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The NASA Orbital Debris Program Office (ODPO) develops, maintains, and updates orbital debris environmental models, such as the NASA Orbital Debris Engineering Model (ORDEM), to support satellite designers and operators by estimating the risk from orbital debris impacts on their vehicles in orbit. Updates to ORDEM utilize the most recent validated datasets from radar, optical, and in situ sources to provide estimates of the debris flux as a function of size, material density, impact speed, and direction along a mission orbit. On-going efforts within the NASA ODPO to update the next version of ORDEM include a new parameter that highly affects the damage risk – shape. Shape can be binned by material density and size to better understand the damage assessments on spacecraft. The in situ and laboratory research activities at the NASA ODPO are focused on cataloging and characterizing fragments from a laboratory hypervelocity-impact test using a high-fidelity, mock-up satellite, DebriSat, in controlled and instrumented laboratory conditions. DebriSat is representative of present-day, low Earth orbit satellites, having been constructed with modern spacecraft materials and techniques. The DebriSat fragment ensemble provides a variety of shapes, bulk densities, and dimensions. Fragments down to 2 mm in size are being characterized by their physical and derived properties. A subset of fragments is being analyzed further in NASA’s Optical Measurement Center (OMC) using broadband, bidirectional reflectance measurements to provide insight into the optical-based NASA Size Estimation Model. Additionally, pre-impact spectral measurements on a subset of DebriSat materials were acquired for baseline material characterization. This paper provides an overview of DebriSat, the status of the project, and ongoing fragment characterization efforts within the OMC.

To make a better discrimination between target and false alarm, amplitude information (AI) of radar signal has been incorporated into many target tracking algorithms, such as probabilistic data association, multiple hypothesis tracking and probability density hypothesis (PHD). In this paper, we present the cubature integration based Gaussian mixture implementation of the PHD filter with AI, namely the GMPHD-AI filter. Since the utilization of multiple sensors is more effective than using only one sensor, we also extend the GMPHD-AI filter to the multi-sensor application where the iterated-corrector scheme is exploited for multi-sensor information fusion. The effectiveness of the presented method is demonstrated by a multiple targets tracking scenario.

In order to improve the accuracy of hail forecasting for mountainous and plateau areas in China, this study presents a novel fusion forecast model based on machine learning techniques. Specifically, known mechanisms of hail formation and two newly proposed elevation features calculated from radar data, sounding data, automatic station data, and terrain data, are firstly combined, from which a hail/short-duration heavy rainfall (SDHR) classification model based on the random forest (RF) algorithm is built up. Then, we construct a hail/SDHR probability identification (PI) model based on the Bayesian minimum error decision and principal component analysis methods. Finally, an “and” fusion strategy for coupling the RF and PI models is proposed. In addition to the mechanism features, the new elevation features improve the models’ performance significantly. Experimental results show that the fusion strategy is particularly notable for reducing the number of false alarms on the premise of ensuring the hit rate. A comparison with two classical hail indexes shows that our proposed algorithm has a higher forecasting accuracy for hail in mountainous and plateau areas. All 19 hail cases used for testing could be identified, and our algorithm is able to provide an early warning for 89.5% (17 cases) of hail cases, among which 52.6% (10 cases) receive an early warning of more than 42 minutes in advance. The PI model sheds new light on using Bayesian classification approaches for high-dimensional solutions.

Environmental perception is a key technology for autonomous driving. Owing to the limitations of a single sensor, multiple sensors are often used in practical applications. However, multi-sensor fusion faces some problems, such as the choice of sensors and fusion methods. To solve these issues, we proposed a machine learning-based fusion sensing system that uses a camera and radar, and that can be used in intelligent vehicles. First, the object detection algorithm is used to detect the image obtained by the camera; in sequence, the radar data is preprocessed, coordinate transformation is performed, and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed. The proposed fusion sensing system was verified by comparative experiments in a real-world environment. The experimental results show that the system can effectively integrate camera and radar data results, and obtain accurate and comprehensive object information in front of intelligent vehicles.

Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent decision-making1,2. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations3,4. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints5,6. While they accurately predict low-intensity rainfall, their operational utility is limited because their lack of constraints produces blurry nowcasts at longer lead times, yielding poor performance on rarer medium-to-heavy rain events. Here we present a deep generative model for the probabilistic nowcasting of precipitation from radar that addresses these challenges. Using statistical, economic and cognitive measures, we show that our method provides improved forecast quality, forecast consistency and forecast value. Our model produces realistic and spatiotemporally consistent predictions over regions up to 1,536 km × 1,280 km and with lead times from 5–90 min ahead. Using a systematic evaluation by more than 50 expert meteorologists, we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods. When verified quantitatively, these nowcasts are skillful without resorting to blurring. We show that generative nowcasting can provide probabilistic predictions that improve forecast value and support operational utility, and at resolutions and lead times where alternative methods struggle., A deep generative model using radar observations is used to create skilful precipitation predictions that are accurate and support real-world utility.

Since the advent of assisted reproductive technology (ART), the utilization of ART procedures has become increasingly popular among women seeking to establish pregnancy. Radiologists are therefore likely to encounter the various complications of ART therapy. The most common is ovarian hyperstimulation syndrome; others are multiple, ectopic, and heterotopic pregnancies. Ultrasonography is considered the initial modality to investigate ART complications, However, nonspecific symptoms might need the use of an additional imaging modality, such as computed tomography or magnetic resonance imaging, as a problem-solving tool. This article briefly discusses the steps involved in assisted reproduction. Its aim is to help radiologists become familiarized with the multimodality imaging features of the spectrum of ART-related complications. Their key imaging features and differential considerations are emphasized. This will facilitate the provision of precise and timely diagnoses, and aid the avoidance of fatal consequences.

