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The continuous miniaturization of semiconductor devices has increased the demand for advanced process control technologies. This process requires real-time measurement systems to monitor manufacturing parameters to ensure efficiency and high quality. This study introduces a novel optical module that uses a spatial light modulator to extract key-point intensity distributions from diffraction images in scatterometry. The efficacy of this method is demonstrated on a grating target with a pitch of 855 nm using a feature extraction algorithm that identifies key point locations based on calculated diffraction images. A particularly designed off-axis extraction pattern facilitates the acquisition of key-point intensity distributions. Moreover, incorporating a cylindrical lens into the optical setup reduces the image feature dimensionality, thereby decreasing the data storage space and enabling the output in a streamlined vector format conducive to further analysis. Experimental data on the development of this scatterometry-based optical module and the subsequent validation of the key-point extraction method indicate a maximum mean absolute error of 0.0080 and a cosine similarity consistently above 0.9999. This study integrates image analysis and measurement techniques by optics, providing a more efficient pathway for key-point extraction in diffraction images, offering the potential for improving real-time process monitoring in the semiconductor manufacturing industry. [ABSTRACT FROM AUTHOR]

The Granites-Tanami Orogen (GTO) is a significant auriferous province located in the poorly exposed southwestern part of the North Australian Craton. This paper looks at the data sources that are suited to studying regolith-landform in areas such as the Tanami Region. One of the most useful datasets for regional regolith-landform mapping is a digital elevation model (DEM). The DEM data that were reviewed demonstrated the utility of TanDEM over other sources, and quantitative results focused on these data for identifying regolith-landform patterns that would aid in geochemical sampling and other land use studies. This paper will demonstrate that neither the classical approach of mapping boundaries based on visual estimates of breaks in slope, nor the alternative approach of producing maps and models derived from algorithms, can be used in isolation. Visual estimates of many boundaries are complex, unreliable and time consuming, and algorithmic mapping is driven by parameters than can produce an infinite set of models. This paper shows that simple landform visualisation is often the most powerful tool for map and model creation, supported by geomorphometric analysis and remotely sensed spectral imagery. The use of TanDEM data is shown to produce the best regolith-landform maps and models of the Tanami Region, which can then improve mineral exploration success.

Based on delay-Doppler maps (DDMs) in raw counts generated by spaceborne global navigation satellite system reflectometry (GNSS-R) receivers, retrieving ocean surface wind speeds is feasible, so several spaceborne GNSS-R missions have been carried out. However, it is currently troubled by global data latency of several hours or even more due to the bottleneck in the satellite downlink. Consequently, this article, for the first time, presents an algorithm for spaceborne GNSS-R receivers to conduct the DDM calibration in orbit and then to retrieve ocean surface wind speeds in real time, which contributes to not only lightening the burden on downloading a wealth of scientific data but also broadcasting real-time ocean surface wind speeds to users. Since there is a power correlation between direct and reflected signals from the same GNSS satellite with respect to the GNSS-R receiver, this algorithm calibrates direct signal power first, and then it estimates the real-time GNSS transmitter effective isotropic radiated power at the reflected signal according to the normalized antenna pattern of the corresponding GNSS satellite. Afterward, DDMs in raw counts are calibrated. Finally, ocean surface wind speeds are computed using pretrained geophysical model functions. Exploiting the scientific data from the GNOS-II onboard China's FY-3E satellite, this algorithm is validated carefully, and final retrieved ocean surface wind speeds against collocated European Centre for Medium-Range Weather Forecasts wind speeds have an overall root-mean-square error of 1.68 m/s and 1.50 m/s for GPS-R and BDS-R, respectively. [ABSTRACT FROM AUTHOR]

The radiometric calibration technique of spaceborne synthetic aperture radar (SAR) aims to establish a constant relationship between the SAR target pulse response energy and the actual radar cross-section (RCS) of the ground. It is an essential technology for achieving quantitative observations of the Earth using radar. In this article, the process of in-orbit radiometric calibration campaign for the SuperView Neo-2 SAR satellite is introduced, which is the distributed X-band SAR satellite in the new generation of Chinese SiWei commercial remote sensing satellite system. More specifically, the initial step involves deriving the total transfer function of the SAR system using the radar equation and establishing a stable calibration constant model for the SAR system. Further, by using the obtained calibration constant to invert the RCS value of the corner reflectors in the Otog Banner calibration field, while validating and analyzing the absolute radiometric accuracy. Considering that the elevation antenna pattern (EAP) predominantly affects the consistency of image radiometric correction, the method of extracting EAP and beam center bias angle from the rainforest is presented. Finally, the quality validation of impulse response function is performed over the California calibration field, which can effectively guarantee the quality of SuperView Neo-2 SAR satellite products. [ABSTRACT FROM AUTHOR]

For spaceborne ISAR imaging of space targets, due to the high-speed relative motion between the radar and targets, both the translational and rotational motion exhibit the high-order characteristics. Conventional translational motion compensation (TMC) methods are ineffective in handling such high-order translational motion. Moreover, the migration through resolution cell (MTRC) stems from the presence of the high-order spatial variant phase of the echo signal, resulting in severe defocusing of ISAR images. In this paper, a novel spaceborne ISAR imaging approach for space targets is proposed, which incorporates the high-order TMC and the high-order MTRC correction. First, the slant range model for spaceborne ISAR imaging is derived as a third-order polynomial form. Based on this model, a modified high-order TMC method is proposed, which utilizes the minimum entropy method combined with the Broyden – Fletcher – Goldfarb – Shanno (BFGS) algorithm. The proposed method leverages orbital model information to obtain the initial TMC coefficients, enabling TMC to be completed more accurately and effectively. Then, a modified polynomial keystone transform (PKT) is proposed to correct the high-order MTRC. The PKT-based method effectively addresses both the high-order and first-order MTRC corrections simultaneously. Finally, a spaceborne ISAR imaging approach is proposed to obtain well-focused ISAR images. Simulated and real data results validate the analysis of the spaceborne ISAR imaging model and demonstrate the effectiveness of the spaceborne imaging approach for space targets proposed in this paper. [ABSTRACT FROM AUTHOR]

This article discusses the importance of radar systems and processing methods for space situational awareness (SSA). It highlights the vulnerability of the space environment due to the increasing number of satellites and space debris, as well as the growing threat of attacks. Radar sensors are particularly useful for SSA because they can detect small debris over long ranges and accurately track objects. The article presents various topics related to radar systems for SSA, including ground-based and space-based radar, multistatic radar networks, and cognitive radar and deep-learning methods. It concludes by providing an overview of the articles included in the special issue. [Extracted from the article]

