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Synthetic aperture radar (SAR) simulation is a vital tool for planning SAR missions, interpreting SAR images, and extracting valuable information. SAR imaging is essential for analyzing sea scenes, and the accuracy of sea surface and scattering models is crucial for effective SAR simulations. Traditional methods typically employ empirical formulas to fit sea surface scattering, which are not closely aligned with the principles of electromagnetic scattering. This paper introduces a novel approach by constructing multiple sea surface models based on the Pierson–Moskowitz (P-M) sea spectrum, integrated with the stereo wave observation projection (SWOP) expansion function to thoroughly account for the influence of wave fluctuation characteristics on radar scattering. Utilizing the shooting and bouncing ray-physical optics (SBR-PO) method, which adheres to the principles of electromagnetic scattering, this study not only analyzes sea surface scattering characteristics under various sea conditions but also facilitates the computation of scattering coupling between multiple targets. By constructing detailed scattering distribution data, the method achieves high-precision SAR simulation results. The scattering model developed using the SBR-PO method provides a more nuanced description of sea surface scenes compared to traditional methods, achieving an optimal balance between efficiency and accuracy, thus significantly enhancing sea surface SAR imaging simulations. [ABSTRACT FROM AUTHOR]

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided. [ABSTRACT FROM AUTHOR]

Forward‐looking radar imaging is a top priority due to a variety of applications. An airborne forward‐looking radar imaging approach via a modified propagator method (MPM) based on the planar phased array is proposed. It can obtain a better focusing effect without range migration correction. Through the MPM algorithm, the azimuth resolution can be enhanced greatly. Compared with the conventional synthetic aperture radar imaging algorithm, the left‐right ambiguity process can be avoided by generating two‐dimensional spatial spectra. Furthermore, the imaging results of different numbers of elements are given to provide an assessment of the imaging performance. Additionally, the authors proposed to apply the scanning planar phased array configuration to airborne forward‐looking two‐dimensional imaging for the first time. The validity of the method is proven by experiments. The image is more focused. Furthermore, the results of the experiments verify that the proposed method is fit for high‐speed situation and aircraft height variation condition. [ABSTRACT FROM AUTHOR]

Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal‐to‐noise ratio environments. To overcome this issue, a novel similarity‐oriented (SO) method with a two‐domain denoising strategy is proposed. A PIxEl similarity‐oriented (PIE‐SO) denoising method designed for range‐Doppler (RD) domain and a modified RAnge Profile Similarity‐Oriented (RAP‐SO) denoising method designed for high‐resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE‐SO method directly performs a two‐dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE‐SO denoising, a Laplacian regularised‐weighted nuclear norm proximal (LR‐WNNP) operator is proposed. The proposed modified RAP‐SO method, that is, the LR‐WNNP operator, exploits the low‐rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method. [ABSTRACT FROM AUTHOR]

In this work, a unique radar hardware is introduced for human-gait micro-Doppler studies. The developed radar sensor operates in C-band microwave frequencies. We share several outdoor experiments at Mersin University facilities to detect and characterize human walking and running movements. In these experiments, various walking and running movements were performed with different people. To examine the Doppler properties of human motion, raw data gathered is transformed onto 2D jointtime-frequency plane. The generation of micro-Doppler signatures in the transformed data is the first step in the extraction of features of the walking/running human motion. It is shown that the directions, durations, range distances as well as torso and limb velocities of walking and running human movements in each experiment are successfully obtained from these micro-Doppler signatures. [ABSTRACT FROM AUTHOR]

Abstract Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal‐to‐noise ratio environments. To overcome this issue, a novel similarity‐oriented (SO) method with a two‐domain denoising strategy is proposed. A PIxEl similarity‐oriented (PIE‐SO) denoising method designed for range‐Doppler (RD) domain and a modified RAnge Profile Similarity‐Oriented (RAP‐SO) denoising method designed for high‐resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE‐SO method directly performs a two‐dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE‐SO denoising, a Laplacian regularised‐weighted nuclear norm proximal (LR‐WNNP) operator is proposed. The proposed modified RAP‐SO method, that is, the LR‐WNNP operator, exploits the low‐rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method.