A head-mounted display cannot cover an angle of visual field as wide as that of natural view (out-of-view problem). To enhance the visual cognition of an immersive environment, previous studies have developed various guidance designs that visualize the location or direction of items presented in the users’ surroundings. However, two issues regarding the guidance effects remain unresolved: How are the guidance effects different with each guided direction? How much is the cognitive load required by the guidance? To investigate the two issues, we performed a visual search task in an immersive environment and measured the search time of a target and time spent to recognize a guidance design. In this task, participants searched for a target presented on a head-mounted display and reported the target color while using a guidance design. The guidance designs (a moving window, 3D arrow, radiation, spherical gradation, and 3D radar) and target directions were manipulated. The search times showed an interaction effect between guidance designs and guided directions, e.g., the 3D arrow and radar shorten the search time for targets presented at the back of users. The recognition times showed that the participants required short times to recognize the details of the moving window and radiation but long times for the 3D arrow, spherical gradation, and 3D radar. These results suggest that the moving window and radiation are effective with respect to cognitive load, but the 3D arrow and radar are effective for guiding users’ attention to necessary items presented at the out-of-view.

Sequences with low correlation have important applications in communications, radar, cryptography, and also in compressed sensing. The ultimate objective of this paper is to design a new family of polyphase sequences with low correlation. Our main contribution is the construction of such a family using additive and multiplicative characters over Galois rings. The proposed sequences have lengths N = pm − 1, family size M = pkm − 1, and a maximum magnitude $\theta _{\max \limits }=p^{k-1}\sqrt {p^{m}}$ , where k is an integer with 1 ≤ k < m.

Countries are increasingly interested in spacecraft surveillance and recognition which play an important role in on-orbit maintenance, space docking, and other applications. Traditional detection methods, including radar, have many restrictions, such as excessive costs and energy supply problems. For many on-orbit servicing spacecraft, image recognition is a simple but relatively accurate method for obtaining sufficient position and direction information to offer services. However, to the best of our knowledge, few practical machine-learning models focusing on the recognition of spacecraft feature components have been reported. In addition, it is difficult to find substantial on-orbit images with which to train or evaluate such a model. In this study, we first created a new dataset containing numerous artificial images of on-orbit spacecraft with labeled components. Our base images were derived from 3D Max and STK software. These images include many types of satellites and satellite postures. Considering real-world illumination conditions and imperfect camera observations, we developed a degradation algorithm that enabled us to produce thousands of artificial images of spacecraft. The feature components of the spacecraft in all images were labeled manually. We discovered that direct utilization of the DeepLab V3+ model leads to poor edge recognition. Poorly defined edges provide imprecise position or direction information and degrade the performance of on-orbit services. Thus, the edge information of the target was taken as a supervisory guide, and was used to develop the proposed Edge Auxiliary Supervision DeepLab Network (EASDN). The main idea of EASDN is to provide a new edge auxiliary loss by calculating the L2 loss between the predicted edge masks and ground-truth edge masks during training. Our extensive experiments demonstrate that our network can perform well both on our benchmark and on real on-orbit spacecraft images from the Internet. Furthermore, the device usage and processing time meet the demands of engineering applications.

Detection of lane region under the road boundary is an imperative module for intelligent vehicle system. Lane markings provide separate regions on the road for the vehicles to avoid the possibility of accidents. Existing methods in lane detection have limited performance using various sensor-based approaches such as Radar and LiDAR and have high operational costs. To achieve a steady and optimal lane detection, the vision-based lane region detection scheme VLDNet is proposed which utilizes a encoder-decoder network using semantic segmentation architecture. In this direction, a hybrid model using UNet and ResNet has been adopted, where UNet is used as a segmentation model and ResNet-50 is used for down-sampling the image and identifying the required features. These identified features have been then applied into UNet for up-sampling and decoding the segments of the images. The publicly available KITTI dataset have been accessed for experiments and validation of the proposed network. The method outperforms the existing state-of-the-art methods in lane region detection. The network achieves better performance using standard evaluation measures such as accuracy of 98.87%, the precision of 98.24%, recall of 96.55%, frequency weighted IoU of 97.78%, and MaxF score of 97.77%.

Natural disasters like floods are happening worldwide. Due to their negative impact on different social, economic and environmental aspects need to monitor and map these phenomena have increased. In fact, to access the zones affected by the flood, we use open source remote sensing (RS) images acquired by optical and radar sensors. Furthermore, we present a method using Sentinel-1 images; we suggest applying Ground Range Detected (GRD) images. For this purpose, pre-processed built and provided by the European Space Agency (ESA), preserved by free software Sentinel Application Platform (SNAP) for data extraction around appropriate demand. Moreover, the principal objective of this article is to assess the capability of Sentinel-1 Synthetic Aperture Radar (SAR) images in order to visualize flood areas in the Inaouene watershed located in north-eastern of Morocco. The origin of this natural hazard is the combination of natural and anthropogenic factors that makes the watershed vulnerable with a sub-annual frequency. The results of this work help decision-makers and managers in the field of natural risk management and land-use planning to implement a strategy and action plan for the protection of the populations and the environment against the negative impact of floods.

Monitoring and estimating rainfall for river basins in developing countries, such as Thailand, during severe storms are important and challenging studies due to the need for high spatiotemporal information of rainfall estimates. There is no end-software to produce rainfall estimate products for the basin because sophisticated systems and algorithms are needed to manipulate spatiotemporal data. Radar-based quantitative precipitation estimation (RQPE) provided by ground-based weather radar is considered to be suitable data for monitoring rainfall over a basin because its system can observe a rainfall event with high resolution of spatiotemporal scanning. This study presents the implementation of mosaicked RQPE from two stations over the Yom River basin in the northern part of Thailand. The study was performed using free and open source for a geographic information system (GIS) software with developing Python programming language in QGIS (PyQGIS) during the severe storm of Sontihn that occurred in July 2018. Using four-times-per-hour radar scanning, the RQPE mosaicking products were created over the basin by developing an algorithm in PyQGIS. The efficiency of the Z-R relationship was evaluated over the basin with multitemporal aggregation in the validation process between the mosaicked RQPE and rainfall observed at gauges. The Rosenfeld Z-R relationship produced the best performance based on validation statistics of the Marshall-Palmer and Summer Deep Tropical Convection Z-R relationship. Spatial mean field bias using the Rosenfeld Z-R relationship was greatly reduced during the storm evolution of Sontihn compared with the other two Z-R relationships. However, the RQPE based on instantaneous observations using the Marshall-Palmer Z-R relationship performed the best for rainfall estimation compared with the others.

