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As the application of UAVs in military and civilian fields becomes more and more widespread, the detection of UAVs in the low-altitude range has also become an important research direction. Compared with radar and visible light detection, infrared technology has become the major UAV detection method with its advantages of all-weather and long range. Most of the current infrared target detection methods are based on convolutional neural networks (CNN), which achieve target detection through feature extraction and feature classification. The performance of all such detection algorithms is highly dependent on their training set. A data set with a large number of samples and wide coverage tends to train a more robust and accurate detector. So, to obtain better detection effects, we perform data augmentation on the infrared UAV dataset by adversarial generative network (GAN). First, we extract the targets from the training set and train a GAN network, using its generator to obtain many new targets which are different from the training set samples, then we randomly extend these targets to the original dataset, and finally we retrain the detectors using the new dataset to achieve better detection. We created an infrared UAV image dataset for our experiments, with only a single target on each image. After data augmentation, multiple UAV targets are randomly generated. The experiments demonstrate that the new dataset trains the model with better detection results. And the GAN data augmentation can be combined with many advanced detectors to make a large improvement in detection.

Optically transparent antennas integrated within an optical aperture enable unique opportunities for shared aperture radio frequency (RF)/electro-optical(EO)/infra-red (IR) sensing and communication. While optical transmission and RF efficiency are critical performance metrics for most applications, those applications with optical imaging requirements necessitate additional imaging quality considerations. This work will present initial investigations into the effects of micromesh antenna elements utilized in a common aperture. Simulated imaging performance as a function of micro-mesh design indicates that as the mesh fill factor increases, the imaging quality as well as the optical transmission decrease. Fill factor increase has also been previously associated with increased RF performance. Specifically, for a 0.5 inch diameter optical aperture with a fill factor of 0.05, a 5% reduction in peak point spread function (PSF) and a 13% reduction in contrast in group-2 of the USAF-1951 resolution chart is presented. The obstruction from the solid topology causes a significant 75% reduction in the PSF. This degradation can be used as an additional metric to evaluate and optimize a hybrid aperture.

CUT-IN is a typical scenario in the process of normal driving, so the ADAS needs to deal with the scenario of cut-in when assisting the driver. In the "CCRT (Intelligent Electric Vehicle) Management Regulation (2020 Edition)" issued by China Automotive Technology and Research Center Co., Ltd in December 2020, the test and evaluation of the cut-in is added. From the test results, it is found that the performance of different models on the scenario of the cut-in is very different. This paper analyzes the scenario of cut-in from the perspective of track test, and summarizes the control strategy of the vehicle. Through analysis, it is found that lane change intention of the target vehicle needs to be found earlier, in order to avoid collision in the cut-in scenario. Therefore, Angle radar needs to be used and the lateral velocity of the target vehicle needs to be detected.

In the recent years a number of FPGA manufacturers have developped System-on-Chip (SoC) devices which integrate FPGA fabric with ARM cores and ADCs/DACs. At the same time, following the rising popularity of deep learning, deep-learning frameworks using FPGA SoCs have emerged which can be used to easily deploy complex neural network models for in-the-field inference. The following paper describes how synthetic-appertureradar satellites based on modern FPGA SoCs can leverage these new developments and gain on-board detection capabilities. In the first section the synthetic aperture radar system is introduced, and modern FPGA SoC features are discussed and the reasoning behindenabling on-board sythetic aperture radar detection capabilities is presented. The next section describes a ship detection system which could be impemented on existing SoCbased radar payloads. The paper is concluded with a few observations on how implementing such a system could affect existing radar platforms.

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.

Some flying objects have high velocity between 10 to 20 Mach, causing extra Doppler dispersion in wideband LFM signal which will deteriorate the ISAR imaging resolution and quality. Also, the detection and imaging of such high-speed objects demands highly on the real-time performance of imaging compensation algorithm. This paper presents a microwave photonic based radar and its compensation algorithm by using digital signal processing. Experiments obtain a clear and high-resolution ISAR image of the high-speed target with velocity of 15Mach. The image has the resolution of 3.12cm*19.12cm, and it just needs 0.30 seconds to do the ISAR imaging.

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.