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Urban building heights offer critical information in studying urbanization. An approach with adequate accuracy is of significant interest. Spaceborne interferometric synthetic aperture radar (InSAR) techniques and multibaseline (MB) InSAR observations can be an alternative to fill the interest. Since the MB InSAR observations are achieved through multitemporal SAR observations with different baselines, temporal decorrelation is unavoidable, affecting most existing MB-based phase unwrapping (PU) approaches. Also, the quality of the derived heights depends on a PU approach. Thus, by combing the permanent scatterers (PS) InSAR technique and the two-stage programming approach (TSPA) framework, we proposed an algorithm to estimate urban building heights with the multitemporal SAR observations, calling it the PS-TSPA algorithm. It takes advantage of the global phase gradient information used in the single baseline PU approaches, not considering the phase continuity assumption. The algorithm should be robust to the temporal decorrelation or noise effect. With the time series of 17 TerraSAR-X data, we estimated building heights in the southern urban area of Chengdu, China. The derived building heights were satisfactory overall and assessed by in situ measurements. In comparison, the Tomo-PS method estimated the heights in the same area and with the same SAR data. Quantitative analyses indicate that the proposed algorithm outperforms the Tomo-PS method. Finally, with routine SAR observations and SAR data downloadable through the Internet, the PS-TSPA algorithm can be inexpensive in estimating urban building heights with reasonable accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the application of deep learning for water-body detection in synthetic aperture radar (SAR) images has seen extensive development. However, a significant proportion of these works primarily concentrate on enhancing and optimizing the model structure, with inadequate exploration of the potential impact of hyperparameter settings, a critical determinant of model performance. Thus, to fully exploit the power of deep learning in water-body detection from SAR images, this letter presents a diversified optimization strategy that revolves around the dung beetle optimizer-convolutional neural network (DBO-CNN) model, complemented by characteristic fusion and decision-level fusion. The DBO-CNN model employs the DBO algorithm to search for the optimal hyperparameter of the CNN model for bolstering the performance of water-body detection in SAR images. To further enhance the performance, the DBO-CNN model uses unique input data which is constructed by integrating the polarimetric characteristic obtained from $H/\alpha $ and model-based (MB) polarization decomposition methods with backscatter characteristics. Finally, two decision-level fusion methods are proposed to optimize detection results, enhancing the recall and intersection over union (IoU) to 96.5% and 91.5%, respectively. In summary, Spaceborne SAR images, with the application of polarization decomposition and neural network, provide new insights and in-depth understanding for detecting water-body. [ABSTRACT FROM AUTHOR]

Two-dimensional (2-D) water surface elevations (WSEs) can be measured for the first time using a new generation of radar altimeter, which uses the interferometric synthetic aperture radar (InSAR) technique on near-nadir swath to measure elevations of both the inland surface waters and the ocean with unprecedented swath and spatial resolution. The interferometric imaging radar altimeter (InIRA) in the Tiangong-2 space laboratory of China is the first spaceborne interferometric radar altimeter, which was launched in 2016. Calibration is essential to a guarantee of the measurement accuracy. Not only the cross-track errors, but also the along-track errors have to be estimated and corrected. This letter proposes a correction method to remove the azimuth time shift of the radar system using geographic positions of the surrounding lakeshores and riverbanks. Real acquisitions of InIRA over the largest inland lake of China, i.e., the Qinghai Lake, have been selected to validate the method. Both azimuth and range system time errors, the media delays, and the cross-track phase errors have been estimated and corrected on the level-1 complex products. The derived 2-D WSEs over an area of ~400 square kilometers have been compared with the high-resolution data of Sentinel-3A synthetic aperture radar altimeter (SRAL). The validation results have shown good agreements in both along-track and cross-track directions with a root mean square error of 28.1 cm and a mean absolute error of 22.7 cm. [ABSTRACT FROM AUTHOR]

The Doppler frequency of the echo signal exhibits time-varying and high-order characteristics due to the high-speed relative motion between the satellites in spaceborne inverse synthetic aperture radar (ISAR) imaging of space target, even during a short coherent processing interval (such as 1 s). Therefore, the selection of the optimal imaging time interval (OITI) is crucial to obtain a well-focused image. The existing methods do not consider the fact that the OITIs of scatterers on the target can also vary significantly. In this letter, an OITI selection method for spaceborne ISAR imaging of space targets based on the time–frequency analysis is proposed. First, the differences in optimal imaging instants (OIIs) of scatterers are analyzed. Then, based on the combination of the Margenau–Hill–spectrogram (MHS) and the S-method, the time–frequency curves can be obtained after extracting the time–frequency ridge and polynomial fitting. The OII of the entire target can be estimated after eliminating the differences in the OIIs by multiplying the constructed third-order phase terms with the signal. Finally, a more suitable OITI can be obtained because of the elimination of the differences in the OIIs. The effectiveness and advancement of the proposed method are verified by simulation and real data results. [ABSTRACT FROM AUTHOR]

Effective oil-spill monitoring is critical for timely response to minimize the impact on the environment. In response to the difficulty in extracting suspected oil films from marine radar images, a semantic segmentation method was proposed. In the preprocessing of training samples in similar scene, a slicing solution was used to compensate for the small sample of original data. The U-Net semantic segmentation network was used to classify oil film into two categories: real and suspected. Existing mainstream marine radar oil-spill identification methods were compared. Experimental results demonstrate that the proposed method achieves more reliability in oil-spill semantic segmentation. It can provide real-time information for oil-spill emergency response and disaster assessment. [ABSTRACT FROM AUTHOR]

Road surface roughness is a major factor that is responsible for the skid resistance of vehicles and, thus, road safety. Therefore, it needs to be monitored regularly to ensure that the roughness values are in the optimal range and to perform maintenance actions when needed. Synthetic aperture radar (SAR) backscatter is sensitive to surface roughness and can provide large-scale estimates of road surface roughness. In this letter, a semiempirical model for estimating road surface roughness using high-resolution spaceborne X-band SAR datasets from Germany’s TerraSAR-X (TS-X) satellite is proposed for the first time. The method is capable of handling the low signal-to-noise ratio (SNR) of spaceborne SAR. To enhance the reliability of the results obtained from rather low SNR datasets, techniques, such as SNR thresholding, multi-dataset fusion, and automatic road extraction, were implemented. The study’s results show good agreement with ground-truth (GT) data. [ABSTRACT FROM AUTHOR]

