Harnessing high-precision spaceborne InSAR data, this study investigates the seismic impacts of the Ms 6.9 Menyuan earthquake in Qinghai, China, on January 8, 2022. The earthquake occurred at the intersection of the Lenglongling (LLLF) and Tuolaishan (TLSF) faults within the Qilian Haiyuan Fault (QL-HYF) zone, causing extensive infrastructure damage but no fatalities. Previous studies explored the step-over rupture zone and slip distribution of the Menyuan event but often relied on oversimplified rectangular dislocation models, insufficient for capturing complex fault ruptures. This simplification impedes accurate representation of curved fault segments in the QL-HYF zone, leading to unclear slip distribution estimates, particularly at the transition from LLLF strike-slip to TLSF thrust behavior. To address these limitations, this study employs a 3D triangulated angular dislocation slip-inversion approach in an isotropic half-space, enabling precise modeling of curved fault geometries. Leveraging Differential InSAR (D-InSAR) and Pixel Offset Tracking (POT), we reconstructed the earthquake’s 3D displacement field and extracted surface fault traces, informing our angular dislocation model for accurate coseismic slip distribution. Our results revealed significant horizontal displacement, with 38.5 cm of left-lateral movement accompanied by a 4 cm downward thrust. The slip model showed 2.7 m of slip along the LLLF and 0.8 m along the TLSF, concentrated at shallow depths between 2 and 7 km, highlighting surface rupture. The transition zone between the faults acted as a valve, modulating rupture progression and controlling energy release. These findings refine the understanding of coseismic deformation and slip distribution, supporting seismic hazard mitigation and emergency response strategies.

The 2023 Mw 6.8 Al Haouz earthquake struck Morocco's Atlas Mountains on September 8, causing over 3000 fatalities and extensive damage, revealing hidden seismic hazards in this slowly deforming region. Despite its impact, Al Haouz earthquake has received limited scientific investigation. The absence of surface rupture, its occurrence in an intraplate seismic silence zone, and ambiguous focal mechanisms have hindered understanding of the fault's kinematics. To address these gaps, our study employs the Interferometric Synthetic Aperture Radar (InSAR) technique to refine the coseismic deformation. We further propose two fault-dipping scenarios, northward and southward, reinforced by a unique local seismic dataset to evaluate the fault rupture characterization. Additionally, stress change analysis assessed the stress transfer effects between the mainshock and aftershocks, culminating in a comprehensive geodynamic model. Our findings reveal a northward-dipping reverse fault with a strike of 249.8 degrees, a dip of 66 degrees, and a rake of 55 degrees, exhibiting a maximum slip of 1.75 m. Stress change analysis demonstrates that stress transfer from the mainshock reactivated pre-existing faults, particularly the Tizi n'Test fault system, triggering shallow aftershocks in high-stress zones. We suggest that mantle upwelling, coupled with fluid injection along pre-existing faults, drives seismic dynamics in the region. The Tizi n'Test fault likely extends to the lithosphere-asthenosphere boundary, where active upwelling facilitates magma fluid intrusion, stimulating seismic activity. These findings are consistent with recent research, providing deeper insights into fault mechanics in the Atlas Mountains. They also highlight the significant contribution of satellite-based SAR techniques in uncovering hidden seismic hazards.

Due to the complex detection environment of ground-penetrating radar (GPR), time-domain algorithm (TDA) can accurately accommodate the wave path variations in layered medium, which presents significant potentials to achieve high focusing quality for GPR-synthetic aperture radar (GPR-SAR) imaging. However, the current TDAs will involve huge interpolation operations and bring redundant computational burden in the processing, which will inevitably degrade the algorithm performance. In this article, a fast subsurface Cartesian factorized back-projection (SCFBP) algorithm is proposed for GPR-SAR imaging, where the sub-image spectrum properties of the layered medium are investigated particularly and a two-step spectrum compression is developed according to the permittivity. As the sub-image spectrums are effectively aligned and compressed, only a very low Nyquist sampling rate (NSR) is required for the noninterpolated sub-image merging process, which will dramatically decrease the computational burden to achieve high performance both in efficiency and accuracy. Moreover, the inaccurate permittivity of the underground medium is particularly considered in GPR-SAR applications. By establishing the mapping relationship between the inaccurate permittivity of the underground medium and the introduced phase error in the imaging process, a fast echo-based compensation is developed with SCFBP, which can effectively eliminate the effects of the inaccurate permittivity on the imaging quality and accurately invert the permittivity of the underground medium for subsequent medium classification and identification. Promising results from both simulation and raw data experiments are presented and analyzed to validate the performance advantages of the proposed algorithm.