The last decade has witnessed important advancements in the field of computer vision and scene understanding, enabling applications such us autonomous vehicles. Radar is a commonly adopted sensor in automotive industry, but its suitability to machine learning techniques still remains an open question. In this work, we propose a neural network (NN) based solution to efficiently process radar data. We introduce RadarPCNN, an architecture specifically designed for performing semantic segmentation on radar point clouds. It uses PointNet$$++$$ + + as a building-block—enhancing the sampling stage with mean-shift—and an attention mechanism to fuse information. Additionally, we propose a machine learning radar pre-processing module that confers the network the ability to learn from radar features. We show that our solutions are effective, yielding superior performance than the state-of-the-art.

Coal mining has an important influence on the soil water content in the vadose zone. In this study, during April, August and October 2015 and June 2016, ground-penetrating radar was used to detect the soil water content of the shallow sand layer (

In view of the disadvantages of vibration safety monitoring technology for offshore wind turbines, a new method is proposed to obtain deformation information of towering and dynamic targets in real-time by the ground-based interferometric radar (GBIR). First, the working principle and unique advantages of the GBIR system are introduced. Second, the offshore wind turbines in Rongcheng, Shandong Province are selected as the monitoring objects for vibration safety monitoring, and the GPRI-II portable radar interferometer is used for the health diagnosis of these wind turbines. Finally, the interpretation method and key processing flow of data acquisition are described in detail. This experiment shows that the GBIR system can accurately identify the millimeter-scale vibration deformation of offshore wind turbines and can quickly obtain overall time series deformation images of the target bodies, which demonstrate the high-precision deformation monitoring ability of the GBIR technology. The accuracy meets the requirements of wind turbine vibration monitoring, and the method is an effective spatial deformation monitoring means for high-rise and dynamic targets. This study is beneficial for the further enrichment and improvement of the technical system of wind turbine vibration safety monitoring in China. It also provides data and technical support for offshore power engineering management and control, health diagnosis, and disaster prevention and mitigation.

Radar technologies of signal processing have been used to implement a highly effective new proprietary technique for studying the bioelectric activity of the heart — ultra-high resolution electrocardiography. Its implementation involves synthesis of optimal and sub-optimal algorithms and structures for identifying components of the useful electrocardiac signal. The circuitry, algorithms, and software for the selection and processing of new ECG markers of development of cardiovascular system pathologies have been developed.

Object detection using FMCW (Frequency-modulated continuous wave) radars is of massive importance for the advanced driver assistance systems. However, it is exceptionally challenging due to the diversity of the electromagnetic environment and the existence of the class imbalance in the radar data space. In this paper, we propose a cascaded object detection network to achieve accurate object detection using FMCW radars. Consisting of a ROI generation stage and a final detection stage, the proposed cascaded network can tackle the problem of the class imbalance and detect objects from the range-Doppler or range-velocity space effectively. Besides, we propose a range-velocity regression procedure to improve the performance of the range-velocity localization. Extensive simulation experiments demonstrate that our proposed approach can robustly detect objects from noisy electromagnetic environments with a high localization accuracy.

The study of asteroids interior is crucial to the understanding of their accretion in the early Solar System. Furthermore, space-borne sounding radar is the most mature technique to image asteroid’s internal structure. The low-frequency radar is a bistatic radar measuring the radar wave propagation between a Lander and an Orbiter through the asteroid. Time accuracy and frequency stability are fundamental for the success of such an instrument. Furthermore, accuracy in the propagation delay measurement is a way to increase science return. This work studies the impact the drift of the clocks located on the two platforms has on the accuracy of the time measurement while taking into account the on-board processing. We present the time analysis of such a radar by developing a behavior model of the system for a long time scale which is implemented in a software simulator. A clock test bench is then developed to provide simulator inputs from true oven controlled crystal oscillators clocks under selection for the mission. The simulator coupled to the test bench allows us to better quantify the impact of the time errors on the measurement, to support clock selection and to improve science return.

Metasurfaces are ultrathin, two-dimensional structures composed of periodic or quasi-periodic arrays of sub-wavelength scatterers. They possess the unique ability to comprehensively control the phase, amplitude and polarization of incident electromagnetic waves with added advantages such as ease of fabrication and less space consumption. On account of these factors, they are progressively replacing their three-dimensional counterparts, i.e. metamaterials in a wide gamut of fields such as signal multiplexing, stealth technology, holographic imaging, planar optical devices, polarization transformation devices and so on. Further, metasurfaces offer a strong and promising platform for aerospace applications due to their diversified functionalities and reduced weight penalties. Moreover, it has been widely used for the realization of thin, broadband and polarization independent radar absorbing structures (RAS). In this regard, this paper presents a concise review on the recent advancements in the field of metasurfaces specifically for stealth applications. Special emphasis has been laid on diffusion and coding metasurfaces due to their attractive properties towards the realization of low observable platforms. Furthermore, various types of metasurfaces as well as the different techniques used for the optimization of metasurfaces are also described in detail.

The present paper addresses numerical calculations on the eigenvalues of hybrid modes in corrugated circular waveguides with varying diameter and corrugation depth. Such calculations are essential for the numerical optimization of advanced mode converters and diameter tapers for future low-loss high-power microwave applications, like broadband high-power radar sensors for space debris observation in low earth orbit (LEO). Corresponding mode converters and diameter tapers may be synthesized based on coupled mode theory. Of particular importance here is the ability to consider varying mode eigenvalues along the perturbed waveguide. The procedure presented here is able to consider arbitrary variations of the corrugation depth as well as the waveguide diameter and therefore is highly flexible. The required computational effort is low. Limitations of the method are discussed.