Compared with the optical imaging system, polarimetric inverse synthetic aperture radar (ISAR) can work all-day and all-weather, which plays an important role in space surveillance. However, high-resolution (HR) ISAR images usually require large bandwidth and coherent integration angle, which is limited by the equipment’s physical conditions. In this vein, the super-resolution (SR) of ISAR images is of vital importance. At present, supervised learning methods are often used in image SR of computer vision. By constructing low-resolution (LR) and HR data pairs, the neural network can learn the mapping relationship between them. However, the low-frequency information in LR image data is less considered. In addition, to obtain different scales of SR reconstruction results, multiple network training repetitions are usually needed, which consumes time and hardware resources. Based on the idea of implicit neural representation, this letter constructs an implicit neural network representation framework for polarimetric ISAR image SR, which can obtain multiscale SR results through one training. A semi-supervised module is also constructed to make the network have the ability of supervised and unsupervised learning, which is conducive to mine and make better use of LR images. A polarimetric ISAR image SR dataset is constructed for satellite targets, while four indexes are adopted for quantitative evaluation in global and local aspects. Experiments demonstrate that the proposed approach achieves better SR performance, where the peak signal-to-noise ratio (PSNR) index can be increased at least by 0.93 dB. [ABSTRACT FROM AUTHOR]

Micro-motion feature vectors of space targets are usually unevenly and multicluster distributed, which limits the performance of the traditional radar anomaly detection methods. To solve this problem, a novel detection method of radar space target abnormal motion method via local density peaks (LDPs) and micro-motion feature is proposed in this letter. First, two discriminative micro-motion features are extracted from the radar echoes to construct a 2-D feature space. Then the abnormal motion detector is derived by classifying the feature vectors into different clusters according to the LDPs and minimum spanning tree clustering (LDP-MST) and solving for the decision thresholds of each cluster with the LDPs, neighbors, and some preset false alarm rates. Electromagnetic simulation experiment results demonstrate that the detection rate of the proposed method is 2.49%, 5.26%, 9.63%, 15.37%, 27.99%, and 49.45% higher than six state-of-art methods, respectively, when the false alarm rate is 5%. [ABSTRACT FROM AUTHOR]

The range-ambiguous clutter in space-based early warning radar is more severe compared with that in airborne radar due to the faster movement velocity. Meanwhile, the clutter angle-Doppler characteristics vary with range owing to Earth’s rotation. As a result, the mainbeam clutter returned from different ambiguous ranges occupies most of the Doppler spectrum, which leads to the degradation of the traditional space–time adaptive processing (STAP) performance in canceling clutter. Though frequency diverse array (FDA) based on multiple-input multiple-output (MIMO) can provide the ability to identify the range-ambiguous clutter, it cannot perform well for space-based early warning radar because of the finite available vertical elements. In this letter, a novel STAP method, which utilizes the element-pulse coding (EPC) and FDA technique to mitigate the range-ambiguous clutter, is proposed. First, EPC is used to prewhiten the most range-ambiguous clutter via coding and decoding in horizontal elements and coherent pulses. Then the interval in the vertical frequency of the residual range-ambiguous clutter is expanded by FDA, and thus the targets and clutter from the vertical mainlobe are easily extracted using a spatial filter with a few vertical elements. Finally, the extracted clutter can be effectively suppressed through the traditional STAP method. Simulation results are provided to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This study investigated the effects of the difference of beam-pointing directions on the Doppler measurements from a spaceborne Ku-band precipitation radar. The Doppler measurement system was a displaced phase center antenna (DPCA) using two antennas that are displaced in the along-track direction. When the beam-pointing directions of the two antennas deviate, Doppler measurements can degrade. The well-known formula for the uncertainty in Doppler velocity measurement is extended to include the effect of the difference of the beam-pointing directions, and a simulation confirmed the formula. The results show that the uncertainty increases from 0.21 to 0.38 m/s for a case of $S/N = 20$ dB when one antenna footprint center is displaced from the other by half of the half-power beamwidth. [ABSTRACT FROM AUTHOR]

Infrared (IR) observations from geostationary satellites have been widely used in estimating the precipitation with high spatiotemporal resolutions. Currently, it is acknowledged that single-channel IR observations are capable to extract the properties of cloud tops, while are insufficient for deeper vertical information. However, based on developments of multispectral IR imagers onboard geostationary satellites, distinguishing different precipitation regions with different vertical structures becomes possible, which has not been fully explored yet. Therefore, combining FengYun-4A/advanced geosynchronous radiation imager (FY-4A/AGRI) and global precipitation measurement/dual-frequency precipitation radar (GPM/DPR), we innovatively proposed frontal precipitation cloud index (FPCI) and saturated precipitation cloud index (SPCI) for IR signals by exploiting the characteristic responses of channel combinations to different precipitation systems. It is proven that multispectral IR observations are capable of responding to certain precipitation systems with different vertical structures. Meanwhile, IR imagers onboard geostationary satellites have significant advantages on tracking developing processes of precipitation systems with high spatiotemporal resolutions than microwave detectors onboard low-earth-orbit (LEO) satellites. In addition, this study is beneficial for impelling IR precipitation estimation to consider more radiation-based physical theory, especially for FY-4A official precipitation product. [ABSTRACT FROM AUTHOR]

This letter presents a new ocean surface wind speed (OSWS) retrieval algorithm called Combined SCAtterometry ReflectometrY (CSCARY). The algorithm uses the maximum-likelihood estimator (MLE) and geophysical model functions (GMFs) to combine backward and forward observables from the advanced scatterometer (ASCAT) and the cyclone global navigation satellite system (CyGNSS) to achieve more accurate wind speed retrieval. To verify the efficacy of the CSCARY algorithm, a case study is conducted on part of the South China Sea. Results showed that the algorithm improved wind speed accuracy by 28.22% for low-medium wind speeds (2–10 m/s) and 25.33% for medium-high wind speeds (6–16 m/s) compared to the operational ASCAT algorithm. Additionally, the most significant improvement is observed at medium wind speeds, with an increase of 23.6%. These findings suggest that combining scatterometer and global navigation satellite system-reflectometry (GNSS-R) could lead to better OSWS retrieval. [ABSTRACT FROM AUTHOR]