Synthetic aperture radar (SAR) images are often degraded by multiplicative speckle noise, hindering their processing and analysis. While convolutional neural networks (CNNs) have limited receptive fields, limiting global feature capture, Transformer-based methods struggle to differentiate between features and noise as network depth increases. We propose Diff Attn Multiscale Net (DAMSNet), a novel U-Net-based architecture designed for the efficient reduction of noise in SAR images while preserving fine details. Experiments on both synthetic and real SAR datasets show that DAMSNet has demonstrated superior performance over traditional methods and state of the art (SOTA) deep learning algorithms. To the best of our knowledge, DAMSNet is the first to incorporate a differential attention (Diff Attn) mechanism for SAR image despeckling.

A direction-of-arrival (DoA) estimation method based on the iterative adaptive approach (IAA) for high-resolution and computationally efficient 2-D DoA estimation in complex arrays is proposed in this article. The method initially utilizes the fast IAA (FIAA) to obtain azimuth information, which is then used to determine the target's location and angle matching. Subsequently, the select range fast iterative adaptive approachd (SFIAA) method is introduced. It estimates the angles within the azimuth range determined by FIAA, providing elevation information. Finally, by merging the information from both dimensions through angle matching, accurate estimation is achieved. The performance of the proposed method is extensively validated through simulation and practical testing, and comparisons are made with existing methods. The results demonstrate that the proposed method significantly reduces computational complexity without sacrificing performance, offering broad applicability and superiority.

The UNESCO Agenda 2030 emphasizes the preservation of cultural heritage sites, focusing on coastal heritage preservation, which still poses substantial difficulties. While earlier studies have addressed the overall consequences of natural hazards along the Alexandria coastline, there is a gap in how they specifically affect coastal heritage sites, e.g., the Qaitbay citadel (our case study). This work seeks to bridge this gap by assessing the critical hazards faced by the Qaitbay citadel, including crustal deformation due to tectonic events, earthquakes, and Sea Level Rise (SLR) resulting from climate change. To comprehensively assess these challenges, a stack of Sentinel-1 SAR datasets (2017-2021) was processed using the Persistent Scatterer InSAR (PS-InSAR) technique to conduct spatial and temporal deformation variations of the citadel's site and its buildings. GPS measurements of Alexandria's (e.g., ALX2) station were correlated with InSAR results within the same period. Furthermore, satellite altimetry data from 1993-2021 covering the citadel and surroundings were processed to highlight long-term SLR trends. The findings indicate 1) A subsidence rate of - 1 +/- 0.2 mm/yr, associated with the citadel's foundational structures and identified through the PS-InSAR analysis, was caused by load-bearing effects and sand migration beneath the citadel, 2) a vertical displacement of - 1.3 +/- 0.6 mm/yr obtained from ALX2-GPS station, consistent with the LOS velocity rate of PS-InSAR time series analysis at that specific location, 3) a SLR trend of +3.96 mm/yr, with notable peaks potentially related to episodes of Northern Ionian Gyre reversal, that could result in changes in water mass redistribution in the surrounding region.