Abstract Forward‐looking radar imaging is a top priority due to a variety of applications. An airborne forward‐looking radar imaging approach via a modified propagator method (MPM) based on the planar phased array is proposed. It can obtain a better focusing effect without range migration correction. Through the MPM algorithm, the azimuth resolution can be enhanced greatly. Compared with the conventional synthetic aperture radar imaging algorithm, the left‐right ambiguity process can be avoided by generating two‐dimensional spatial spectra. Furthermore, the imaging results of different numbers of elements are given to provide an assessment of the imaging performance. Additionally, the authors proposed to apply the scanning planar phased array configuration to airborne forward‐looking two‐dimensional imaging for the first time. The validity of the method is proven by experiments. The image is more focused. Furthermore, the results of the experiments verify that the proposed method is fit for high‐speed situation and aircraft height variation condition.

Abstract The synthesis of rain fields is essential in multiple research fields and applications, including Single‐image Derain. However, there is a lack of research on simulated rain fields, and the existing rain field generation models struggle to capture complex spatial distributions and generate truly random rain fields. To address this, the authors propose a generative adversarial networks‐based rain field generation network, which consists of a generator, a discriminator, and a feature extraction block that can produce realistic and complex rain fields. The authors’ experiments demonstrate that this method achieves an average Frechet Inception Distance score of 0.035, and user studies indicate that the generated rain distribution looks naturally.

With the rapid development of high-resolution radar imaging technology, artificial intelligence, and big data technology, remarkable advancements have been made in the intelligent interpretation of radar imagery. Despite growing demands, radar image intrpretation is now facing various technical challenges mainly because of the particularity of the radar sensor itself and the complexity of electromagnetic scattering physical phenomena. To address the problem of microwave radar imagery perception, this article proposes the development of the cross-disciplinary microwave vision research, which further integrates electromagnetic physics and radar imaging mechanism with human brain visual perception principles and computer vision technologies. This article discusses the concept and implication of microwave vision, proposes a microwave vision perception model, and explains its basic scientific problems and technical roadmaps. Finally, it introduces the preliminary research progress on related issues achieved by the authors’ group.

Abstract An important aspect of Space Situational Awareness is to estimate the intent of objects in space. This paper discusses how discriminating features can be obtained from Inverse Synthetic Aperture Radar images of such objects and how these discriminators can be used to recognise the objects or to estimate their intent. If the object is, for example, a satellite of a known type, the scheme proposed is able to recognise it. The ability of the scheme to detect damage to the object is also discussed. The focus is on imagery obtained in the sub‐terahertz band (typically 300 GHz) because of the greater imaging capability given by the diffuse scattering which is observed at these frequencies. The paper also discusses the importance of being able to use images obtained by electromagnetic simulation to be able to train the subsystem which recognises features of the objects and describes a practical scheme for creating these simulations for large objects at these very short wavelengths.

Abstract Long baseline bistatic radar systems herald enhanced sensitivity and metric accuracy for space objects in geosynchronous orbits and beyond. Radio telescopes are ideal participants in such a system; in particular, they often feature large apertures with low‐noise temperatures and have stable, synchronised clocks. Pairing radio telescopes with high‐power radars creates new methodologies for Space Domain Awareness. This paper describes long baseline bistatic measurements using the Millstone Hill Radar in the USA, the Tracking and Imaging Radar in Germany, multiple receivers of the enhanced multi‐element remotely linked interferometer network array in the United Kingdom, and the Westerbork Synthesis Radio Telescope in the Netherlands. The authors, a Research Task Group formed by the NATO Science and Technology Organisation Sensors and Electronic Technology Panel (SET‐293), performed novel bistatic and monostatic radar imaging experiments with real on‐orbit tumbling rocket bodies. These experiments on tumbling objects at near‐geosynchronous orbits highlight successful demonstrations of advanced bistatic Doppler characterisation across diverse imaging geometries. Specialised Doppler processing on tumbling targets, such as the Doppler superpulse algorithm, enables high‐fidelity rotation period estimation and determination of minimum target size.