This letter aims at investigating the potential of high-resolution (up to $0.7\times0.5\,\,\text{m}^{2}$) synthetic aperture radar (SAR) images in generating digital elevation models (DEMs) using the radargrammetry technique. In this work, we process two SAR images recorded by the Capella Space X-band satellite-borne radar sensor on two consecutive days, October 2 and 3, 2021, over La Palma (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption. We adopt an iterative point-aggregation algorithm to identify matching pixels between the two images; then, the height estimation is performed using a distance minimization routine over the previously identified pairs of matching points. The resultant radargrammetric DEM is validated against a lidar-based DEM for various land cover (LC) classes, showing a good agreement in the areas less affected by lava flow. An estimation of the lava thickness is performed, yielding profiles of the cone area, which are compared to the photogrammetry estimates obtained from the Pléiades mission data. [ABSTRACT FROM AUTHOR]

A new satellite observation concept to estimate upward air mass flux in convective storms has been proposed using a convoy of small radars that measure reflectivity only. The approach is to analyze a pair of radar reflectivity profiles obtained within 30–120 s of one another, and infer the upward transport above the freezing level and at a nominal 3‐km horizontal resolution from the reflectivities and their temporal change. This paper summarizes the theoretical basis for this approach, with quantitative justification using sensitivity analyses of convection‐permitting model simulations and from ground‐based zenith profiler data. We then describe and quantify the performance of the approach to detect updrafts from the radar observations. Finally, we illustrate the expected performance of retrievals of the vertical transport and evaluate their ability to meet the objectives of the "Investigation into Convective Updrafts" mission, selected by NASA to place in orbit in 2027–2028 a convoy of three identical Ka‐band radars measuring radar reflectivity within their common swath. Plain Language Summary: This paper describes a new concept to measure the vertical transport in updrafts in convective storms, from space, using pairs of small radars that measure radar reflectivity only. The paper first presents the theoretical relation between the evolution of reflectivity from the time of the first radar observation to the time of the last (up to 2 min later), on one hand, and the underlying vertical velocities in the updraft. The paper then demonstrates the forward‐sensitivity of reflectivity, under the practical radar constraints, to the vertical velocities. And finally the paper demonstrates the ability to invert this forward sensitivity and produce retrievals of vertical velocity with low uncertainty. Key Points: A new satellite observation concept to estimate air mass transport in convective updrafts is explainedIt is shown that chronological sequences of radar reflectivities are sensitive to the characteristics of vertical transportIt is shown how the characteristics of the vertical transport can be derived from the radar observations [ABSTRACT FROM AUTHOR]

Roadside millimeter-wave radar is one of the most important sensors in roadside perception systems, which have been widely used in intelligent traffic systems. It is important to find an effective calibration procedure for roadside radar to obtain the World Geodetic System-1984 (WGS-84) coordinates of detected targets. However, it is difficult to calibrate roadside millimeter-wave radar safely on public roads without road closures. To solve this problem, this article proposes a roadside millimeter-wave radar calibration method based on connected vehicle technology. First, all the trajectory points of vehicles detected by radar, including the connected vehicle, are collected, and the WGS-84 coordinates of the connected vehicle are obtained by vehicle-to-everything (V2X) communication with the roadside unit. Then, the trajectory points of vehicles are clustered with the density-based spatial clustering of applications with noise (DBSCAN) method, and the trajectory of the connected vehicle in the radar coordinate system is identified with the velocity-matching method automatically. The data pairs of the connected vehicle in the radar coordinate system and WGS-84 coordinate system are obtained with time synchronization. Finally, the calibration parameters are solved with five different types of optimization methods. We verify these methods on the beltway in Changsha. The results show that the improved pseudo inverse method (IPIM) and PIM achieve better performance than the extrinsic calibration method (ECM), rotate coordinate method (RCM), and improved RCM, and they can meet lane-level positioning requirements for V2X applications. This study provides an economical and effective way to solve the calibration problem for roadside millimeter-wave radar in an engineering project. [ABSTRACT FROM AUTHOR]

This article presents a novel 3‐D shape reconstruction strategy for small solar system bodies using spaceborne radar which is applied in flyby trajectories to characterize multiple targets in one mission to save time and cost. In this study, we establish a measurement geometry for rapidly spinning small solar system bodies that combines the motion properties of synthetic aperture radar and circular synthetic aperture radar techniques. We also propose a modified imaging algorithm, which is processed using Cardano's formula and the modified polar format algorithm, to improve the imaging quality. Finally, we present simulated results of point‐scattering and 3D shape models. Our analysis shows that the shape reconstruction errors of the algorithm are less than 1.5 m as indicated by the standard deviation. Plain Language Summary: Exploring small solar system bodies is crucial to understanding the evolution of our solar system. However, most of these small bodies are distant from Earth, and the small size, weak gravity, irregular shape, and high speed rotation further increase the difficulty of exploring a small solar system body. To tackle these challenges and characterize as many targets as possible in one mission, we propose a novel 3‐D imaging strategy. Our approach aims to reconstruct the 3D shape of rapidly spinning small solar system bodies in flyby trajectories. We begin by establishing an observation model and analyzing the imaging geometry and signal characteristics. Next, we describe our reconstruction procedure in detail and propose a modified algorithm to improve the imaging quality. Finally, we present simulated results to validate our proposed method. Key Points: A new three‐dimensional shape reconstruction strategy is presented for rapidly spinning small solar system bodies in flyby missionsA modified imaging algorithm to compensate the phase errors to optimize the image quality is provided [ABSTRACT FROM AUTHOR]

Abstract This article presents a novel 3‐D shape reconstruction strategy for small solar system bodies using spaceborne radar which is applied in flyby trajectories to characterize multiple targets in one mission to save time and cost. In this study, we establish a measurement geometry for rapidly spinning small solar system bodies that combines the motion properties of synthetic aperture radar and circular synthetic aperture radar techniques. We also propose a modified imaging algorithm, which is processed using Cardano's formula and the modified polar format algorithm, to improve the imaging quality. Finally, we present simulated results of point‐scattering and 3D shape models. Our analysis shows that the shape reconstruction errors of the algorithm are less than 1.5 m as indicated by the standard deviation.