Antenna arrays have potential applications in Radar, mobile, broadcasting, astronomy and other defense and commercial platforms. The design of antenna arrays is essential as they are capable of controlling the radiation pattern in terms of sidelobe level (SLL), beamwidth (BW), nulls and beam steering. Among several geometries of antenna arrays, the circular antenna arrays have the advantage in terms of inherent capability to control the phase implicitly. In this paper, circular antenna array synthesis is presented using novel metaheuristic techniques like the Social Group Optimization Algorithm (SGOA) and Modified SGOA (MSGOA). The main objective of the synthesis process is to synthesize radiation patterns with suppressed SLL while the BW being equal to the uniform distribution. A comparative study is performed to analyze the performance of various synthesis techniques like amplitude only, inter-element spacing only, and amplitude spacing. The MGSOA and SGOA have proved to be performing well in accomplishing the objective. Further, the CAA synthesis problem is translated into single-variable and multivariable optimization problems following which the obvious advantage associated with the inclusion of additional variable as an additional degree of freedom is demonstrated. The simulation is carried out in Matlab®, and results are analyzed using the plotted radiation patterns.

Monitoring urban land through satellite images has rapidly developed with the advent of modern technologies, and the increasing number of satellites plays a contributing role. While optical images have a high capability in urban monitoring, they still have some limitations, including their dependence on climatic conditions and spectral information, which lead to difficulty in making a distinction between bare land, buildings and other features. The impossibility of optical imagery at night is another issue that can make the land cover classification difficult. Synthetic aperture radar (SAR) allows imaging in all climatic conditions and at nighttime, with an ability to detect phenomena based on their geometry, roughness, and location, making the land cover classification much easier. In the present study, radar Sentinel-1 images with polarization VV and VH were used for the land classification in Tabriz. Sentinel-2 images for the same time were applied as a reference for the calibration and accuracy assessment. Maximum likelihood (ML) and support vector machine (SVM) algorithms were also employed for supervised classification. In both algorithms, the classification was performed in windows with different sizes once by the SAR backscattering coefficient (σ0) and then by combining the backscattering coefficients with the statistical data obtained from the texture. The results showed that the use of radar images only with backscattering intensity resulted in poor performance while using the gray-level co-occurrence matrix (GLCM) of texture features increased the accuracy. The transmitted frequencies of radar images have different redistributions to different phenomena. The numerical results obtained from the radar image classification show that using only the radar image redistribution led to low accuracies at both VV and VH polarization, but the use of the textural analysis significantly increased the accuracy of the classifications. The statistical results obtained from the ML and SVM classifications for radar images at VV and VH polarization indicated that the latter performed better than the former. When texture analysis was not used in the classes, the classification accuracy was low with kappa values of 0.37 and 0.42 for VV and VH polarization, respectively. The use of texture analysis and obtaining the optimum window size is increase the classification accuracy with a better performance for VH polarization. The SVM classification method with a kappa coefficient of 0.72% showed better performance than the ML one with a kappa coefficient of 0.61%. Conclusively, in the absence of Sentinel-2 datasets, Sentinel-1 images are good alternatives if the preserved texture information is available for the land cover classification. Results of this research are of great importance for developing the remote sensing methods and their techniques can be considered as progressive research in the domain of remote sensing sciences.

Recently, the photonics-radar technology comes out as an attractive candidate in the arena of smart autonomous transportation, surveillance, and navigation-related applications owing to provide wide-spectra to attain improved and precise radar-resolutions. On the other hand, microwave radars, due to limited bandwidth, are incapable of coping with the demands of next-generation radar technology. Moreover, the atmospheric fluctuations become more prominent at higher frequencies and affect the radar’s performance significantly. Subsequently, the authors develop a 2 × 2 multi-input multi-output (MIMO) employed linear frequency-modulated continuous-wave coherent photonic-radar system (MIMO-Co-PHRAD) using OptiSystem™ and MATLAB™ to attain a prolonged detection-range with an enhanced visibility. The developed MIMO-Co-PHRAD is investigated with heterodyne- and homodyne-detection approaches under weak-to-strong regimes of the atmospheric fluctuations like Rain and Fog. A comparison is also drawn for both the demonstrated MIMO-equipped laser-driven coherent photonic-radar systems. The performance of both the developed MIMO-Co-PHRAD systems is evaluated by measuring the intensity of reflected-echoes, signal-to-noise ratio, and range-Doppler patterns. A contrast with the single-input single-output coherent photonic-radar (SISO-Co-PHRAD) is also established to validate the robustness of the demonstrated MIMO-Co-PHRAD.

Sensors are the quintessential part of Blind Spot Detection (BSD) systems, which have a profound effect on the performance of the system. Every sensor has its unique deficiencies that can deteriorate the performance of the system under grievous circumstances. Hence, making vital tasks in BSD such as object detection arduous. Indeed, previous studies have demonstrated that data fusion techniques can diminish the adverse effects of sensors and improve detection accuracy in the BSD system. One of the main advantages of data fusion is to improve detection accuracy and reduce the processing time by multiple sensors cooperation. We propose a BSD model that objects are detected in consecutive time intervals in the BSD system. Then, association techniques are employed for multi-sensor fusion since all sensors data are not ordinarily ready for fusion simultaneously. It should be noted that the orthodox approach in data association techniques in BSD often includes a global nearest neighbor, joint probabilistic data association, and multiple hypothesis tests. We simulate and compare these techniques by tracking multiple targets and multi-sensor fusion using virtual data in MATLAB. Furthermore, we illustrate that our multi-sensor fusion detection accuracy in the BSD system is augmented compared to a single sensor BSD system.

Radar target detection (RTD) is one of the most significant techniques in radar systems, which has been widely used in the field of military and civilian. Although radar signal processing has been revolutionized since the introduction of deep learning, applying deep learning in RTD is considered as a novel concept. In this paper, we propose a model for multitask target detection based on convolutional neural network (CNN), which works directly with radar echo data and eliminates the need for time-consuming radar signal processing. The proposed detection method exploits time and frequency information simultaneously; therefore, the target can be detected and located in multi-dimensional space of range, velocity, azimuth and elevation. Due to the lack of labeled radar complex data, we construct a radar echo dataset with multiple signal-to-noise ratio (SNR) for RTD. Then, the CNN-based model is evaluated on the dataset. The experimental results demonstrated that the CNN-based detector has better detection performance and measuring accuracy in range, velocity, azimuth and elevation and more robust to noise in comparison with traditional radar signal processing approaches and other state-of-the-art methods.