Radar forward-looking super-resolution imaging is a hot spot in the field of radar imaging research. Restricted by Doppler bandwidth and platform size, traditional high-resolution synthetic aperture imaging and real aperture imaging are not suitable for forward-looking imaging, so a deconvolution-based radar forward-looking super-resolution imaging technology is proposed. The traditional methods currently used in the field of forward-looking deconvolution super-resolution imaging of scanning radar have poor ability to recover the texture details of the target image direction, but simply describe the errors of all measurement data uniformly, which leads to an increase in the result error and have poor ability to adapt to different scenarios. So, this article proposes an improved Tikhonov regularization direction total variation (DTV) deconvolution super-resolution algorithm based on Rayleigh entropy. The algorithm introduces the DTV operator to more accurately restore the edge texture details of the image, and adds a weight matrix to the loss function to more accurately reflect the error degree of each measurement value in the loss function. The entropy enhances the applicability of the algorithm in different scenarios, and significantly improves the radar's ability to recover targets in a low signal-to-noise ratio environment. Finally, the simulation data and measured data processing results show that compared with the traditional method in the field of scanning radar forward looking deconvolution super-resolution imaging, the algorithm proposed in this article is better.

This letter mainly considers the environmental clutter problem in distinguishing between stationary humans and animals through-wall circumstances. Focusing on the challenges of object identification in the time–frequency map, we propose a cross-scale feature aggregation (CSFA) network based on channel–spatial attention, which can improve the identification accuracy of stationary humans and animals. Specifically, life detection radar is utilized to collect data, and the time–frequency analysis method synchrosqueezing transform (SST) is used to suppress the signal noise and generate higher-resolution time–frequency maps. In order to make full use of the target information, we use a feature pyramid network (FPN) to obtain multilevel feature information maps from time–frequency maps. Then, the CSFA module is utilized to extract detailed micro-Doppler feature information from feature maps. And we use a deep convolutional neural network (CNN) to classify humans from animals. Experimental results show that the proposed model has a better performance in accuracy compared with the existing methods.

Multiple azimuth channels (MACs) synthetic aperture radar (SAR) can theoretically achieve high azimuth resolution and wide swath (HRWS). Nevertheless, in practice, channel mismatch will lead to ghost or azimuth ambiguities, which will degrade the imaging quality. This article proposes a novel idea for estimating the channel mismatch of MACs SAR in the image domain. First, we found that the degree of freedom (DOF) of MACs signals doubles after signal reconstruction and imaging. As a result, when the channel number is not great enough, the subspace method for error estimation is unable to be implemented. To deal with this problem, we introduce a DOF compression method based on spectral filtering. This method can decrease the image-domain DOF. Finally, an image-domain subspace method is proposed to estimate the channel phase error, using the focused data and selecting the high SNR region of SAR images. The proposed method has advantages for the channel phase error estimation. Simulated space-borne MACs SAR data and real measured airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

Spaceborne synthetic aperture radar (SAR) can operate at various modes, including stripmap mode, spotlight mode, sliding spotlight mode, and Terrain observation by progressive scans (TOPS) mode. These four imaging modes can be regarded as unified, differing in rotation-center ranges. To uniformly focus the data of these four imaging modes in real-time, this article proposes a real-time unified focusing algorithm (RT-UFA) for the multi-mode SAR via azimuth sub-aperture complex-valued image combining and scaling. The imaging processing can be performed while the data are being recorded. In the first stage of imaging, sub-aperture complex-valued images with relative low-resolution can be obtained by the cascade of the extended chirp scaling (ECS) and azimuth dechirp. Then, these complex-valued images are coherently combined by shifting the integer number of pixels, and thus the full-resolution image of all the recorded data can be obtained. The azimuth scaling and the pixels shifting in the RT-UFA are analyzed in detail. Simulation and SAR data results are presented to validate the analysis and RT-UFA.

This letter considers the coherent integration problem for a maneuvering target in low signal-to-noise-ratio (SNR) circumstances. Focusing on the range migration (RM) and Doppler frequency migration (DFM) problems caused by the motion of the target, we propose a new method called Radon-general linear chirplet transform (RGLCT). Jointly motion parameters search is employed to obtain the trajectory of the maneuvering target and the coherent integration is achieved via general linear chirplet transform (GLCT). Because of the nonsensitive-to-noise feature of the GLCT, RGLCT can realize weak target coherent integration in very low SNR environments. Multitarget detection can be achieved successfully because the GLCT is not influenced by the cross-term components. Finally, simulations and real data experiments are performed to demonstrate the effectiveness of the method. The results show that the proposed method has superior detection ability than methods, including Radon-Fourier transform (RFT) and Radon-Lv's distribution (RLVD). Both theory and experiments have fully proved that the proposed method can effectively realize coherent integration in low SNR environments.