With the number of new satellites increasing dramatically, comprehensive space surveillance is becoming increasingly important. Therefore, high-resolution inverse synthetic aperture radar (ISAR) imaging of satellites can provide an in-situ assessment of the satellites. This paper demonstrates that pseudo-noise signals can also be used for satellite imaging, in addition to classical linear frequency-modulated chirp signals. Pseudo-noise transmission signals offer the advantage of very low cross-correlation values. This, for instance, enables the possibility of a system with multiple channels transmitting instantaneously. Furthermore, it can significantly reduce signal interference with other systems operating in the same frequency spectrum, which is of particular interest for high-bandwidth, high-power systems such as satellite imaging radars. A new routine has been introduced to generate a wideband pseudo-noise signal with a peak-to-average power ratio (PAPR) similar to that of a chirp signal. This is essential for applications where the transmit signal power budget is sharply limited by the high-power amplifier. The paper presents both theoretical descriptions and analysis of the generated pseudo-noise signal as well as the results of an imaging measurement of a real space target using the introduced pseudo-noise signals. [ABSTRACT FROM AUTHOR]

Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target's motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target's effective rotation angle and the equivalent rotation angular velocity and then define the target's optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The synthesis of rain fields is essential in multiple research fields and applications, including Single‐image Derain. However, there is a lack of research on simulated rain fields, and the existing rain field generation models struggle to capture complex spatial distributions and generate truly random rain fields. To address this, the authors propose a generative adversarial networks‐based rain field generation network, which consists of a generator, a discriminator, and a feature extraction block that can produce realistic and complex rain fields. The authors' experiments demonstrate that this method achieves an average Frechet Inception Distance score of 0.035, and user studies indicate that the generated rain distribution looks naturally. [ABSTRACT FROM AUTHOR]

The Super Dual Auroral Radar Network (SuperDARN) consists of more than 30 monostatic high‐frequency (HF, 8–20 MHz) radars to study dynamic processes in the ionosphere. SuperDARN provides maps of global‐scale ionospheric plasma drift circulation from the mid‐latitudes to the poles. The conventional SuperDARN radars consecutively scan through 16 beam directions with a lower limit of 1 minute to sample the entire field of view. In this work, we use the advanced capabilities of the recently developed Borealis digital SuperDARN radar system. Combining a wide transmission beam with multiple narrow reception beams allows us to sample all conventional beam directions simultaneously and to speed up scanning of the entire field‐of‐view by up to 16 times without noticeable deterioration of the data quality. The wide‐beam emission also enabled the implementation of multistatic operations, where ionospheric scatter signals from one radar are received by other radars with overlapping viewing areas. These novel operations required the development of a new model to determine the geographic location of the source of the multistatic radar echoes. Our preliminary studies showed that, in comparison with the conventional monostatic operations, the multistatic operations provide a significant increase in geographic coverage, in some cases nearly doubling it. The multistatic data also provide additional velocity vector components, increasing the likelihood of reconstructing full plasma drift velocity vectors. The developed operational modes can be readily implemented at other fully digital SuperDARN radars. Plain Language Summary: The Super Dual Auroral Radar Network (SuperDARN) consists of more than 30 high‐frequency (HF, 8–20 MHz) radars that observe the ionized part of the Earth's atmosphere to study Space Weather phenomena, like magnetic storms and the aurora. Each SuperDARN radar transmits a radio wave at a particular frequency and receives the returns scattering from ionized structures. The returns are analyzed to extract information about processes in near‐Earth space. Conventionally, these radars scan 16 azimuthal directions consecutively, dwelling for 3.5 s in each direction, which restricts a scan of the field‐of‐view to 1 minute. In this work, we use a recently developed digital SuperDARN radar system called Borealis to shorten the scan duration by an order of magnitude by illuminating the whole field of view with a broad transmitter beam and receiving radar returns from all 16 directions simultaneously using narrow receiver beams. We also implemented a technique to receive signals sent from one radar by other radars with overlapping viewing areas. This provides a significant increase (nearly doubles) the geographic area of coverage, compared with using the same radar for both transmission and reception. Key Points: We developed wide‐beam transmission to probe an entire Super Dual Auroral Radar Network (SuperDARN) field of view simultaneously and to enable bistatic operationsNew multistatic operation of SuperDARN radars provides additional measured velocity vector componentsNew wide‐beam and multistatic operations significantly improve temporal resolution and spatial coverage by SuperDARN [ABSTRACT FROM AUTHOR]