Conventional transmitarray antennas are designed to produce a directional pattern with the beam peak in a single direction when the feed is placed at a distinct location. A real-life application requiring conical beam scanning necessitates the development of antennas with patterns that sweep out a cone in space through successive excitation of feeds placed at multiple locations along the body of the revolution ring focus. In this communication, we propose transmitarrays as an antenna solution that can achieve the conical beam sweep. Compared to the previously reported transmitarrays, the development of an off-axis-fed transmitarray for conical beam scanning requires significant advancements regarding the synthesis and optimization of the off-axis-fed transmitarray. A novel synthesis algorithm is presented based on the optimization of the feed location and a modified transmitarray phase compensation. To validate the approach, an 18 cm transmitarray with a thickness of 0.8 cm operating at 13.4 GHz is designed, simulated, prototyped, and measured. Predictions obtained by full-wave simulations agree well with the measured results. The conical beam scanning capability of the transmitarray is demonstrated through measurements taken with the feed placed at five locations within the ring focus. [ABSTRACT FROM AUTHOR]

Conventional radars repeat the transmission of the same waveform after a predefined interval of time called pulse-repetition-interval (PRI). This technique helps estimate the range and Doppler shift of the targets and suppress clutter. However, in dual-function radar communication (DFRC), a different symbol waveform is transmitted after each PRI. Depending on the number of targets, radar receiver output yields several peaks representing different targets’ ranges. Each peak comes with its side-lobes called range-side-lobes (RSL). In DFRC, due to different symbol waveform transmission, peaks and RSLs do not remain coherent, making Doppler shift estimation and clutter suppression challenging tasks. In most of the available literature, iterative receive filters have been designed for DFRC to minimize RSLs and achieve coherent output for different waveforms. However, the proposed receive filter does not guarantee coherent output for more than two waveforms. In contrast, we proposed two novel closed-form algorithms to design receive filters for DFRC that guarantee coherent output response for several waveforms and suppress RSLs. Simulation results demonstrate that the proposed receivers achieve full coherency, and the RSLs are significantly lower than the conventional method. Furthermore, the advantage of achieving coherent output response is shown in target detection and bit-error-rate improvement. [ABSTRACT FROM AUTHOR]

During the New Space era small countries are becoming important players in the space business. While space activities are rapidly increasing globally, it is important to make operations in a sustainable and safe way in order to preserve satellite services for future generations. Unfortunately, the sustainability aspect has been largely overlooked in the existing surveys on space technologies. As a result, in this survey paper, we discuss the multi-layer networking approaches in the 6G era specifically from the sustainability perspective. Moreover, our comprehensive survey includes aspects of some interesting industrial, proprietary, and standardization views. We review the most important regulations and international guidelines and revisit a three-dimensional architecture vision to support the sustainability target for a variety of application areas. We then classify and discuss space safety paradigms that are important sustainability enablers of future satellite communications. These include space traffic management, debris detection, environmental impacts, spectrum sharing, and cyber security aspects. The paper also discusses advances towards a planned European connectivity constellation that could become a third flagship infrastructure along with the Galileo and Copernicus systems. Finally, we define potential research directions into the 2030s.

This article presents the first demonstration of a full-waveguide feed at W-band, enabling transmit–receive duplexing and 2-D tracking of targets. The proposed device is a compact unit designed to be used as feed of a 94 GHz space-borne radar. Its duplexing concept relies on a septum orthomode transducer polarizer for transmitting and receiving sum signals with opposite circular polarizations. Additionally, the first higher order mode of circular waveguide (TM01) is allowed to propagate inside the device to extract a difference signal for tracking. By comparing received sum and difference signals in amplitude and phase, both angular directions of a target position can be derived. A prototype of the duplexer was fabricated and tested: measurements at 94 GHz show return loss and isolation above 23 dB at all ports, with reasonable performance from 92 to 96 GHz. The measured monopulse characteristic enables 2-D tracking of targets deviated by less than 8.5° from boresight. [ABSTRACT FROM AUTHOR]

With the increasing of on-orbit satellites, target dynamic monitoring becomes more and more important in space situation awareness applications. Currently, there are some exploratory methods monitoring attitude-stabilized targets using various high-resolution remote sensing technologies. However, it is still a big challenge to achieve dynamic estimation of spin satellite with single sensor. Inspired by the existing matching based works, this article presents a dynamic estimation method of spin satellite using single-station inverse synthetic aperture radar (ISAR) images. When the angle accumulation is set to be a constant in each ISAR imaging period, the projection change of target structures in the long-term observation sequence is described with the hidden Markov model to build the mathematic expression of target on-orbit state. In this condition, target sequential attitude can be solved with Viterbi decoding algorithm even the azimuth scaling of each image is difficulty due to target spin motion. Then, combined with the radar observation geometry, target sequential attitude parameters are substituted into a spin parameter optimization, which is solved by particle swarm optimization algorithm. In the end, target instantaneous attitude vectors and spin speed are used to express its dynamic during the observation period. Simulation experiments of a typical spin spacecraft, Tiangong-I, confirm the feasibility of the proposed algorithm, and its superiority is also investigated by comparison with the existing work. [ABSTRACT FROM AUTHOR]

The advantages of sub-terahertz technology (sub-THz, 200–700 GHz) have already been advanced for imaging and recognition of a space object's state from a space-based sensor using inverse synthetic aperture radar (ISAR). The technology benefits from wide absolute signal bandwidths, giving high range resolution and from enhanced sensitivity to surface texture. An experimental validation of such a system in controlled laboratory conditions is presented. Possible image formation methods are proposed and evaluated experimentally. Images of real parts of satellites have been produced at sub-THz frequencies and compared to lower frequency images. Other results include sub-THz bistatic ISAR, and sub-THz cross-polarized images which highlight the scattering from rough surfaces while attenuating the very bright scatterers seen from conventional monostatic images. [ABSTRACT FROM AUTHOR]

