Search Results (343)

High-resolution three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging is essential for the characterization of target scattering in various environments. The practical application of this technique is frequently impeded by the lengthy measurement time necessary for comprehensive data acquisition with turntable-based systems. Sub-sampling the aperture can decrease acquisition time; however, traditional reconstruction algorithms that utilize matched filtering exhibit significantly impaired imaging performance, often characterized by a high peak side-lobe ratio. A methodology is proposed that integrates compressed sensing(CS) theory with sparse-aperture optimization to achieve high-fidelity 3D imaging from sparsely sampled data. An optimized sparse sampling aperture is introduced to systematically balance the engineering requirement for efficient, continuous turntable motion with the low mutual coherence desired for the CS matrix. A deep Bayesian optimization framework was developed to automatically identify physically realizable optimal sampling trajectories, ensuring that the sensing matrix retains the necessary properties for accurate signal recovery. This method effectively addresses the high-sidelobe problem associated with traditional sparse techniques, significantly decreasing measurement duration while maintaining image quality. Quantitative experimental results indicate the method’s efficacy: the optimized sparse aperture decreases the number of angular sampling points by roughly 84% compared to a full acquisition, while reconstructing images with a high correlation coefficient of 0.98 to the fully sampled reference. The methodology provides an effective solution for rapid, high-performance 3D ISAR imaging, achieving an optimal balance between data acquisition efficiency and reconstruction fidelity. Full article

Parametric optimization/inversion of Interferometric Synthetic Aperture Radar (InSAR) measurements enables the modeling of the volcanic deformation source by considering the approximation of the analytic formulations or by defining refined scenarios within a Finite Element (FE) framework. However, the geodetic data modeling can lead to ambiguous solutions when constraints are unavailable, turning out to be time-consuming. In this work, we use an integrated multiscale approach for retrieving the geometric parameters of volcanic deformation sources and then constraining a Monte Carlo optimization of FE parametric modeling. This approach allows for contemplating more physically complex scenarios and more robust statistical solutions, and significantly decreasing computing time. We propose the Campi Flegrei caldera (CFc) case study, considering the 2019–2022 uplift phenomenon observed using Sentinel-1 satellite images. The workflow firstly consists of applying the Multiridge and ScalFun methods, and Total Horizontal Derivative (THD) technique to determine the position and horizontal sizes of the deformation source. We then perform two independent cycles of parametric FE optimization by keeping (I) all the parameters unconstrained and (II) constraining the source geometric parameters. The results show that the innovative application of the integrated multiscale approach improves the performance of the FE parametric optimization in proposing a reliable interpretation of volcanic deformations, revealing that (II) yields statistically more reliable solutions than (I) in an extraordinary tenfold reduction in computing time. Finally, the retrieved solution at CFc is an oblate-like source at approximately 3 km b.s.l. embedded in a heterogeneous crust. Full article

This paper reveals a counterintuitive, non-monotonic dependence of terahertz coded-aperture imaging (TCAI) performance on the imaging range. This phenomenon stems from phase-induced spatiotemporal correlations in the reference-signal matrix (RSM), governed by the wavefront phase interactions between the coded-aperture elements and scatterers on the imaging plane. Image quality deteriorates noticeably when a specific dimensionless criterion, which is defined mathematically and physically in this work, precisely reaches integer values. Under such conditions, the relative phase difference concentrates or clusters into discrete values determined by the imaging range, leading to strong column and row correlations in RSM that compromise the spatiotemporal independence essential for high-quality reconstruction. For imaging ranges exceeding the critical threshold determined by the number of grid points along one dimension of the imaging plane, two degradation mechanisms emerge: increased correlation between RSM columns mapping to directly adjacent scatterers and phase coverage reduction in wavefront encoding. Both effects intensify as the imaging range increases, resulting in a monotonic deterioration of imaging performance. Crucially, reconstruction fails primarily when strong correlations involve dominant scatterers, whereas correlations among non-dominant (dummy) scatterers have a negligible impact. The Two-step Iterative Shrinkage/Thresholding (TwIST) algorithm demonstrates superior robustness under these challenging conditions compared to some other conventional methods. These insights provide practical guidance for optimizing TCAI system design and operational range selection to avoid performance degradation zones. Full article