The method of speckle reduction is widely used in synthetic aperture radar (SAR) imagery over the last three decades. The SAR images are inherently speckled in nature. Speckle noise is a granular pattern distribution, usually modeled as a multiplicative noise that affects the SAR images, as well as all coherent images. Other SAR related problems are also discussed in this paper. Therefore, despeckling approaches are needed to improve the quality of SAR images. However, there is a trade-off between speckle reduction and the preservation of fine details in the despeckled SAR image. The reduction of the speckle noise without losing the fine details of the SAR image is a diffucult task. However, many despeckling methods have been discussed to reduce the speckle noise from the SAR images. Each method has their own norms, advantages and disadvantages. This article contains a review of some major work in the field of SAR image despeckling. Often, scientists and scholars have faced the struggle to understand the pattern distribution of the speckle noise in SAR images. Hence, a brief details about radar, SAR imaging, speckle noise in SAR images and the prevalent approaches of SAR image despeckling are reviewed here. The advantages and disadvantages of SAR image despeckling approaches are also analysed and discussed.

Autonomous driving has recently gained lots of attention due to its disruptive potential and impact on the global economy; however, these high expectations are hindered by strict safety requirements for redundant sensing modalities that are each able to independently perform complex tasks to ensure reliable operation. At the core of an autonomous driving algorithmic stack is road segmentation, which is the basis for numerous planning and decision-making algorithms. Radar-based methods fail in many driving scenarios, mainly as various common road delimiters barely reflect radar signals, coupled with a lack of analytical models for road delimiters and the inherit limitations in radar angular resolution. Our approach is based on radar data in the form of a two-dimensional complex range-Doppler array as input into a deep neural network (DNN) that is trained to semantically segment the drivable area using weak supervision from a camera. Furthermore, guided back propagation was utilized to analyse radar data and design a novel perception filter. Our approach creates the ability to perform road segmentation in common driving scenarios based solely on radar data and we propose to utilize this method as an enabler for redundant sensing modalities for autonomous driving. Self-driving vehicles must reliably detect the drivable area in front of them in any weather condition. An actively developed sensor approach is camera-based road segmentation, but it is limited by the visible spectrum. Radar-based approaches are a promising alternative and a new method extracts the drivable area from raw radar data by training a deep neural network using paired camera data, which can be labelled automatically using pretrained computer vision models.

In this paper, a real-time Monte Carlo localization (RT_MCL) method for autonomous cars is proposed. Unlike the other localization approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution. The RT_MCL method is based on the fusion of lidar and radar measurement data for object detection, a pole-like landmarks probabilistic map and a tailored particle filter for pose estimation. The lidar and radar are fused using the unscented Kalman filter (UKF) to provide pole-like static-object pose estimations that are well suited to serve as landmarks for vehicle localization in urban environments. These pose estimations are then clustered using the grid-based density-based spatial clustering of applications with noise algorithm to represent each pole landmark in the form of a source-point model to reduce computational cost and memory requirements. A reference map that includes pole landmarks is generated offline and extracted from a 3-D lidar to be used by a carefully designed particle filter for accurate ego-car localization. The particle filter is initialized by the fused GPS + IMU measurements and used an ego-car motion model to predict the states of the particles. The data association between the estimated landmarks by the UKF and that in the reference map is performed using the iterative closest point algorithm. The RT_MCL is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Extensive simulation studies have been carried out to evaluate the performance of the RT_MCL in both longitudinal localization and lateral localization.

A novel feature extraction method based on the energy cumulant of Choi-Williams distribution has been proposed, which copes with the complex electromagnetic environment and varied signal styles. The energy cumulant of Choi-Williams distribution has been calculated from the accumulations of each frequency sample value with different time samples. Preceding this procedure, the time frequency distribution via Choi-Williams distribution has been processed through base noise reduction. Henceforth, a simulation analysis based on the discriminability and recognition effect of the proposed features in a low SNR environment has been carried out. In the discriminability experiment, the kernel fuzzy C-means clustering algorithm has been employed for classifying the radar pulse modulated radiation source signals. In the recognition effect analysis, the deep belief network has been proposed to employ the input feature vectors for training to achieve the radar emitter recognition and classification. Simulation results demonstrate that the proposed feature extraction method is feasible and robust in radar emitter classification and recognition even at a low SNR.

Researches on radar and infrared stealth compatibility have drawn much attention in recent years. In this work, a flexible metasurface with low infrared emission, broadband microwave absorption as well as high optical transmission is simultaneously achieved. The whole structure is composed of an infrared shielding layer (IRSL), a radar absorption layer (RAL), a substrate and a backplane, and the total thickness is only 3.5 mm. Based on the impedance matching theory, the microwave absorption higher than 90% can be achieved in the radar waveband ranging from 7.3 to 18.8 GHz, corresponding to a relative bandwidth of 88.1%. By using a conductive patch array as the IRSL, a low infrared emissivity of 0.49 can be realized in the infrared region from 8 to 14 μm. Moreover, by rational designing both the structures and materials, the metasurface in this work cannot only achieve bi-stealth functions with low infrared emission and broad microwave absorption, but also shows optically transparent and flexible properties, thus quite suitable for practical applications. Both the simulated and experimental results suggest that the proposed metasurface is promising in the multispectral stealth fields.

Over the past few years, numerous technologies have emerged to enable safe and convenient driving. However, there still exist various problems autonomous vehicles should overcome. Precise detection and perception of surrounding environments are the essential foundations to overcome them. Consequently, many sensor fusion algorithms have been developed to handle more complex situations, with sensor manufacturers also making strenuous efforts to enhance sensor performance. Although Light Detection And Ranging(LiDAR) sensor generally outperforms other sensor types, they remain prohibitively expensive from car manufacturing companies perspective. Therefore, camera and radar sensors have been enhanced, and are starting to provide free space information, similar to LiDAR sensor data and somewhat different from target information they have previously provided. The aim of this paper was to utilize the free space information to improve track information for vehicles. We employ the probability model with two occupancy grid map (OGM) types, which are Bayesian theory and Dempster-Shafer theory based OGMs, to classify free space information states and to efficiently handle free space information. Final output from the proposed algorithm is the target vehicle’s compensated track. Experimental results verify superior performance compared with non-compensated algorithms.