The ability to detect and locate the moving object in a video is a fundamental procedure in applications of computer vision. However, these tracking methods still face some challenges, and are contradictory among different tasks. In this paper, a unified framework for joint moving object detection and tracking in the sky and underwater is proposed. This framework meets the requirements of two real applications: (i) tracking unmanned aerial vehicle (UAV) in the sky; and (ii) tracking unmanned underwater vehicle (UUV) in water. It consists of three key steps: (i) moving object detection by pixel classification; (ii) data association by blob detection; and (iii) object tracking by efficient convolution operator. Finally, analysis on the accuracy of the proposed framework is provided. Experimental results on real-world datasets and object tracking benchmark (OTB) demonstrate the advantage of the tracking method compared with some state-of-the-art trackers, in terms of accuracy and robustness. In addition, to the best of the authors’ knowledge, there is no previously published work for joint moving target detection and tracking in the sky and underwater.

This article presents a high-gain slotted ridge waveguide antenna array (SRWAA) with inductive diaphragms, which can realize a wide-sector beam with a low sidelobe simultaneously. The proposed antenna can cover a wide detection range and avoid interference from other directions. The expected excitation distribution for the antenna array is extracted through a beamforming method. To reduce the influence of the dispersion phenomenon on signal quality, inductive diaphragms are inserted into the sidewall of the ridge waveguide, which is fully analyzed from the point of the equivalent circuit. A cut-off-mode power divider is utilized, which can control the power ratio flexibly. An SRWAA working at 24.125 GHz, including a six-way feeding network, and a $6\times24$ slot array with the size of 330 mm $\times66.8$ mm is designed and fabricated. The measured sidelobe level (SLL) and half-power beamwidth (HPBW) in the elevation plane are −19.6 dB and 54.41°, with the counterparts in the azimuth plane −29.8 dB and 3.15°, respectively. The measured peak gain is 22.3 dBi at 24.125 GHz. The measured results are in good agreement with the simulated ones. This work has the potential to be applied in air detection, anti-unmanned aerial vehicles (UAVs), meteorological radar, and imaging radar.

Unmanned aerial vehicles (UAVs) play an essential role in various applications, such as transportation and intelligent environmental sensing. However, due to camera motion and complex environments, it can be difficult to recognize the UAV from its surroundings thus, traditional methods often miss detection of UAVs and generate false alarms. To address these issues, we propose a novel method for detecting and tracking UAVs. First, a cross-scale feature aggregation CenterNet (CFACN) is constructed to recognize the UAVs. CFACN is a free anchor-based center point estimation method that can effectively decrease the false alarm rate, the misdetection of small targets, and computational complexity. Secondly, the region of interest-scale-crop-resize (RSCR) method is utilized to merge CFACN and region-of-interest (ROI) CFACN (ROI-CFACN) further, in order to improve the accuracy at a lower computational cost. Finally, the Kalman filter is adopted to track the UAV. The effectiveness of our method is validated using a collected UAV dataset. The experimental results demonstrate that our methods can achieve higher accuracy with lower computational cost, being superior to BiFPN, CenterNet, YoLo, and their variants on the same dataset.

Due to the independence of azimuth-invariant assumption of an echo signal, time-domain algorithms have significant performance advantages for missile-borne synthetic aperture radar (SAR) focusing with curve moving trajectory. The Cartesian factorized back projection (CFBP) algorithm is a newly proposed fast time-domain implementation which can avoid massive interpolations to improve the computational efficiency. However, it is difficult to combine effective and efficient data-driven motion compensation (MOCO) for achieving high focusing performance. In this paper, a new data-driven MOCO algorithm is developed under the CFBP framework to deal with the motion error problem for missile-borne SAR application. In the algorithm, spectrum compression is implemented after a CFBP process, and the SAR images are transformed into the spectrum-compressed domain. Then, the analytical image spectrum is obtained by utilizing wavenumber decomposition based on which the property of motion induced error is carefully investigated. With the analytical image spectrum, it is revealed that the echoes from different scattering points are aligned in the same spectrum range and the phase error becomes a spatial invariant component after spectrum compression. Based on the spectrum-compressed domain, an effective and efficient data-driven MOCO algorithm is accordingly developed for accurate error estimation and compensation. Both simulations of missile-borne SAR and raw data experiment from maneuvering highly-squint airborne SAR are provided and analyzed, which show high focusing performance of the proposed algorithm.