The space-air-ground-sea integrated network (SAGSIN) plays an important role in offering global coverage. To improve the efficient utilization of spectral and hardware resources in the SAGSIN, integrated sensing and communications (ISAC) has drawn extensive attention. Most existing ISAC works focus on terrestrial networks and cannot be straightforwardly applied in satellite systems due to the significantly different electromagnetic wave propagation properties. In this work, we investigate the application of ISAC in massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems. We first characterize the statistical wave propagation properties by considering beam squint effects. Based on this analysis, we propose a beam squint-aware ISAC technique for hybrid analog/digital massive MIMO LEO satellite systems exploiting statistical channel state information. Simulation results demonstrate that the proposed scheme can operate both the wireless communications and the target sensing simultaneously with satisfactory performance, and the beam-squint effects can be efficiently mitigated with the proposed method in typical LEO satellite systems. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of Advanced Driver Assistance Systems (ADAS), vehicular millimeter-wave (mmWave) radar plays an important role in urban intelligent transportation system. Self-interference caused by multipath in radar channel is a widely concerned problem. However, the research on radar channel propagation characteristics is very limited and there are few models for radar channel available. In this paper, we propose a pioneering research for radar channel model. Based on measurement data, channel characteristics are analysed using the space-alternating generalized expectation-maximization (SAGE) algorithm. An empirical statistical model is established for the 77GHz mmWave radar channel in the underground parking lot scenario, which is one of the most challenging environments because of time-variation and abundant multipaths, filling the gap in the field. A path-association algorithm is used to obtain multipath component (MPC) trajectories in delay, Doppler frequency and power domains. Additionally, some trajectory (in range domain) features are analysed. Moreover, based on geometric analysis of trajectories in range domain, the deterministic component of trajectories is modeled, and then a novel method for discrimination between actual targets and ghost images is proposed and verified by simulation and measurement data. [ABSTRACT FROM AUTHOR]

This letter is the second part of a three-part tutorial focusing on rate-splitting multiple access (RSMA) for 6G. As Part II of the tutorial, this letter addresses the interplay between RSMA and integrated radar sensing and communications (ISAC). In particular, we introduce a general RSMA-assisted ISAC architecture, where the ISAC platform has a dual capability to simultaneously communicate with downlink users and probe detection signals to a moving target. Then, the metrics of radar sensing and communications are respectively introduced, followed by a RSMA-assisted ISAC waveform design example which jointly minimizes the Cramér-Rao bound (CRB) of target estimation and maximizes the minimum fairness rate (MFR) amongst communication users subject to the per-antenna power constraint. The superiority of RSMA-assisted ISAC is verified through simulation results in both terrestrial and satellite scenarios. RSMA is demonstrated to be a powerful multiple access and interference management strategy for ISAC, and provides a better communication-sensing trade-off compared with the conventional benchmark strategies. Consequently, RSMA is a promising technology for next generation multiple access (NGMA) and future networks such as 6G and beyond. [ABSTRACT FROM AUTHOR]

In recent years, there are increasing interests and applications on radar–communication integration for system miniaturization, cost reduction, and so on. Moreover, thanks to the common functions of hardware components including transceiver, antennas, and even basic signal/data processing, the integrated design of radar and communication systems becomes feasible. In this article, through design of proper time sequences, waveforms, and recognition algorithms, radar sensing and wireless communication can be integrated into 4-D antenna arrays simultaneously. In addition, with the high design degrees of freedom of 4-D arrays, the proposed approach can also provide a low probability of intercept capability. Selected numerical and experimental results are provided to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This is the third of three articles that quantify the high added value of frequent satellite microwave observations of the atmosphere (with a “refresh” time on the order of 1 min) to capture the dynamics of weather systems. Recent advances in small-satellite and microwave miniaturization, such as the “Temporal Experiment for Storms and Tropical systems” (TempEST) millimeter-wave radiometer developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between passive microwave observations and their change in time and the underlying cloud variables and processes and to evaluate the sensitivity to the different physical and instrument parameters. In this third part, simulations are used to demonstrate and quantify the direct sensitivity of a time sequence of mm-wave radiances to the vertical structure of the vertical updrafts in convective storms. It is demonstrated that the brightness temperatures from a pair of low-Earth-orbit (LEO) radiometers maintaining a separation of 1 or 2 min can indeed be used to retrieve the column-maximum magnitude of the vertical wind as well as the height of the maximum. While such passive retrievals do not provide the vertical detail that a pair of radars would produce, the radiometers have a vast swath and therefore enable the observation of an entire storm. The application is therefore quite different from the case of a convoy of radars: rather than compiling radar statistics over multiple years, the radiometer-convoy measurements can be used to analyze every storm that is observed. [ABSTRACT FROM AUTHOR]

Radar sounders (RSs) are gaining importance in planetary missions thanks to their unique capability of providing direct measurements of subsurface (SS) structures. To support their design and data interpretation, several electromagnetic (e.m.) simulation techniques have been developed with enhanced capabilities for emulating the RS acquisition process. However, the raw simulated radargrams obtained from e.m. simulators are difficult to interpret and analyze without a focusing operation, which results in an underestimation of the RS detection performance. While frequency methods for range and azimuth compression of real RS data are well-established, their use on simulated data is not addressed in the literature and requires major modifications. This article presents a novel method that implements azimuth compression using unfocused and focused processing on simulated raw data. The proposed method is based on an adaptation of the range-Doppler algorithm to the case of raw data generated by a coherent RS simulator. The method is demonstrated in three case studies to show the similarity between simulated and real data processing: 1) simple geometries; 2) a simulated SHAllow RADar (SHARAD) radargram compared with the real data product; and 3) a real application scenario for supporting the design of a new RS instrument. The results indicate higher fidelity of the focused simulated data with the real data product and the target structure, confirming the usefulness of the proposed approach in obtaining realistic processing of simulated radargrams. [ABSTRACT FROM AUTHOR]

Spaceborne radar sounders are high frequency (HF)/very high frequency (VHF) nadir-looking sensors devoted to subsurface investigations. Their data interpretation can be severely hindered by off-nadir surface clutter. Recent literature showed that the clutter suppression capabilities of this class of systems can be greatly enhanced by deploying an array of orbiting sensors in formation flight synthesizing a narrow radar antenna beam. In this article, we assess the capability of distributed radar sounding to discriminate clutter from subsurface returns by exploiting direction of arrival (DOA) estimation techniques. This is achieved by first outlining an approach for designing and evaluating the distributed radar sounder DOA estimation performance as function of the radar system parameters (e.g., intersensor distance) and external noise factors such as ionospheric scintillations. Then, the theory is complemented by radar simulations of several acquisitions over Greenland assuming a variety of subsurface geometries. The simulations confirm that clutter discrimination through DOA estimation is a viable approach to further improve the array capability in disambiguation of subsurface echoes from surface ones. [ABSTRACT FROM AUTHOR]

Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic synthetic aperture radar (SAR) with its serving base station (BS) to sense over large areas with superb sensing resolutions at no additional requirement of spectrum. This paper designs this novel BS-UAV bistatic SAR platform, and optimizes the flight path of the UAV to minimize its propulsion energy and guarantee the required sensing resolutions on a series of interesting landmarks. A new trajectory planning algorithm is developed to convexify the propulsion energy and resolution requirements by using successive convex approximation and block coordinate descent. Effective trajectories are obtained with a polynomial complexity. Extensive simulations reveal that the proposed trajectory planning algorithm outperforms significantly its alternative that minimizes the flight distance of cellular-aided sensing missions in terms of energy efficiency and effective consumption fluctuation. The energy saving offered by the proposed algorithm can be as significant as 55%. [ABSTRACT FROM AUTHOR]

When a missile-borne radar system works in downward-looking surveillance mode, the broadened ground clutter signal in virtue of platform high-speed motion will be received by the radar receiver, which will cause difficulty in moving target detection and attacking. Unlike airborne and spaceborne platforms, a missile-borne platform exhibits some unique motion characteristics, such as diving, spinning, and coning, causing the clutter space–time distribution property significantly different from those of airborne and spaceborne radar platforms. In addition, the forward target striking requirements make the missile-borne clutter space–time spectrum further exhibit the severe range-dependent property. To deal with these issues, accurate motion modeling of a missile-borne radar platform is first carried out in this article, where the complex platform motions including forward-looking diving, spinning, and coning are considered. Then, the autocorrelation processing combined with iterative adaptive approach is applied to estimate the clutter angle-Doppler center frequencies, so as to effectively realize the clutter nonstationary compensation along spatial and temporal directions. Finally, a time-domain sliding window-based subspace projection method is proposed to achieve the robust clutter suppression. Both simulation and real-measured radar data processing results are presented to validate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Weather radar echo extrapolation, which predicts future echoes based on historical observations, is one of the complicated spatial–temporal sequence prediction tasks and plays a prominent role in severe convection and precipitation nowcasting. However, existing extrapolation methods mainly focus on a defective echo-motion extrapolation paradigm based on finite observational dynamics, neglecting that the actual echo sequence has a more complicated evolution process that contains both nonlinear motions and the lifecycle from initiation to decay, resulting in poor prediction precision and limited application ability. To complement this paradigm, we propose to incorporate a novel long-term evolution regularity memory (LERM) module into the network, which can memorize long-term echo-evolution regularities during training and be recalled for guiding extrapolation. Moreover, to resolve the blurry prediction problem and improve forecast accuracy, we also adopt a coarse–fine hierarchical extrapolation strategy and compositive loss function. We separate the extrapolation task into coarse and fine two levels which can reduce the downsampling loss and retain echo fine details. Except for the average reconstruction loss, we additionally employ adversarial loss and perceptual similarity loss to further improve the visual quality. Experimental results from two real radar echo datasets demonstrate the effectiveness of our methodology and show that it can accurately extrapolate the echo evolution while ensuring the echo details are realistic enough, even for the long term. Our method can further be improved in the future by integrating multimodal radar variables or introducing certain domain prior knowledge of physical mechanisms. It can also be applied to other spatial–temporal sequence prediction tasks, such as the prediction of satellite cloud images and wind field figures. [ABSTRACT FROM AUTHOR]

For a space-based radar system with hybrid baseline, the problem of clutter angle-Doppler spectral broadening poses a significant challenge to clutter cancellation in the terrain fluctuant observation scene. To improve the robustness of clutter suppression, this article proposed a homogeneous sample selection method based on a novel concept of generalized spatial spectrum density function (GSSDF). This method can be summarized as three crucial steps. First, the GSSDF was constructed by the prior information of digital elevation model (DEM) data, radar system parameters, and the backscattering model. Then, the angle of deflection (AOD) and the equivalent bandwidth (EBW) of GSSDF were adopted to measure the diffusion of clutter spectral. Subsequently, the sample selection criterion was established by a new threshold detection strategy, but the key here is that an appropriate detection threshold of the AOD and EBW can be determined by the characteristic of filter response. To summarize, this approach ensures that the training samples sharing similar clutter properties can be selected to estimate clutter covariance matrix (CCM), thereby enhances clutter suppression capability. Finally, the experimental results demonstrated that the proposed method can obtain better clutter suppression performance than the other contrast methods. [ABSTRACT FROM AUTHOR]

Multiple-input and multiple-output synthetic aperture radar (MIMO SAR) is a very potential technique for high-resolution and wide-swath (HRWS) imaging due to the fact that it can provide more degrees of freedom. In this article, an innovative MIMO-HRWS SAR imaging system is introduced with segmented phase coding (SPC) waveforms. However, it is well-known that the echo separation issue is the most technical challenge for MIMO SAR. It has shown that the separation of orthogonal echoes from cross correlation interference can be achieved by scan-on-receive (SCORE). However, this method often requires large computing resources on board, especially for null-steering process. For this, we propose a two-stage spaceborne–ground echo separation scheme. This scheme is mainly divided into two steps: the first step is a real-time SCORE beamforming process on board to reduce the downlink data volume, and the second step is a bandpass–null-steering process to suppress the interfering echoes on the ground. Thus, this scheme allows for the low computing resources on board and sufficient suppression of the interference simultaneously. Following this scheme, the MIMO-HRWS SAR system enables its number of equivalent phase centers nearly double that of the azimuth multichannel SAR system, leading to higher resolution and wider swath imaging. Finally, detailed simulation experiments for the MIMO-HRWS SAR imaging system are performed to verify the practicability and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article presents a new method to retrieve dynamic information from spaceborne Doppler radar observations. The method is based on a complex convolution Doppler resampling (CConDoR) formulation, which links the spaceborne pulse-pair (PP) correlation measurements to high-resolution PP products that are not affected by the spacecraft motion. The CConDoR formalism allows to easily simulate Doppler products via convolution products. The CConDoR representation enables also retrievals by deconvolution. This approach is illustrated using a Wiener deconvolution algorithm. Results are shown for simulations of the radar of the Earth Cloud Aerosol Radiation Explorer (EarthCARE; developed by the European and Japanese space agencies). The proposed corrections improve the accuracy of the measured mean velocities (by correcting for nonuniform beam-filling (NUBF) biases) and spectral widths (by correcting for the spectral broadening), which to the best of the authors’ knowledge is novel. [ABSTRACT FROM AUTHOR]