Surveillance is an important task during naval operations. This task can be performed with a combination of different sensors, including camera systems and radar. To obtain a consistent operational picture of possible threats in the vicinity of a ship, the information from the different sensors need to be combined into one overview image, in which all information related to one object is assigned to this object. In this paper, we present a new dataset for maritime surveillance applications and show two examples of combining information from different sensors. We have recorded data with several camera systems, automatic identification system (AIS) and radar in the Rotterdam Harbor. From all sensors we can obtain tracking information from the different objects. We present a method to associate the tracks and describe how snippets of the ships in the cameras can be used to enrich the information of the objects. Next to that, we show the combined information from AIS and imagery.

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.

The development of new sensing abilities, such as hyperspectral sensors, small man-portable infrared sensors, battlefield radars and faster AI based data processing requires new capabilities of camouflage materials. Military systems also have to work in changing environments calling for adaptivity. ACAMSII is an EU funded project within the Preparatory Action for Defence Research (PADR) programme under grant agreement No 800871. Scientists from Sweden, Germany, Portugal, France, Lithuania and the Netherlands cooperate in the project. ACAMSII conducts applied research on improved Camouflage for the Soldier with purpose to provide increased survivability in diversified environments. The ACAMSII multifunctional camouflage demonstrator consists of three main parts: • The IR adaptivity is realized by integrating phase changing and low emissivity materials in the printed camouflage patterned textile. • Adaption to background colours and lighting conditions in the visible range is managed by operating and driving light emitting diodes (LEDs) embedded in a panel of clothing textile material. The LEDs are controlled by means of a camera and a light sensor placed in the back. Thermochromic materials give further adaptation. • Woven fabric is coated with microwave shielding and absorbing materials reduces the radar signature. For a soldier combat system, not only camouflage properties are of importance but also the comfort properties of the system is evaluated. In this paper emphasis is put on military needs (top-down or market pull) and scientific aspects and technical readiness level of available technology and materials (technology push or down-up), on how to build a consortium in an international collaboration during a pandemic. The concept of EDA Overarching Strategic Research Agenda is introduced and the projects relation to this explained. Only general aspects and results from the three-year project, ending 2021, is presented here whereas specific details of the camouflage system are presented in other papers presented during the session.

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.

Decoys can be used in warfare to affect the enemy intelligence and situational awareness as well as to protect military equipment and soldiers. Since much of current and especially future, reconnaissance is based on advanced wide spectral range sensors, especially airborne sensors carried by drones and satellites, it is imperative that a modern decoy is capable of creating a credible sensor signature in the most commonly used sensors. For this goal, a foil system was designed and fabricated using printed electronics technologies that has sensor responses to thermal imaging, radar and visible or hyperspectral imaging. The foil has a matrix of heating elements, which can be heated in an arbitrary pattern to form any thermal signature desired. Radar response was created by laminating a metal layer on the foil. The foil was painted with a layer of camouflage paint in order to achieve authentic hyperspectral and visible response. The roll-to-roll manufactured, general-purpose decoy foil allows mass production scale benefits since the same foil can be used to mimic any target by using a programmable heater controller.

Radar sounders (RSs) mounted on airborne platforms are active sensors widely employed to acquire subsurface data of the cryosphere for Earth observation. RS data, also called radargrams, provide information on the buried geology by identifying dielectric discontinuities in the subsurface. Recently, a strong effort can be observed in designing automatic techniques to identify the main targets of the cryosphere. However, most of the methods are based on target-specific handcrafted features. Newly convolutional neural networks (CNNs) automatically extract meaningful features from data. However, supervised training requires numerous labeled data that are hard to retrieve in the RS domain. In this work, we adopt a CNN pre-trained in domains other than RS for automatically segmenting cryosphere radargrams. To adapt to the radargram characteristics, we introduce convolutional layers at the beginning of the pre-trained network. We modify the top layers of the network to a U- fashion autoencoder to extract relevant features for the target task. The new layers are fine-tuned with few labeled radargrams to identify and segment five targets: free space, continental ice layering, floating ice, bedrock, and EFZ and thermal noise. The pre-trained weights are not updated during fine-tuning. We applied the proposed approach to radargrams from Antarctica acquired by MCoRDS3, obtaining high overall accuracy. These results demonstrate the effectiveness of the method in segmenting radargrams and discriminating continental and coastal ice structures.