Life detection radar is widely applied in life detection, especially in situations that demand searching life signal through ruins after disasters. In these circumstances, weak signal feature extraction and analysis are vital technologies. Because of the clutter of environment objects, life signal that received by radar is seriously weak. Traditional methods cannot separate life signal from clutter and noise. This paper propose an algorithm combined with feedback pulse canceller, multiple autocorrelation and synchronous squeeze S-transform (SSST). This method can effectively suppress the clutter and remove the environment noise, then obtain a high-resolution time-frequency distribution. In this way we can make best of life signal feature and classify the type of living target.

Due to the independence of azimuth invariance and high implementing efficiency, a fast time-domain algorithm has significant advantages for airborne bistatic synthetic aperture radar (BiSAR) data process with general geometric configuration. In this article, the practical problem of unexpected motion errors of the airborne platform is carefully analyzed under a fast factorized back-projection (FFBP) framework for a general BiSAR process and a coherent data-driven motion compensation (MOCO) algorithm integrated with FFBP is proposed. By utilizing wavenumber decomposition, the analytical spectrum of a polar grid image is obtained where the motion error can be conveniently investigated in image spectrum domain and the coherence between azimuthal phase error (APE) and motion-induced nonsystematic range cell migration (NsRCM) can be perfectly revealed. Then, a new data-driven MOCO method for both APE and NsRCM correction is developed with the FFBP process. Different from the data-driven MOCO in most frequency-domain algorithms, the residual NsRCM introduced by the FFBP process is particularly analyzed and addressed in the MOCO, which significantly improves the image quality in focusing. Promising results from both simulation and raw data experiments are presented and analyzed to validate the advantages of the proposed algorithm for the airborne BiSAR process.

Moving ship refocusing is challenging because the target motion parameters are unknown. Moreover, moving ships in squint synthetic aperture radar (SAR) images obtained by the back-projection (BP) algorithm usually suffer from geometric deformation and spectrum winding. Therefore, a spectrum-orthogonalization algorithm that refocuses moving ships in squint SAR images is presented. First, “squint minimization” is introduced to correct the spectrum by two spectrum compression functions: one to align the spectrum centers and another to translate the inclined spectrum into orthogonalized form. Then, the precise analytic function of the two-dimensional (2D) wavenumber spectrum is derived to obtain the phase error. Finally, motion compensation is performed in the two-dimensional wavenumber domain after the motion parameter is estimated by maximizing the image sharpness. This method has low computational complexity because it lacks interpolation and can be implemented by the inverse fast Fourier translation (IFFT) and fast Fourier translation (FFT). Processing results of simulation experiments and the GaoFen-3 squint SAR data validate the effectiveness of this method.

To improve the life-detection radar resolution under certain hardware conditions, in this letter, a deep mutual learning generative adversarial network model (Deep Mutual GAN) is proposed. In the proposed model, the generator can improve the angular resolution of the input low-resolution radar image by five times, which is enough to meet our requirements for the resolution of life detection. We innovatively use two generators in GAN with the same network structure and make the two generators learn from each other. In this way, the learning process of a generator is not only achieved by its confrontation with the discriminator but also guided by another generator. As a result, the knowledge of the generator is no longer only obtained through its own learning; each generator learns knowledge from another generator while learning knowledge by itself. The proposed model can effectively make the convergence of GAN more stable and improves the super resolution effect. We also introduce the details of the network structure of generator and discriminator, in which residual learning and a symmetrical network structure are applied. The experimental results show that the proposed method can achieve state-of-the-art imaging effect, which is meaningful for subsequent target detection and recognition.