Lake ice thickness (LIT), a thematic product of lakes as an essential climate variable (ECV), is sensitive to changes in air temperature and on-ice snow mass. Here, a novel and efficient analytic method (retracking approach) is presented for the estimation of LIT from Ku-band (13.6 GHz) radar altimetry data. The new retracker, referred to as ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ , is based on the physical modeling of the conventional radar echoes (also called low-resolution mode or LRM) over ice-covered lakes that show a characteristic step-like feature in their leading edge attributed to the reflection of radar waves at the snow–ice and ice–water interfaces. The method is applied to Jason-2 and Jason-3 data acquired over Great Slave Lake, Canada, over three ice seasons (2013–2016). As expected, the agreement between the Jason-2 and Jason-3 LIT estimates over their overlapping period (2016 ice season) is excellent with a mean bias error of 0.013 m and root mean square error (RMSE) of 0.024 m. LIT estimates from ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ are in good agreement with simulations from a thermodynamic lake ice model and in situ measurements with RMSE values of the order of a few centimeters for the three winter seasons. The retracker also provides a robust way to assess the accuracy of LIT estimates which is in the order of 0.10 m when the ice cover is well established and prior to melt onset. In addition, ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ captures the seasonal transitions during the freeze-up and breakup periods and ice growth over different winter seasons, making it a promising method for monitoring inter-annual variability and trends in LIT from past and current conventional radar altimetry missions. [ABSTRACT FROM AUTHOR]

Methodologies for characterization of the lateral indentation of silicon-germanium (SiGe) nanosheets using different non-destructive and in-line compatible metrology techniques are presented and discussed. Gate-all-around nanosheet device structures with a total of three sacrificial SiGe sheets were fabricated and different etch process conditions used to induce indent depth variations. Scatterometry with spectral interferometry and x-ray fluorescence in conjunction with advanced interpretation and machine learning algorithms were used to quantify the SiGe indentation. Solutions for two approaches, average indent (represented by a single parameter) as well as sheet-specific indent, are presented. Both scatterometry with spectral interferometry as well as x-ray fluorescence measurements are suitable techniques to quantify the average indent through a single parameter. Furthermore, machine learning algorithms enable a fast solution path by combining x-ray fluorescence difference data with scatterometry spectra, therefore avoiding the need for a full optical model solution. A similar machine learning model approach can be employed for sheet-specific indent monitoring; however, reference data from cross-section transmission electron microscopy image analyses are required for training. It was found that scatterometry with spectral interferometry spectra and a traditional optical model in combination with advanced algorithms can achieve a very good match to sheet-specific reference data. [ABSTRACT FROM AUTHOR]

In pattern classification, we may have a few labeled data points in the target domain, but a number of labeled samples are available in another related domain (called the source domain). Transfer learning can solve such classification problems via the knowledge transfer from source to target domains. The source and target domains can be represented by heterogeneous features. There may exist uncertainty in domain transformation, and such uncertainty is not good for classification. The effective management of uncertainty is important for improving classification accuracy. So, a new belief-based bidirectional transfer classification (BDTC) method is proposed. In BDTC, the intraclass transformation matrix is estimated at first for mapping the patterns from source to target domains, and this matrix can be learned using the labeled patterns of the same class represented by heterogeneous domains (features). The labeled patterns in the source domain are transferred to the target domain by the corresponding transformation matrix. Then, we learn a classifier using all the labeled patterns in the target domain to classify the objects. In order to take full advantage of the complementary knowledge of different domains, we transfer the query patterns from target to source domains using the K-NN technique and do the classification task in the source domain. Thus, two pieces of classification results can be obtained for each query pattern in the source and target domains, but the classification results may have different reliabilities/weights. A weighted combination rule is developed to combine the two classification results based on the belief functions theory, which is an expert at dealing with uncertain information. We can efficiently reduce the uncertainty of transfer classification via the combination strategy. Experiments on some domain adaptation benchmarks show that our method can effectively improve classification accuracy compared with other related methods. [ABSTRACT FROM AUTHOR]

Abstract Spaceborne synthetic aperture radar (SAR) with digital beam‐forming (DBF) in elevation uses very sharp time‐variant antenna beams to improve the signal‐to‐noise ratio (SNR) and range ambiguity‐to‐signal ratio. In acquisition scenarios of imprecisely known Earth topography, however, antenna pointing might be mismatched with surface geometry. To handle this issue, it is suggested that preprocessing is applied to DBF to determine the angle of the incident echo signal on the planar antenna array. The authors derive a method that estimates the direction of arrival on the acquired raw data with high accuracy in real time. The method is based on the matrix pencil, which is a common approach in mathematics for solving linear equation systems. As a key innovation, this paper describes further improvements and adaptions to SAR scenarios and provides a possible concept for practical implementation. For proof of concept, a series of different simulation tasks is performed.

Currently, the spaceborne synthetic aperture radar (SAR) system transmits a great deal of data to the ground processing station and generates massive images daily, only a tiny fraction of which contains radio frequency interference (RFI). However, most of the existing RFI detection methods are based on the prior conditions in which the known image contains interference. In fact, it is difficult to learn whether SAR images contain RFIs without prescreening, so it is of great significance to the rapid and real-time screening and detection of RFI in SAR images. This article proposes a method to screen and detect RFI from massive SAR images simultaneously. 1) We construct an approximate RFI-free background image by using the preprocessed time-series SAR images acquired in the past. 2) We generate difference images based on the image change detection method and analyze them by using an adaptive threshold, then calculate the entropy of all difference images to complete the preliminary screening of the RFI-containing). According to the preliminary results, we remove the RFI-containing parts; after reconstructing the background, we repeat step 2 with the images to be detected and obtain the final screening and detection results. Massive experimental results based on Sentinel-1 images validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) is a significant threat to accurate microwave remote sensing. The RFI signals manifest themselves in unpredictable locations and patterns in the image, which will cause measurement distortion and image degradation or even lead to wrong retrievals of the geophysical parameters. Accurate detection of RFI artifacts is a prerequisite step to preserve the overall quality of remote sensing quality. In this article, a semantic cognitive enhancement network for RFI signature detection is proposed. It employs an encoder–decoder architecture, which incorporates the atrous spatial pyramid pooling, depthwise convolution, and self-attentional mechanism. Rather than detecting the existence of RFI artifacts for an entire image, the proposed scheme can realize RFI recognition in a pixelwise manner without setting predefined thresholds. Extensive experimental results on diverse scenarios in Sentinel-1 images with various RFI types are provided, which demonstrates robust detection performance for both strong and weak interference without requiring a large number of training samples. [ABSTRACT FROM AUTHOR]