In air traffic flow management system, more attention of flight trajectory prediction is paid to the passing time and altitudes on some report points. For this purpose, a KNN based method using both flight plan and radar trajectory data is proposed in this paper. This method takes radar trajectory data to search for the neighbors of the query trajectory, and then takes the corresponding flight plan data to predict the report-point-conditioned time and altitudes. The experiments on actual flight data verify that the proposed method is able to predict flight point-conditioned time and altitudes accurately.

Millimeter (MM) and Terahertz (THz) waves are transparent to many non-metallic materials and is expected to be applicable for non-destructive inspections. Especially, the radar 3D imaging technology using the semiconductor continuous wave (CW) MMW and THz sources is important because of its compactness, stability and high signal-to-noise ratio (SNR). In this presentation, we will introduce the development of a prototype of a 300 GHz walk-through body scanner for the security gates. For the actual system, we are developing the frequency-modulated continuous wave (FMCW) imaging method around 300 GHz. We have demonstrated the 3-dimentional real-time imaging of a material hidden underneath cloths. We will demonstrate the system configuration and the current status as well as some results of the actual imaging.

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.

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.

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.

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.

This work builds on another effort described in Application of Jupyter Notebook interfaces and iLauncher to deep learning work ows on HPC systems.22 We describe a complex work ow application which generates millions of images in parallel on an HPC system via web interfaces using ipywidgets in Jupyter Notebooks and the Interface Launcher (iLauncher). Some computations are so complicated, taking many millions of HPC hours, that only a few subject matter experts are able to generate information efficiently. We present our custom application that walks the user through a work flow to include: target selection, configuration of the target, radar phase history simulation, and finally SAR image generation. The interface requests the user to enter a minimal set of parameters while other variables essential to computations are generated on the y and provides status updates on work ow computations. Additionally, the ability to download any data component or view images interactively is provided. This application can be disconnected from the HPC system and reconnected at any time without slowing down the computations on the work ow submitted. Although typically a maximum run time must be specified when submitting a job to the queuing interface on an HPC system, this application uses the HPC-GPS tool to allow users to extend run times even after the initial request is submitted. Our new application helps to reduce the barrier to entry for both complicated physics-based simulations and using HPC systems.

Computational millimetre-wave (mmW) imaging and machine learning have followed parallel tracks since their inception. Recent developments in computational imaging (CI) have significantly improved the imaging capabilities of mmW imaging systems. Machine learning algorithms have also gained huge popularity among researchers in the recent past with several approaches being investigated to make use of them in imaging systems. One such algorithm, image classifier, has gained significant traction in applications such as security screening and traffic surveillance. In this article, we present the first steps towards a machine learning integrated CI physical model for image classification at mmW frequencies. The dataset used for training CI system is generated using the developed single-pixel CI forward-model, eliminating the need for traditional raster-scanning based imaging techniques.

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.

Drone classification based on radar return signal is an important task for public safety applications. Determining the make or class of a drone gives information about the potential intent of the UAV. We present a novel method for classifying commercially available drones based on their radar return signal, using a convolutional neural network. Our approach achieves 0.46 mean Average Precision (mAP) on a simulated dataset at 5 dB SNR.

This paper considers the autofocusing problem for a rotorcraft-borne forward-looking multiple-input multipleoutput (MIMO) synthetic aperture radar (SAR) system. Autofocusing is critically important for forward-looking MIMO SAR imaging, since the radar position errors due to the limited precision of navigation systems can severely degrade the imaging quality. In this paper, we propose a novel autofocusing method to address the radar position errors, resulting from both translational and rotational motion errors, for forward-looking MIMO SAR imaging. The effectiveness and efficiency of the proposed autofocusing algorithm are demonstrated by using several numerical experiments.