As a significant energy source enabling the global energy transition, efficient shale gas development is critical for diversifying supplies and reducing carbon emissions. Zipper fracturing widely enhances the stimulated reservoir volume (SRV) by generating complex fracture networks of shale reservoirs. However, recent trends of reduced well spacing and increased injection intensity have significantly intensified interwell interference, particularly fracture-driven interactions (FDIs), leading to early production decline and well integrity issues. This study develops a fully coupled hydro–mechanical–damage (HMD) numerical model incorporating an explicit discrete fracture network (DFN), opening and closure of fractures, and an aperture–permeability relationship to capture the nonlinear mechanical behavior of natural fractures and their role in FDIs. After model validation, sensitivity analyses are conducted. Results show that when the horizontal differential stress exceeds 12 MPa, fractures tend to propagate as single dominant planes due to stress concentration, increasing the risks of FDIs and reducing effective SRV. Increasing well spacing from 60 m to 110 m delays or eliminates FDIs while significantly improving reservoir stimulation. Fracture approach angle governs the interaction mechanisms between hydraulic and natural fractures, influencing the deflection and branching behavior of primary fractures. Injection rate exerts a dual influence on fracture extension and FDI risk, requiring an optimized balance between stimulation efficiency and interference control. This work enriches the multi-physics coupling theory of FDIs during fracturing processes, for better understanding the fracturing design and optimization in shale gas production. Full article

Synthetic Aperture Radar (SAR) target detection models are highly vulnerable to adversarial attacks, which significantly reduce detection performance and robustness. Existing adversarial SAR target detection approaches mainly focus on the image domain and neglect the critical role of signal propagation, making it difficult to fully capture the connection between the physical space and the image domain. To address this limitation, we propose an Echo Signal-Guided Adversarial Example Generation method for SAR target detection (SAR-ESAE). The core idea is to embed adversarial perturbations into SAR echo signals and propagate them through the imaging and inverse scattering processes, thereby establishing a unified attack framework across the signal, image, and physical spaces. In this way, perturbations not only appear as pixel-level distortions in SAR images but also alter the scattering characteristics of 3D target models in the physical space. Simulation experiments in the Scenario-SAR dataset demonstrate that the SAR-ESAE method reduces the mean Average Precision of the YOLOv3 model by 23.5% and 8.6% compared to Dpatch and RaLP attacks, respectively. Additionally, it exhibits excellent attack effectiveness in both echo signal and target model attack experiments and exhibits evident adversarial transferability across detection models with different architectures, such as Faster-RCNN and FCOS. Full article

This paper presents a physics-guided machine learning (PGML) approach to model the I–V characteristics of GaN current aperture vertical field effect transistors (CAVET). By adopting the method of transfer learning and the shortcut structure, a physically guided neural network model is established. The shallow neural network with $tanh$ as the basis function is combined with a hypernetwork that dynamically generates its weight parameters. The influence of transconductance is added to the loss function. This model can synchronously predict the output and transfer characteristics of the device. Under the condition of small samples, the prediction error is controlled within 5%, and the $R^2$ value reaches above 0.99. The proposed PGML approach outperforms conventional approaches, ensuring physically meaningful and robust predictions for device optimization and circuit-level simulations. Full article