This study undertook verification of the applicability and accuracy of wind data measured using a WindCube V2 Doppler Wind Lidar (DWL). The data were collected as part of a field experiment in Zhoushan, Zhejiang Province (China), which was conducted by Shanghai Typhoon Institute of China Meteorological Administration during the passage of Super Typhoon Lekima (2019). The DWL measurements were compared with balloon-borne GPS radiosonde (GPS sonde) data, which were acquired using balloons launched from the DWL location. Results showed that wind speed measured by GPS sonde at heights of 100 m) was found for DWL-measured wind speed time-averaged during the ascent of the GPS sonde from the ground surface to the height of 270 m (correlation coefficient: 0.82; root mean square (RMS): 2.19). Analysis revealed that precipitation intensity (PI) exerts considerable influence on both the carrier-to-noise ratio and the rate of missing DWL data; however, PI has minimal effect on the wind speed bias of DWL measurements. Specifically, the rate of missing DWL data increased with increasing measurement height and PI. For PI classed as heavy rain or less (PI 90 mm·h−1), only data below 100 m were valid. Up to the height of 300 m, the RMS of the DWL measurements was nearly half that of wind profile radar (WPR) estimates (4.32 m·s−1), indicating that DWL wind data are more accurate than WPR data under typhoon conditions.

Small structures in coal mine working face is one of the main hidden dangers of safe and efficient production in coal mine. Currently, seismic exploration is often used as the main method for detecting such structures. However, limited by the accuracy of seismic data processing and interpretation, the interpreted location of small structures is often deviated. Ground-penetrating radar (GPR) can detect small structures accurately, but the exploration depth is shallow. The combination of the two methods can improve the exploration accuracy of small structures in coal mine. Aiming at the 1226# working face of Shuguang coal mine, we propose a method of seismic-attributes based small-structure prediction error correction using GPR data. First, we extract the coherence, curvature, and dip attributes from seismic data, that are sensitive to small structures, then by considering factors such as the effective detection range of GPR and detection environment, we select two structures from the prediction results of seismic attributes for GPR detection. Finally, based on the relationship between the positions of small structures predicted by the two methods, we use statistical methods to determine the overall offset distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position. The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis. The accuracy of the prediction results is greatly improved, with the error controlled within 5 m and reduced by more than 80%. Therefore, the feasibility of the method proposed in this study is verified.

Due to the fast development of various wireless devices and radar detection technology, the microwave absorbing composites have been developed to solve the problem of electromagnetic pollution and radar stealth by transferring microwave energy into thermal energy. Given that, the morphology is a key factor which can influence the absorbing performance of absorbers to a large extent. Recently, abundant investigations on fabrication of microwave absorbers with miraculous morphologies and microstructures have been reported. This review aims at summarizing the recent progress of magnetism-based microwave absorbing composites from perspective of several typical morphologies, including core-shell structure, layered structure, porous structure, polyhedral structure, flower-like structure and coral/needle-like structure etc. Firstly, the influential factors of electromagnetic absorption ability in materials and their relationship with morphology have been introduced briefly, then the synthesis methods, microwave absorption properties, and electromagnetic absorbing mechanisms of these composites in each morphology are discussed in detail. Moreover, the promising prospects of magnetism-based microwave absorbing composites with different morphologies are also proposed.

Non-destructive testing techniques are highly valuable in the present day scenario as it can save money and time for the qualitative evaluation of any site. This paper presents a case study utilizing one such technique, employing a ground-coupled Ground-Penetrating Radar (GPR). The GPR was used to estimate the damages to the pavement and underlying subgrade soil at Cochin International Airport Limited (CIAL), Cochin, Kerala, India. The state of Kerala received 75% more than expected rainfall in the monsoon of 2018 and hence, faced one of the worst natural disasters in the form of floods between July and August. Therefore, a subsurface survey was performed at CIAL to analyse the pavement area and identify any detrimental effects caused by the floods. The subsurface survey scope was to image the subsurface to an approximate depth of 2–3 m to map the anomalies and quantitatively evaluate the affected areas of the runway, taxiways, and the link-ways. Non-destructive testing was opted not to affect the regular operations at the airport. This investigation involved the use of remote measurement methods, and therefore, all the findings presented here are the result of the measurement and interpretation of GPR data. The analysis was performed using a dedicated data analytics software called RADAN7. Based on the analysis, a quantitative criterion called the “stripping index” has been computed to define the degree of damages sustained by the pavement and its underlying layers. The results are both graphically and quantitatively discussed in this paper.

High-intensity coal mining activities in China’s western mining area are damaging fragile ecological environments. The key to coordinating ecological protection and coal mining is to understand the law of overlying strata migration in high-intensity mining. However, it is difficult for conventional monitoring methods to efficiently monitor overburden movement in high-intensity mining areas in real time. In this paper, a space-sky-surface (3S) integrated system is proposed to conquer this challenge. This monitoring system consists of three parts: a space monitoring component (a combination of a global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR)), a sky observation component (an unmanned aerial vehicle (UAV) equipped with a measuring camera (MC)), and a surface exploration component (ground-penetrating radar (GPR) and high-density electricity method (HDEM)). This system was successfully applied to the 12,401 longwall face in the Shangwan coal mine (the maximum mining height is 8.8 m). The deformation cloud map of the entire mining area was obtained through InSAR monitoring technology accompanying the 3D laser scanning to improve the accuracy in the local region of 12,401 longwall face. The distribution characteristics and dynamic evolution of surface fractures were obtained using UAV and MC (visible light camera + infrared camera). Using the HDEM and GPR technologies, the fracture development was well detected. Based on the strata movement theory, combined with the monitoring results, a three-dimensional model of overburden rock fracture after 12,401 longwall face mining was constructed. Monitoring results and the fracture model provide a basis for controlling air leakage in the goaf and remediation of surface fracture.