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

The U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory (ARL) has been investigating the capability of using 3-D millimeter-wave synthetic aperture radar (SAR) technology to provide navigation for aircraft through terrain and obstacles in a degraded visual environment (DVE). One of the key challenges associated with this technology is the focus quality of the 3-D millimeter-wave SAR image, which requires very-high-accuracy data from the positioning measurement system for the platform motion in the 3-D space. The level of accuracy needed is currently not achievable by state-of-the-art positioning measurement systems. Thus, an effective autofocus algorithm is critical for the success of this program. In this paper, we propose an image metric-based autofocus algorithm to address the SAR image focusing challenge associated with the Ka frequency band, our MIMO radar configuration and forward-looking 3-D geometry. The algorithm models the errors in the measured radar positioning data as the rotation and translation parameters of the MIMO antenna frame in 3-D space. The algorithm solves an optimization problem that maximizes the specified SAR image metric. We evaluate the proposed algorithm and compare its performance with the Phase Gradient Autofocus (PGA) algorithm using both linear side-looking SAR and our proposed forward-looking 3-D SAR geometries.

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.

This work targets the problem of drivable path detection in poor weather conditions including on snow covered roads. A successful drivable path detection algorithm is vital for safe autonomous driving of passenger cars. Poor weather conditions degrade vehicle perception systems, including cameras, radar, and laser ranging. Convolutional Neural Network (CNN) based multi-modal sensor fusion is applied to path detection. A multi-stream encoder-decoder network that fuses camera, LiDAR, and Radar data is presented here in order to overcome the asymmetrical degradation of sensors by complementing their measurements. The model was trained and tested using a manually labeled subset from the DENSE dataset. Multiple metrics were used to assess the model performance.

Pulse-compression thermography is an emerging technique that has shown versatility by combination of pulsed and lock-in thermography. Accordingly, several aspects of this technique are still unexplored, and some others not fully developed yet. Barker codes were widely used in radar applications due to their simplicity and their optimum autocorrelation function. Nevertheless, applications were limited by the amplitude of the sidelobes present in the autocorrelation function and therefore, several filters have been developed which aim to reduce the sidelobes. However, the filters usually depend on empirical parameters which must be determined for each application. A better alternative would improve the applicability of the Barker codes. In this work, we further develop the pulse-compression thermography technique by introducing a 13-bit modified Barker code (mBC): This allows to drastically reduce the sidelobes characteristic of the 13-bit Barker code (BC). Consequently, the thermographic impulse response, obtained by cross-correlation, is almost free of such sidelobes. Deeper defects become easier to detect in comparison with using a 13-bit Barker code. Numerical simulations using the finite element method are used for comparison and experimental measurements are performed in a sample of steel grade St 37 with machined notches of three different depths: 2 mm, 4 mm and 6 mm.

Localization, tracking, and classification requires precise recovery of the target signal amidst a sea of environmental noise and reflections from terrain and buildings. In this work, we use machine learning (ML), specifically generative-adversarial networks (GANs), to remove a range of synthetic additive noise and convolutional reverberations from a well-defined subset of sounds, namely English speech. Unlike simple denoising autoencoders, GANs attempt to generate realistic solutions, suppressing the production of unresolved (i.e., blurry) mixed inferences. We demonstrate that our models can dynamically resolve varying amounts of noise and convolution, which will be important in the field, where the amount and type of signal degradation will generally be unknown. While we focus only on speech here, ML-based deconvolution can also be applied to the restoration of images, radar, radio, and acoustic and seismic sensing distorted by weather, interference, and reflections.

There are various scenarios, whether they be commercial or defense, where privacy is important. In communications, the metrics of low probability of interception is often used to measure the signal’s ability to resist interception and decoding by unauthorized parties. Joint radar sensing and communications (RadarCom) has been of interest recently and an important requirement of RadarCom signals is its immunity to interceptions. In this context it is of interest to understand the statistics of background clutter. This paper uses machine learning (ML) approaches to classify and model clutter in presence of noise/interference. We employ 32 sub-carrier orthogonal frequency division multiplexing waveforms as a basis for clutter return collection and subsequent use as RadarCom signals. We then present the ML combination method with the best classification accuracy of 78.9%.

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.