In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions. Full article

Floods are among the most frequent and damaging hazards worldwide, with impacts intensified by climate change and rapid urban growth. This review analyzes how satellite-based Earth Observation (EO) technologies are evolving to meet operational needs in flood detection and water depth estimation, with a focus on the Copernicus Emergency Management Service (CEMS) as a mature and widely adopted European framework. We compare the capabilities of conventional EO datasets—optical and Synthetic Aperture Radar (SAR)—with 3D geospatial datasets such as high-resolution Digital Elevation Models (DEMs) and Light Detection and Ranging (LiDAR). While 2D EO imagery is essential for rapid surface water mapping, 3D datasets add volumetric context, enabling improved flood depth estimation and urban impact assessment. LiDAR, in particular, can capture microtopography between high-rise structures, but its operational use is constrained by cost, data availability, and update frequency. We also review how artificial intelligence (AI), including machine learning and deep learning, is enhancing automation, generalization, and near-real-time processing in flood mapping. Persistent gaps remain in model transferability, uncertainty quantification, and the integration of scarce high-resolution topographic data. We conclude by outlining a roadmap towards hybrid frameworks that combine EO observations, 3D datasets, and physics-informed AI, bridging the gap between current technological capabilities and the demands of real-world emergency management. Full article

Recent advances in time-modulated metasurfaces (TMMs) have introduced approaches for controlling target features in radar imaging. These technologies enable dynamic reconstruction of scattering center locations and intensities by flexibly manipulating radar echoes. However, most existing methods focus on amplitude and phase modulation, lacking joint control over the polarimetric scattering characteristics of targets. As a result, the modulated outputs tend to exhibit limited polarimetric diversity and remain strongly tied to the targets’ physical structures. To address this limitation, this paper proposes a modulation method for polarimetric synthetic aperture radar (PolSAR) images based on a flexible metasurface with independently controllable polarizations (FM-ICP). The method independently controls the echo energy distribution in two polarization channels, enabling target representations in PolSAR images to exhibit polarimetric characteristics beyond their physical geometry—for example, rendering a flat plate as a cylinder, or vice versa. In addition, the method can generate synthetic scattering centers with controllable locations and polarimetric properties, which can be precisely tuned via modulation parameters. This work offers a practical approach for target feature manipulation and shows potential in PolSAR image simulation and feature reconstruction. Full article

This paper proposes a multi-scale time-frequency representation fusion network (MTRFN) for target recognition in synthetic aperture radar (SAR) imagery. Leveraging the spectral characteristics of six radar sub-views, the model incorporates a multi-scale representation fusion (MRF) module to extract discriminative frequency-domain features from two types of radar sub-views with high learnability. Additionally, physical scattering characteristics in SAR images are captured via time-frequency domain analysis. To enhance feature integration, a gated fusion network performs adaptive feature concatenation. The MRF module integrates a lightweight residual block to reduce network complexity and employs a coordinate attention mechanism to prioritize salient targets in the frequency spectrum over background noise, aligning the model’s focus with physical scattering principles. Furthermore, the model introduces an angular additive margin loss function during classification to enhance intra-class compactness and inter-class separability while reducing computational overhead. Compared with existing interpretable methods, the proposed approach combines architectural transparency with physical interpretability, thereby lowering the risk of recognition errors. Extensive experiments conducted on four public datasets demonstrate that the proposed MTRFN significantly outperforms existing benchmark methods. Comparative experiments using heat maps further confirm that the proposed physical feature-guided module effectively directs the model’s attention toward the target rather than the background. Full article

The challenge of achieving high-precision direction-of-arrival (DOA) estimation with enhanced degrees of freedom (DOFs) under a limited number of physical array elements remains a critical issue in array signal processing. To address this limitation, this paper makes the following three key contributions: (1) a novel moving sparse array synthesis model incorporating time-frequency-spatial joint processing for coprime frequencies signal sources; (2) an optimized coprime frequencies-based unmanned aerial vehicle array (CF-UAVA) configuration with derived closed-form expressions for the distribution of synthesized array; and (3) two DOA estimation methods: a group sparsity-based approach universally applicable to the proposed aperture synthesis model and a joint group sparsity and virtual array interpolation tailored for the proposed CF-UAVA configuration. Comprehensive simulation results demonstrate the superior DOA estimation accuracy and increased DOFs achieved by our proposed aperture synthesis model and DOA estimation algorithms compared to conventional approaches. Full article