Long baseline bistatic radar systems herald enhanced sensitivity and metric accuracy for space objects in geosynchronous orbits and beyond. Radio telescopes are ideal participants in such a system; in particular, they often feature large apertures with low‐noise temperatures and have stable, synchronised clocks. Pairing radio telescopes with high‐power radars creates new methodologies for Space Domain Awareness. This paper describes long baseline bistatic measurements using the Millstone Hill Radar in the USA, the Tracking and Imaging Radar in Germany, multiple receivers of the enhanced multi‐element remotely linked interferometer network array in the United Kingdom, and the Westerbork Synthesis Radio Telescope in the Netherlands. The authors, a Research Task Group formed by the NATO Science and Technology Organisation Sensors and Electronic Technology Panel (SET‐293), performed novel bistatic and monostatic radar imaging experiments with real on‐orbit tumbling rocket bodies. These experiments on tumbling objects at near‐geosynchronous orbits highlight successful demonstrations of advanced bistatic Doppler characterisation across diverse imaging geometries. Specialised Doppler processing on tumbling targets, such as the Doppler superpulse algorithm, enables high‐fidelity rotation period estimation and determination of minimum target size. [ABSTRACT FROM AUTHOR]

An important aspect of Space Situational Awareness is to estimate the intent of objects in space. This paper discusses how discriminating features can be obtained from Inverse Synthetic Aperture Radar images of such objects and how these discriminators can be used to recognise the objects or to estimate their intent. If the object is, for example, a satellite of a known type, the scheme proposed is able to recognise it. The ability of the scheme to detect damage to the object is also discussed. The focus is on imagery obtained in the sub‐terahertz band (typically 300 GHz) because of the greater imaging capability given by the diffuse scattering which is observed at these frequencies. The paper also discusses the importance of being able to use images obtained by electromagnetic simulation to be able to train the subsystem which recognises features of the objects and describes a practical scheme for creating these simulations for large objects at these very short wavelengths. [ABSTRACT FROM AUTHOR]

Radar forward-looking imaging has gained increasingly significance in diverse applications, including battlefield reconnaissance, target surveillance, and precision guidance. While Synthetic Aperture Radar techniques are widely employed to achieve high azimuth resolution, the domain of forward-looking imaging has its challenges. These challenges arise due to the minimal Doppler frequency changes in forward-looking areas and the symmetrical Doppler history about flight path. These constraints negatively impact the radar's ability to accurately detect and image targets in forward-looking areas. Consequently, there has been a growing interest in the development of novel approaches to these problems. Due to the application of deep learning, image super-resolution methods based on convolutional neural networks (CNNs) have received extensive attention and demonstrated good performance. This avenue of research provides fresh insights into improving the azimuth resolution of radar imaging. In this letter, we propose a novel convolutional neural network, called MSUS-Net (Multi-scale U-shaped Network), for radar forward-looking imaging. It combines asymmetric feature fusion with a unique connection structure to replace traditional layers. Moreover, different from conventional image restoration networks that use content loss, we use the L1 distance in the frequency domain, restoring missing high-frequency details. Simulated and real radar data validate that this approach offers robust recovery and better imaging performance. [ABSTRACT FROM AUTHOR]

Fast settling phase locked loops (PLLs) play a pivotal role in many applications requiring rapid attainment of a stable frequency and phase. In modern communication standards, these PLLs are extensively utilized to guarantee precise compliance with dynamic resource allocation requirements. In processors, these PLLs manage dynamic voltage frequency scaling. Moreover, the fast-settling PLLs expedite the scanning of frequency spectra in sophisticated electronic radar set-ups, proving particularly advantageous for imaging and scanning radar applications. The rapid response exhibited by these PLLs is also harnessed in quantum technologies, catering to the urgent need for precise frequency adjustments to manipulate qubit states effectively. The strategies employed to attain fast-settling PLLs are primarily classified into five broad techniques in this article: enhanced phase frequency detection, hybrid multiple subsystems, VCO start-up, gear shift, and look-up table or finite state machine. This article explores the fundamental operational principles encompassing these techniques and presents optimal settling times for each method reported in the literature. Finally, the architectures utilizing these techniques will be evaluated based on their figure of merit (FoM), settling time, and tuning range. [ABSTRACT FROM AUTHOR]

Abstract The problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model‐driven learning imaging network with a complex‐valued convolution block attention module plugged into each sub‐network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple‐Input Multiple‐Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.

The problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model‐driven learning imaging network with a complex‐valued convolution block attention module plugged into each sub‐network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple‐Input Multiple‐Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.An improved model‐driven learning imaging network with a complex‐valued convolution block attention module plugged into each sub‐network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple‐Input Multiple‐Output (MIMO) radar imaging. In addition, it can better focus on target area and capture target information to boost model representation power. [ABSTRACT FROM AUTHOR]

Abstract Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation systems. The system uses a group of image processing tools for video pre-processing, and Kalman filtering to do the main task. For testing the system performance, two measures of accuracy and false alarms probability are computed for real vision data. Two types of scenes are analyzed including the scene with single target, and the scene with multiple targets that is more complicated for automatic target detection and tracking systems. The proposed system has achieved a high performance in our tests.

With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.

New satellite constellations for broadband Internet service emerge as promising illuminators of opportunity for the of passive radar (PR) applications. Their wideband signals, high power flux at ground level, and theoretical global availability anticipate ameliorated performance compared to preexisting satellite-based PRs. On the other hand, the constellation deployment in low Earth orbits generates favorable movements for the radar imaging of the Earth's surface. In this article, the Starlink constellation is considered for the formation of passive SAR images. The Starlink datalink concerning the data transmission from the satellite to the user's terminal is selected as the illumination signal. The users' downlink signal characteristics are studied using experimental data from a dedicated measurement campaign, confirming its suitability for radar applications. Then, the time-variant radar geometry and the passive SAR processing scheme are described. A back projection radar image formation approach is selected to tackle the complexity of the bistatic geometry. The system validation is performed in a second measurement campaign concerning dual-channel measurements using the Starlink-based PR demonstrator developed at the Warsaw University of Technology. The first Starlink-based passive SAR images are obtained under direct satellite illumination of the PR deployment area and under adjacent-cell illumination conditions. The experimental results confirm the feasibility of exploiting Starlink transmissions for the formation of passive SAR images of urban areas.

In arc array bistatic synthetic aperture radar (AA-BiSAR), an accelerated moving transmitter is helpful for enlarging the imaged scene and improving the flexibility of the imaging system, which is of great significance in the field of helicopter-assisted landing and emergency rescue. The motion state of the transmitter is closely related to the SAR echo model and the imaging quality. In this article, the imaging geometry of AA-BiSAR with an accelerated moving transmitter is established, and the impact of the transmitter acceleration on the imaging quality is analyzed. According to the echo signal model, a two-step compensation imaging algorithm based on the series reversion method is proposed. First, the first compensation for the residual phase caused by the movement of the transmitter is operated in the azimuth-time range-frequency domain by the transfer function, which is obtained from the approximated fourth-order high-precision Taylor series expansion of the double-root bistatic slant range. Then, the second compensation for the coupling terms between angle and range frequencies are carried out in the two-dimensional (2-D) frequency domain by uniform range migration and second range compression functions. The 2-D spectrum is derived by the series inversion method. Imaging results of simulated point and distributed targets are validated by the proposed imaging method.

Automotive radar systems face challenges in generating high-resolution images that are essential for advancing autonomous driving technology. One promising solution to improve the angle resolution of radar images is the synthetic aperture radar (SAR) technique. However, achieving satisfactory SAR images involves overcoming difficulties such as high computational burden and accurate platform location determination. To address these challenges, we propose an innovative approach that integrates SAR imaging with digital beamforming (DBF) and multiple input multiple output (MIMO) techniques. The proposed approach significantly reduces the computational time required for SAR image formation and demonstrates superior phase error suppression compared to conventional methods. Our implemented algorithm reduces the number of radar samples and imaging complexity by up to a factor of 10 without compromising resolution and image quality. Furthermore, our proposed angle variant phase correction method can be used in challenging automotive scenarios to efficiently mitigate the effects of platform position inaccuracies and undesirable motions. Through simulations and practical experiments, we present promising results to highlight the advantages of combining real and synthetic apertures for radar imaging and phase error correction.