Today’s world is contemplating to set the new developments in radar to face the security challenges in defense. In the scope of radar signal processing, technical expertise in the signal processing, coding theory and techniques brought a lot of exposers to improve the detection probability in terms of range and position. The present art of work focusing on the detection of multiple moving targets using Doppler radar. Though the existing approaches try to increase the Merit Factor and range resolution of the acquired Signal, As such methods fail to find out the target when they are in multiple and moving, due to which the amplitude of the side spikes (Noise) is much more with respect to the highest detectable limit. Those side spikes dominate the probability of detection of a target because the weak echoes from the small targets may be masked by these side spikes of huge moving targets. This recommended approach provides clear information about the targets with respect to Doppler by creating multiple clear windows at various Doppler's with respect to the range. The amplitude of all windows is below 85–90 dB down, so all moving targets can be easily detected. This approach is validating by the use of Mat lab.

With the rapid advancement of technology of unmanned aerial vehicles (UAVs), security and safety of military and civil infrastructure have been jeopardized. By exploiting the unreliable capabilities of radar to detect low-flying UAVs with small radar cross section, they can be utilized for malicious purposes, e.g., unauthorized surveillance. To detect UAVs, therefore, various other techniques including audio sniffing/analysis of environment have been investigated. It has been shown recently that a UAV can be differentiated from other sound generating objects based on the various features extracted from the sound captured by a single or multiple acoustic sensors. However, features extraction and classification process can only give reliable results if it is fed with a sound generated by a single source. In practice, the captured sound may be a mixture of contribution of two or more different sources. In this paper, we investigate a well-known blind source separation technique, known as projection pursuit, to separate the constituent sounds in a mixture. We have considered a scenario when the different mixed unvoiced sounds are independent and non-Gaussian. The results show that in the given scenario, projection pursuit can be applied successfully to separate UAV sounds from various other unvoiced sounds.

We have designed a polarization conversion coding metasurface (CM) for broadband radar cross-section reduction. The CM is made up of a kind of deformed bowtie-shaped particle and its mirror, named “0” and “1”, respectively, which can realize cross-polarization conversion under the vertical incidence of a linearly polarized wave. According to the coding sequence optimized by the genetic algorithm, the two kinds of unit are arranged to form a design, called a 1-bit CM, which can scatter electromagnetic waves in various directions. The simulation results demonstrate that the two kinds of unit can achieve more than a 90% polarization conversion ratio, and the whole CM can realize more than a 10-dB reduction of monostatic radar cross-section (RCS) from 8.8 GHz to 20.9 GHz, while the maximum RCS reduction of 32.5 dB can be obtained at 14 GHz under the normal incidence of x- and y-polarized waves. The experimental results are almost identical to the simulation. Thus, we believe that the designed 1-bit CM can be well applied for the stealth business.

We have observed some discrepancies between installed geodetic instruments including global positioning systems (GPS) and very long baseline interferometry (VLBI) in the Medicina Geodetic Observatory (MGO). This study overcomes the above-mentioned discrepancies. We analysed several CosmoSkyMed images utilizing the interferometric synthetic aperture radar (InSAR) technique to improve the understanding of 3D surface displacements. The MGO is in a rural area, and the radar coherence is low; therefore, the coverage of permanent scatterers (PS) is limited. In ascending mode, the closest scatterer to the MEDI GPS station shows that the surface displacements are as large as 0.72 ± 1.45 mm/year along the line of sight (LOS) direction. The GPS height projected on the LOS vector shows a rate of 0.92 ± 0.04 mm/year of displacements. In descending mode, the closest scatterer to MEDI shows that the displacement is at a rate of − 0.1 ± 0.51 mm/year along the LOS direction, and the MEDI projected on the LOS vector is at a rate of 0.91 ± 0.002 mm/year. In this study, the deformation rates and their standard deviations stress that the GPS and PS-InSAR have different values. Despite a large (32-m-diameter parabolic antenna) VLBI telescope, we do not observe a single PS on the telescope for ascending and descending passes due to the frequent movements of the VLBI telescope in different directions.

This paper examined the attenuation method, which is used abroad, to derive a method to evaluate the condition of bare concrete bridge decks using ground penetrating radar (GPR) technology. For this purpose, 12 GPR surveys of 10 bridge decks in public service from the beginning of its service to 25 years of service were carried out to examine the attenuation characteristics of GPR signals under various concrete conditions. The survey revealed the signal below the top rebar of concrete bridge deck under the condition of using de-icing chlorides for snow removal was not clear. Therefore, using the receiving wave signal from top rebar was reasonable when attenuation of GPR signal was to be used for bridge deck evaluation. Examining the signal attenuation by the condition of concrete, dispersion of the attenuated signal was overall large in the initial performance period. However, since this is not due to deterioration, exclusion of the large dispersion from the evaluation of bridge deck condition was desirable. The attenuation size was linearly proportional to its two way travel time (signal transmission time), i.e., the depth of the top rebar, in a sound bridge deck. Also, the dispersion of the attenuation was small and symmetric to the linear regression line. If the bridge deck was maintained adequately, the linear regression correlation was also maintained similarly for the next several years. However, if deterioration occurred to be accompanied by increased attenuation and dispersion, correlation coefficient of the linear regression line declined. Nonetheless, the upper value of the attenuation representing sound concrete condition was still tended to be linearly proportional to two way travel time. Since the attenuation quantity could result in considerable debilitation just by the construction error, it is necessary to deal with the depth-error of top rebar for evaluating the concrete condition by using the GPR signal attenuation. Calibration was carried out by deriving a linear regression line for the signal two way travel time and the upper 90th percentile values of the attenuation obtained from the top bar position of the bridge and then removing it from the total attenuation.

