuous wave' signal with a device such as a network analyzer. By obtaining the frequency response in the desired bandwidth (S-parameters), signal-specific digital filters can be applied in order to obtain the response of any other signal. By transforming the filtered frequency response into the time domain, the ordinary processing of such signals can take place in the digital domain. The advantages of different signals can therefore be used by a single optimized chip, simply by swapping its software.