This article uses the inverse synthetic aperture radar (ISAR) imaging method to present real-world tests on 3-D radar imaging of noncooperative air targets. Initially, the fundamentals of 3-D ISAR are introduced. This is followed by a discussing of the challenges of obtaining high-quality 3-D radar images. An essential feature of the applied method is its basis on the back-projection family of techniques, eliminating the need for iterative image reconstruction. These theoretical concepts are validated using both simulations and real-life signals. This article also provides insights into the measurement campaign and the signal processing techniques applied to achieve the presented results.

Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation systems. The system uses a group of image processing tools for video pre-processing, and Kalman filtering to do the main task. For testing the system performance, two measures of accuracy and false alarms probability are computed for real vision data. Two types of scenes are analyzed including the scene with single target, and the scene with multiple targets that is more complicated for automatic target detection and tracking systems. The proposed system has achieved a high performance in our tests. [ABSTRACT FROM AUTHOR]

This study presents a method for short‐range synthetic aperture radar (SAR) imaging employing a fast adaptive plug‐and‐play framework based on the alternating direction method of multipliers (ADMM). The proposed approach capitalizes on the advanced ADMM‐based image‐solving model to enhance the efficiency of SAR image reconstruction. By integrating adaptive parameter tuning with a plug‐and‐play strategy, the algorithm dynamically adjusts regularization parameters, resulting in enhanced convergence and superior image quality. The methodology exploits the inherent flexibility and speed of the ADMM algorithm, offering a computationally efficient solution tailored for short‐range SAR imaging applications. Experimental results demonstrate the effectiveness of the proposed approach in achieving high‐quality SAR reconstructions while accommodating variations in scene complexity and noise levels. [ABSTRACT FROM AUTHOR]

Monostatic rapid single-view radar imaging technology is a technique that employs single incidence angle and single frequency point information to implement rapid monostatic radar imaging within a small angular field. Owing to its analytical expression, this technique can substitute the traditional frequency-angle- scanning imaging in a small angular range, facilitating the rapid generation of highly realistic radar imaging data slices for complex targets and environments. This technology has been significantly applied in scatter hotspot diagnostics and target recognition. In order to achieve the windowing effect equivalent to that of frequency-angle-scanning imaging, and to enhance the scattering feature of monostatic imaging while controlling sidelobes, this paper derives analytic windowed imaging formulas for monostatic radar. It then obtains analytical expressions for various typical monostatic windowing rapid radar imaging scenarios. This enables the monostatic rapid imaging technology to maintain high efficiency in its analytical expressions while achieving the windowing effect equivalent to traditional imaging. The validity and correctness of the analytical formula and software implementation have been confirmed through 1D, 2D, and 3D imaging verifications. This technology can provide a vast amount of training data for modern radars. [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]

Aiming to solve the bottleneck problem of electromagnetic scattering simulation in the scenes of extremely large-scale seas and ships, a high-frequency method by using graphics processing unit (GPU) parallel acceleration technique is proposed. For the implementation of different electromagnetic methods of physical optics (PO), shooting and bouncing ray (SBR) and physical theory of diffraction (PTD), a parallel computing scheme based on the central processing unit (CPU)-GPU parallel computing scheme is realized to balance computing tasks. Finally, a multi-GPU framework is further proposed to solve the computational difficulty caused by the massive number of ray tubes in the ray tracing process. By using the established simulation platform, signals of ships at different seas are simulated and their images are achieved as well. It is shown that the higher sea states degrade the averaged peak signal-to-noise ratio (PSNR) of radar image.

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.

Abstract Due to the excellent characteristics of Synthetic Aperture Radar (SAR), it has become an important technical means for land and sea target detection applications worldwide. However, the SAR image imaging process is often accompanied by speckle noise, which affects the accuracy of target detection. For this problem, this paper proposes an SAR image reconstruction model based on image denoising and feature extraction. Given a noisy SAR image, at each pixel of the image, the value of the singularity index of a metric defined by the signal gradient norm density is calculated. Then through a hierarchical decomposition of these singular indices, a set of pixels containing the most informative feature set of the image is extracted, and a denoised image is reconstructed using the extracted features. In comparison to traditional filtering and denoising techniques such as non‐local filtering, this algorithm achieves remarkable local filtering and denoising effects while preserving the image's texture information. The denoising performance of the proposed method is evaluated through a series of quantitative indicators and visual comparisons. The results demonstrate that this method effectively reduces background clutter, resulting in a higher contrast between the target and the background.