This paper considers two methods for improving the visibility of hidden objects in radar tomography using nonlinear time transformations of short ultra-wideband (UWB) pulses. The first method is based on selection of a digital coherent jitter formed by special nonlinear signal transformation. The resulting increase in the contribution of high-frequency components to the reflected signal spectrum improves the spatial resolution of images of hidden objects. The second method uses relatively powerful monochromatic side irradiation in the clocked mode to detect a target. This leads to a characteristic distortion of the radar sensing pulse reflected from nonlinear radio-electronic elements – parts of the target being located. The observed change in the radar pulse waveform allows selective tomography of nonlinear radio-electronic elements to be carried out.

Nonlinear acoustic nondestructive evaluation (NA NDE) methods have a higher sensitivity for defect detection than standard methods. These methods use various kinds of acoustic wave interactions. In this paper, we suggest augmenting the acoustic framework and use the interaction between acoustic and electromagnetic waves. A brief review of NA NDE and medical nonlinear acoustic imaging methods is presented. Medical methods based on electromagnetic wave modulation by an acoustic radiation force are discussed where improvements using ultrasound contrast agents are suggested. The estimation of the modulation of a radar signal by a crack vibration were made based on standard static measurements. The effects of modulation of an acoustic wave by an electromagnetic field are briefly considered for the method of crack detection in metal materials. The effects considered in this paper may be used in the new methods of NDE and medical diagnostics.

An inherent disadvantage of a Microstrip Patch Antenna (MPA) is its narrow bandwidth and low gain. There are various techniques in the market that either enhances gain or bandwidth of the MPA. The proposed system uses a novel technique which proves to enhance both the bandwidth, as well as the gain up to a great extent, simultaneously. We have tried to overcome these limitations by inserting slots in the proposed Rectangular Microstrip Patch Antenna (RMPA), also known as the slotted array technique. The proposed antenna has been designed for an operating frequency of 9 GHz which lies in the X-band of an electromagnetic system. It has been simulated over an RT Roger/duroid 5880 material that has a dielectric constant of 2.2, using HFSS software. It was observed that the RMPA has a bandwidth of 425.2 MHz, whereas, a bandwidth of 920 MHz has been achieved for slotted RMPA, that means 494.8 MHz of bandwidth enhancement. In addition to this, a gain of 6.92 dB has been achieved for RMPA, whereas, by introducing slots, the gain becomes 19.88 dB, i.e., an enhanced value of 12.96 dB has been achieved. This antenna can be used in various wireless applications such as: Wi-Fi, Bluetooth, and wireless LAN, and satellite and radar communication.

Acoustic interference of atmosphere has been an attractive research area because of its potential effect on environment, water resources, ecology, agriculture, and other areas. However, it is also a controversial topic because of the difficulty of quantitative assessment and high operating costs. In this study, a novel acoustic interference technology is proposed that uses strong low-frequency sound waves. There is no chemical pollution or dependence on airborne vehicles, and it can be remotely controlled at low cost. A complete equipment system for acoustic atmospheric interference technology is established, based on which a series of experimental studies on cloud and precipitation response under acoustic action are performed, mainly including the radar echo intensity, cloud microphysical characteristics and the spatial distribution of ground rainfall intensity. The trigger and periodic effect of the acoustic waves on the cloud are proposed to be the key responses of acoustic atmospheric interference. This study is important to further research on atmosphere interference technology based on low frequency strong sound waves.

The relationship between precipitation intensity and cloud water in typhoon systems remains unclear. This study combined time- and space-synchronized precipitation and spectral data obtained by the Precipitation Radar (PR) as well as the Visible and Infrared Scanner (VIRS) onboard the TRMM satellite, to overcome the limitations of precipitation properties and cloud parameters not being synchronized in previous studies. A merged dataset of near-surface rain rate (RR) and corresponding cloud water path (CWP) was established and used to analyze the potential correlation between cloud microphysical properties and precipitation, to deepen our understanding of the evolution of cloud to rain. In addition, 25 collocated satellite overpasses of mature typhoon cases in the Northwest Pacific Ocean from 1998 to 2012 were obtained, and the relationships between the CWP and RR of 144 515 pixels were analyzed in detail. The results show that the CWP and RR of mature typhoon systems with different precipitation types, precipitation cloud phases, and vertical depths of precipitation can be fitted by a notable sigmoid function, which may be useful for estimating CWP and parameterizing precipitation in models. Furthermore, the relationship was applied and tested with an independent sample to show that RR is a significant indicator of CWP.

The feasibility of recognizing different human–human interactions based on the time-range dimension is investigated. Measured data of six kinds of human–human interactions including hugging, kicking, pointing, punching, pushing, and shaking hands were collected by ultra-wideband radar (UWB). By observing the variation of the time-range dimension matrix, the time-varying UWB signatures are characterized in a time window. Four features are extracted from a time-range dimension matrix by analyzing the characteristics of time-varying UWB signatures. K-nearest neighbor is used to recognize six kinds of human–human interactions based on four measurement features, and the recognition accuracy is found to be up to 99%. In addition, the results obtained by the proposed recognition algorithm are compared with those obtained by other feature extraction algorithms, which further demonstrates the superiority and generalization ability of the algorithm. In addition, the recognition accuracy of the proposed algorithm is higher than some deep learning algorithms, including AlexNet, VGGNet, ResNet, and DenseNet in the case of small samples.

Terahertz wave radar offers a higher resolution and smaller aperture compared with microwave radar. However, despite the emergence of terahertz sources and detectors suitable for radar front ends, the integration of a phased-array radar system remains challenging due to the lack of phase shifters and circulators, the basic components for beam steering and input–output isolation. Here we show that leaky-wave coherence tomography, which can integrate a terahertz radar system using a pair of reverse-connected leaky-wave antennas, can be used to implement beam steering and homodyne detection in one package. Our approach can detect direction and range without using phase shifters, circulators, half-mirrors, lenses or mechanical scanners, providing a compact, penetrating and high-resolution radar system suitable for mobile devices and drones. To illustrate the capabilities of the technique, we use it to create a remote heartbeat detector that can measure the chest displacement of a person through their clothes. A pair of leaky-wave antennas can be used to make a compact, integrated terahertz radar detection system without phase shifters or circulators.