According to the American Road and Transportation Builders Association (ARTBA), 46,052 of America’s 616,087 bridges are rated “structurally deficient” and need urgent repairs. The detection of damages through conventional methods, such as visual inspection and hammer tests are expensive, time-consuming, and challenging to perform without interfering with traffic operations. In the last years, different Non-Destructive Evaluation (NDE) techniques such as computer-vision-based crack detection, impact echo, ultrasonic surface waves, electrical resistivity, ground-penetrating radar, and infrared thermography (IRT) have been developed to inspect aging structures. Among all, IRT has shown the capabilities of detecting defects resulting in different temperature distribution. It can be useful to identify sub-surface damages as delamination and water infiltration, hardly detectable using other traditional methods. In this paper, an algorithm to automatically detect damages in bridges from IR images is proposed. The algorithm exploits the temperature difference between damaged and undamaged parts through machine learning and computer vision techniques to highlight the location of flaws in the structure. Laboratory experiments and real-world analysis on in-service bridges are described in this research to validate the proposed method's accuracy. This study aims to automate the damage detection phases on large-scale structures

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.

As the ultra-high frequency nature of lightwave corresponds to great potential in wideband signal processing, nextgeneration electronic information systems of surveillance, radar and communications is promised with photonic signal processing systems. However, the sophisticated photonic systems suffer from various hardware defects, which severely limit the performance of signal processing. By introducing the emerging deep learning technology into the photonic system, the hardware defects can be recovered by the trained neural networks. Using different modified neural networks, we have demonstrated high-accuracy photonic analog-to-digital converters, Brillouin instantaneous frequency measurement, and high-fidelity photonic radar receivers. The demonstrated systems with simple configurations can outperform the conventional photonic processing system with complex configurations. Note that the adoption of neural networks may cause additional time delay to the signal flow. Photonic neural network accelerators (PNNs) become a promising solution to realize real-time signal processing. We propose and experimentally demonstrate several system architectures of photonic convolutional neural networks. The photonic dot product unit architecture implements the basic operation in convolution neural networks. And an optical patching scheme is demonstrated to enhances the power efficiency of the input ports in PNNs. Performance evaluations show that the proposed PNN architectures possess potential advantages of energy efficiency and computational power. We believe that, by combining the technical advantages of photonic signal processing and PNN acceleration, intelligent photonic signal processing systems with high-performance real-time wideband signal processing capabilities can be realized. Moreover, the large-scale photonic integration technology promises the fabrication of such hybrid systems in the future.

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.

In this paper, the effects of multi-radiators on the electromagnetic scattering characteristics of coated flat plate in half space are investigated. The electromagnetic scattering characterization of coated flat plate are represented by active radar cross section (ARCS) under multi-radiators. Simulations were conducted to research the effect of radar absorbing materials (RAM) and multi-radiators on the electromagnetic scattering characteristics of flat plate in half space. Through the resultants, it can be concluded that the electromagnetic scattering characteristics are significantly influenced by RAM and multi-radiators.

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.

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.

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.

Microwave frequency measurement (MFM) is to estimate frequencies of intercepted microwave signals, which is critical to modern military and civil radio-frequency (RF) systems, such as wireless communications, electronic countermeasure (ECM), radar warning and electronic intelligence systems. In this paper, a photonic-assisted MFM method based on harmonic down-conversion with semiconductor optical amplifiers (SOAs) is proposed. Two optical harmonic intensifiers consisting of an electro-optic intensity modulator and a SOA are used to generate high-order optical harmonics based on cascaded four-wave mixing in the SOA, which has low-frequency and tunable spacing. It enables ultra-wide harmonic down-conversion of microwave signals under test in the electrical domain with low-frequency local oscillator (LO). The microwave frequency is therefore unequivocally determined by cross-referencing two pairs of harmonic down-converted tones within the LO frequency. It enables multi-tone frequency measurement and eliminates the trade-off between the measurement range and frequency-resolution. Moreover, it avoids the limitation of deadband by the cross-referenced frequency discrimination.

The Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) is a Japanese-European collaborative earth observation satellite mission. The Cloud Profiling Radar (CPR) is one of the EarthCARE instruments and it is the world's first on-board millimeter-wave Doppler radar in space. The Japan Aerospace exploration agency (JAXA) is responsible for development of the CPR in this mission in cooperation with the National Institute of Information and Communications Technology (NICT). This paper mainly describes the development status and performance of EarthCARE CPR Proto Flight Model (PFM).