The overtopping-induced lateral erosion breaching of tailings dams represents a critical disaster mechanism threatening structural safety, particularly in reinforced fine-grained tailings dams where erosion behaviors demonstrate pronounced water–soil coupling characteristics and material anisotropy. Through physical model tests and numerical simulations, this study systematically investigates lateral erosion failure patterns of reinforced fine-grained tailings under overtopping flow conditions. Utilizing a self-developed hydraulic initiation test apparatus, with aperture sizes of reinforced geogrids (2–3 mm) and flow rates (4–16 cm/s) as key control variables, the research elucidates the interaction mechanisms of “hydraulic scouring-particle migration-geogrid anti-sliding” during lateral erosion processes. The study revealed that compared to unreinforced specimens, reinforced specimens with varying aperture sizes (2–3 mm) demonstrated systematic reductions in final lateral erosion depths across flow rates (4–16 cm/s): 3.3–5.8 mm (15.6−27.4% reduction), 3.1–7.2 mm (12.8–29.6% reduction), 2.3–11 mm (6.9–32.8% reduction), and 2.5–11.4 mm (6.2–28.2% reduction). Smaller-aperture geogrids (2 mm × 2 mm) significantly enhanced anti-erosion performance through superior particle migration inhibition. Concurrently, a pronounced positive correlation between flow rate and lateral erosion depth was confirmed, where increased flow rates weakened particle erosion resistance and exacerbated lateral erosion severity. The numerical simulation results are in basic agreement with the lateral erosion failure process observed in model tests, revealing the dynamic process of lateral erosion in the overtopping breach of a reinforced tailings dam. These findings provide critical theoretical foundations for optimizing reinforced tailings dam design, construction quality control, and operational maintenance, while offering substantial engineering applications for advancing green mine construction. Full article

A deep reinforcement learning (DRL)-based power allocation method is proposed to achieve a low probability of intercept (LPI) in a netted synthetic aperture radar (SAR). To provide a physically meaningful and intuitive assessment of a netted radar for LPI performance, a netted circular equivalent vulnerable radius (NCEVR) is proposed and adopted. For SAR detection performance, the resolution, signal-to-noise ratio in a single pulse, and signal-to-noise ratio in SAR imaging are integrated at the task level. The LPI performance is achieved by minimizing NCEVR within the constraints of SAR detection performance. The powers in multiple moments are optimized using the DRL proximal policy optimization algorithm with the designed reward and observation. A DRL-based solver is provided for LPI radar, which handles problems that are difficult to optimize using traditional methods. The effectiveness is verified by simulations. Full article

A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies. Full article

Integrated Sensing and Communication (ISAC) has been enhanced to serve as a pivotal enabler for next-generation communication systems. In the context of target area detection, a UAV-SAR (Unmanned Aerial Vehicle–Synthetic Aperture Radar) based ISAC system, which shares both physical infrastructure and spectrum, can enhance the utilization of spectrum and hardware resources. However, existing studies on UAV-SAR-based ISAC systems for target imaging remain limited. In this study, we first established an ISAC mechanism to enable SAR imaging and communication. Then, we analyzed the energy consumption model, which includes both UAV propulsion and ISAC energy consumption. To maximize system energy efficiency, we propose an optimization method based on sequential convex optimization with linear state-space approximation. Furthermore, we propose a plan with general constraints, including the initial and final positions, the signal-to-noise ratio (SNR) constraint for SAR imaging, the data transmission rate constraint, and the total power limitation of the UAV. To achieve maximum energy efficiency, we jointly optimized the UAV’s trajectory, velocity, communication beamforming, sensing beamforming, and power allocation. Numerical results demonstrate that compared to existing benchmarks and PSO algorithms, the proposed method significantly improves the energy efficiency of UAV-SAR-based ISAC systems through optimized trajectory design. Full article