Tomographic synthetic aperture radar (TomoSAR) possesses 3D imaging capability, making it significant for building reconstruction using TomoSAR data. The reconstruction algorithm is closely related to building point cloud detection, while traditional detection methods suffer from low automation and reliance on manual configuration. This study proposes a building point cloud reconstruction method based on deep learning semantic segmentation. Initially, deep learning method is employed for end‐to‐end building point cloud segmentation, followed by point cloud reconstruction based on the segmentation results. The proposed method is simple and efficient, elevating the level of automation in point cloud processing. Experimental validation on real TomoSAR data confirms that the proposed method achieves automated and refined reconstruction of building point clouds. [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]

Synthetic aperture radar (SAR) simulation is a vital tool for planning SAR missions, interpreting SAR images, and extracting valuable information. SAR imaging is essential for analyzing sea scenes, and the accuracy of sea surface and scattering models is crucial for effective SAR simulations. Traditional methods typically employ empirical formulas to fit sea surface scattering, which are not closely aligned with the principles of electromagnetic scattering. This paper introduces a novel approach by constructing multiple sea surface models based on the Pierson–Moskowitz (P-M) sea spectrum, integrated with the stereo wave observation projection (SWOP) expansion function to thoroughly account for the influence of wave fluctuation characteristics on radar scattering. Utilizing the shooting and bouncing ray-physical optics (SBR-PO) method, which adheres to the principles of electromagnetic scattering, this study not only analyzes sea surface scattering characteristics under various sea conditions but also facilitates the computation of scattering coupling between multiple targets. By constructing detailed scattering distribution data, the method achieves high-precision SAR simulation results. The scattering model developed using the SBR-PO method provides a more nuanced description of sea surface scenes compared to traditional methods, achieving an optimal balance between efficiency and accuracy, thus significantly enhancing sea surface SAR imaging simulations.

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided.

Abstract This study presents a method for short‐range synthetic aperture radar (SAR) imaging employing a fast adaptive plug‐and‐play framework based on the alternating direction method of multipliers (ADMM). The proposed approach capitalizes on the advanced ADMM‐based image‐solving model to enhance the efficiency of SAR image reconstruction. By integrating adaptive parameter tuning with a plug‐and‐play strategy, the algorithm dynamically adjusts regularization parameters, resulting in enhanced convergence and superior image quality. The methodology exploits the inherent flexibility and speed of the ADMM algorithm, offering a computationally efficient solution tailored for short‐range SAR imaging applications. Experimental results demonstrate the effectiveness of the proposed approach in achieving high‐quality SAR reconstructions while accommodating variations in scene complexity and noise levels.

With the number of new satellites increasing dramatically, comprehensive space surveillance is becoming increasingly important. Therefore, high-resolution inverse synthetic aperture radar (ISAR) imaging of satellites can provide an in-situ assessment of the satellites. This paper demonstrates that pseudo-noise signals can also be used for satellite imaging, in addition to classical linear frequency-modulated chirp signals. Pseudo-noise transmission signals offer the advantage of very low cross-correlation values. This, for instance, enables the possibility of a system with multiple channels transmitting instantaneously. Furthermore, it can significantly reduce signal interference with other systems operating in the same frequency spectrum, which is of particular interest for high-bandwidth, high-power systems such as satellite imaging radars. A new routine has been introduced to generate a wideband pseudo-noise signal with a peak-to-average power ratio (PAPR) similar to that of a chirp signal. This is essential for applications where the transmit signal power budget is sharply limited by the high-power amplifier. The paper presents both theoretical descriptions and analysis of the generated pseudo-noise signal as well as the results of an imaging measurement of a real space target using the introduced pseudo-noise signals.

Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target’s motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target’s effective rotation angle and the equivalent rotation angular velocity and then define the target’s optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm.