Search Results (142)

To address the suboptimal radiometric calibration accuracy observed in specific beam codes of the GaoFen-3 (GF-3) series satellite for sea surface wind speed (SSWS) retrieval, this study introduces a calibration constant correction method based on the geophysical model function (GMF). This approach enables high-precision SSWS retrieval from GF-3B data. Conventional SAR-based SSWS retrieval models typically rely on pointwise mapping relationships, which overlook the spatial characteristics inherent in dynamic sea surface wind fields. To overcome this limitation, this study proposes an attention-guided dual-branch feature-fusion network (ADBFF-NET). The first branch, implemented as a backpropagation neural network (BPNN), learns nonlinear mappings between the normalized radar cross-section (NRCS, σ⁰), incidence angle, azimuth look direction, and wind vectors (speed and direction). The second branch, designed as a residual convolutional neural network, extracts spatial features of wind fields. An attention mechanism fuses the outputs of both branches, thereby enhancing retrieval accuracy. Experiments conducted with GF-3 series satellite data were validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5), Advanced Scatterometer (ASCAT) wind fields, and altimeter-derived wind speeds. The results indicate that the SSWS retrieved from GF-3B SAR data using the corrected calibration constants achieve a root mean square error (RMSE) of 1 m/s against ERA5 wind speeds, representing an approximately 40% reduction compared with the RMSE obtained using the original calibration constant. Furthermore, compared to ERA5 and ASCAT data, the RMSE of the wind speeds retrieved by the ADBFF-NET model reaches 1.17 m/s and 1.03 m/s, respectively. Full article

Frequency-modulated continuous-wave (FMCW) radars are widely used for range and velocity estimation. However, conventional velocity measurement techniques based on 2D-FFT processing require a large number of chirps and suffer from a maximum unambiguous velocity limitation, which restricts their applicability to high-speed targets. This study addresses these challenges by proposing a hybrid FMCW chirp waveform that employs bandwidth variation between consecutive chirps while maintaining a constant chirp duration. The proposed method enables separation of range- and Doppler-dependent frequency components using only two chirps; thus, it improves the maximum velocity constraint by keeping intermediate-frequency bandwidth and sampling requirements low. In addition, spatial angle estimation is performed using an amplitude-comparison monopulse antenna configuration, allowing single-snapshot angle measurement with low computational complexity. To enhance measurement robustness, extended and unscented Kalman filters are integrated for target tracking. Simulation results demonstrate that the proposed waveform achieves accurate velocity estimation for very high-speed targets and that the unscented Kalman filter consistently outperforms the extended Kalman filter in terms of convergence speed and robustness, particularly under poor initialization and strong nonlinearities. The results confirm that the proposed framework provides an efficient solution for tracking a single, fast-moving, isolated target in a homogeneous environment using FMCW radar systems at short and medium ranges. Full article

This paper presents a re-analysis of the track and the intensity of tropical cyclone Senyar, an unprecedented tropical cyclone that formed over the Strait of Malacca south of 5 degrees North, moving eastwards towards the South China Sea. This cyclone brought about heavy rainfall, severe flooding and landslides to southern Thailand, Malaysia and Indonesia, and this re-analysis helps document such a special and disastrous storm. Some key meteorological observations are presented to support the re-analysis, including weather radar imageries and surface weather observations. Forecasting aspects of Senyar by medium-range models and a sub-seasonal model are also presented. It turns out that both the numerical weather prediction model and the artificial intelligence model manages to resolve the warm core structure of the cyclone, but the sub-seasonal forecast fails to capture the occurrence of this very rare storm even with a forecast time of one week ahead. The formation of Senyar is found to be related to the terrain of Malay Peninsula and Sumatra, as revealed by a number of numerical simulations using a mesoscale meteorological model with different modifications of the terrain. This may be related to the lee low downstream of the terrain of Malay Peninsula under the prevailing northeasterly flow. Full article

The atmospheric reentry of hypersonic vehicles generates a plasma sheath enveloping the vehicle surface. This fluid medium moves at velocities distinct from the vehicle body, significantly altering its electromagnetic scattering properties. This paper introduces a Multi-Frame Lorentz Transformation Finite-Difference Time-Domain (FDTD) method, which incorporates a spatially varying velocity field into the computational scheme. The proposed algorithm maintains velocity synchronization in electromagnetic field updates and employs a near-to-far-field transformation for far-zone analysis. We systematically investigate the scattering characteristics of a plasma-sheath-covered hypersonic vehicle across a range of velocities and analyze the effect of velocity on the Radar Cross-Section (RCS) under different polarization conditions. Full article

Although the elevated orbit and highly squinted observation geometry bring advantages for medium-earth-orbit (MEO) synthetic aperture radar (SAR) in applications, they also complicate signal processing. The severe spatial variability of Doppler parameters and large extended range distribution of echo make it challenging for the traditional imaging algorithms to get the expected results. To quantify the variation, a spatially variable two-dimensional (SV2D) spectrum is established in this paper. The sufficient order and spatially variable terms allow it to preserve the features of targets both in the scene center and at the edge. In addition, the huge data volume and incomplete azimuth signals of edge targets, caused by the large range walk when MEO SAR operates in squinted mode, are alleviated by the variable pulse repetition interval (VPRI) technique. Based on this, a novel data-focusing method for highly squinted MEO SAR is proposed. The azimuth resampling, achieved through the non-uniform fast Fourier transform (NUFFT), eliminates the impact of most Doppler parameter space variation. Then, a novel imaging kernel is applied to accomplish target focusing. The spatially variable range cell migration (RCM) is corrected by a similar idea, with Doppler parameter equalization, and an accurate high-order phase filter derived from the SV2D spectrum guarantees that the targets located in the center range gate and the center Doppler time are well focused. For other targets, inspired by the non-linear chirp scaling algorithm (NCSA), the residual spatially variable mismatch is eliminated by a cubic phase filter during the scaling process to achieve sufficient focusing depth. The simulation results are given at the end of this paper and these validate the effectiveness of the method. Full article

All-medium metamaterial absorbers (MMAs) have attracted considerable attention for ultra-broadband electromagnetic wave (EMW) absorption. Herein, a lightweight graphene aerogel (GA) was synthesized through a low-temperature, atmospheric-pressure reduction route. Benefiting from its 3D porous network, enriched oxygen-containing functional groups, and improved graphitization, the GA offers diverse intrinsic attenuation pathways and a limited effective absorption bandwidth (EAB) of only 6.46 GHz (11.54–18.00 GHz at 1.95 mm). To clarify its attenuation mechanism, nonlinear least-squares fitting was used to quantitatively separate electrical loss contributions. Compared with graphene, the GA shows markedly superior attenuation capability, making it a more suitable medium for MMA design. Guided by equivalent circuit modeling, a stacked frustum-configured GA-based MMA (GA-MMA) was developed, where structure-induced resonances compensate for the intrinsic absence of magnetic components in the GA, thereby substantially broadening its absorption range. The GA-MMA achieves an EAB of 40.7 GHz (9.1–49.8 GHz, reflection loss < −10 dB) and maintains stable absorption under incident angles up to ± 70°. Radar cross-section simulations further indicate its potential in electromagnetic interference mitigation, human health protection, and defense information security. This work provides a feasible route for constructing ultralight and broadband MMAs by coupling electrical loss with structural effects. Full article

Small and medium Unmanned Aerial Vehicles (UAVs) are commonly equipped with diverse sensors for situational awareness, including cameras, Frequency-Modulated Continuous-Wave (FMCW) radars, Light Detection and Ranging (LiDAR) systems, and ultrasonic sensors. However, optical systems are constrained by adverse weather and darkness, while the limited detection range of compact FMCW radars-typically a few hundred meters-is often insufficient for higher-speed UAVs, particularly those operating Beyond Visual Line of Sight (BVLOS). This paper presents a Collision Avoidance System (CAS) based on a lightweight pulse radar, targeting medium UAV platforms (10–300 kg MTOM) where installing large, nose-mounted radars is impractical. The system is designed for obstacle detection at ranges of 1–3 km, directly addressing the standoff distance limitations of conventional sensors. Beyond its primary sensing function, the pulse architecture offers several operational advantages. Its lower time-averaged power also results in a reduced electromagnetic footprint, mitigating interference and supporting emission-control objectives. Furthermore, pulse radar offers greater robustness against interference in dense electromagnetic environments and lower power consumption, both of which directly enhance UAV operational endurance. Field tests demonstrated a one-to-one correspondence between visually identified objects and radar detections across 1–3 km, with ${\mathrm{P}}_{\mathrm{FA}}$ = 1.5%, confirming adequate standoff for tens of seconds of maneuvering time, with range resolution of 3.75 m and average system power below 80 W. Full article

A compact, single-layer W-band microstrip antenna for forward-looking ADAS radar in the 77–79 GHz band is presented. The 16.5 × 22 mm² PCB element integrates a linear microstrip taper, two shorting vias, and a slot-loaded cavity to stabilize input reactance and broaden the in-band match. Full-wave simulations and launcher-based measurements using WR-12 TRL de-embedding and anechoic-chamber substitution confirm S₁₁ ≤ −10 dB across 77–79 GHz. At 77/79 GHz, the antenna achieves end-fire realized gains of ≈9.9/≈11.2 dBi. The main beam is end-fire (peak near θ ≈ 90°), with −3 dB beamwidths of ≈36° in the θ-cut at φ = 0 (pointing ≈ 61°/56°) and ≈11.6° in the φ-cut at θ = 90°. First sidelobes are about −2.3/−2.5 dB (θ-cut) and −3.1/−3.4 dB (φ-cut). Cross-polarization is ≥18 dB below co-polarization, and the simulated radiation efficiency reaches ≈85% at 77 GHz and ≈80% at 79 GHz. A controlled thermal sweep (25–105 °C) yields < 100 MHz resonance drift while maintaining ≥ 10 dB return loss. Due to its planar architecture and clean feed integration, compact module packaging in short- to medium-range automotive radars. Full article

Gestures serve as an intuitive and natural medium for conveying human intent and personal identity, offering a convenient, contactless, and privacy-preserving interaction modality for human–computer interaction (HCI) systems. This paper proposes a radar-based multimodal framework for joint gesture and identity recognition, aimed at enhancing performance in radar-based gesture-identity recognition tasks. First, a robust preprocessing and multimodal feature extraction method is introduced, which integrates gesture-range-based valid frame detection with clutter suppression, enabling the extraction of multidimensional gesture features including micro-Doppler maps (MDMs), elevation–time maps (ETMs), and azimuth–time maps (ATMs). Next, a novel Joint Recognition Framework with Cross-Modal Attention Fusion (JRF-CMAF) is proposed, which incorporates Adaptive Rectification Blocks (ARBs) to dynamically leverage the complementary and correlated information across modalities. Extensive experiments were conducted on a custom radar gesture dataset collected from 7 volunteers performing 7 distinct gestures. The proposed JRF-CMAF achieves accuracies of 99.76%, 97.57%, and 96.84% in gesture recognition, identity recognition, and joint recognition tasks, respectively. Compared with conventional gesture recognition approaches and existing radar-based identity recognition methods, it attains the highest overall recognition accuracy. Full article

This paper presents the final implemented design and performance evaluation of the ground-based C-band Cheia radar system, developed to enhance Romania’s contribution to the EU Space Surveillance and Tracking (EU SST) network. All data used for performance analysis are real-time, real-life measurements of true spatial test objects orbiting Earth. The radar is based on two decommissioned 32 m satellite communication antennas already present at the Cheia Satellite Communication Center, that were retrofitted for radar operation in a quasi-monostatic architecture. A Linear Frequency Modulated Continuous Wave (LFMCW) Radar design was implemented, using low transmitted power (2.5 kW) and advanced software-defined signal processing for detection and tracking of Low Earth Orbit (LEO) targets. System validation involved dry-run acceptance tests and calibration campaigns with known reference satellites. The radar demonstrated accurate measurements of range, Doppler velocity, and angular coordinates, with the capability to detect objects with radar cross-sections as low as 0.03 m² at slant ranges up to 1200 km. Tracking of medium and large Radar Cross Section (RCS) targets remained robust under both fair and adverse weather conditions. This work highlights the feasibility of re-purposing legacy satellite infrastructure for SST applications. The Cheia radar provides a cost-effective, EUSST-compliant performance solution using primarily commercial off-the-shelf components. The system strengthens the EU SST network while demonstrating the advantages of LFMCW radar architectures in electromagnetically congested environments. Full article

Accurate wind measurements in the mesosphere and lower thermosphere (MLT) region are essential for climate modeling, satellite drag estimation, and space weather prediction. This study presents a comprehensive comparison and correlation analysis of the zonal and meridional wind observations from co-located meteor radar and medium-frequency (MF) radar systems in Kunming (102.1°E, 24.2°N), China, in the year 2022. Both zonal and meridional wind components were analyzed within the overlapping altitude range of 70–100 km. Statistical distributions of the wind speeds from both radars followed a near-Gaussian pattern concentrated within ±100 m/s, indicating good consistency. A joint dataset was constructed for the 78–100 km range, where over 2000 h of concurrent observations were available. The strongest correlation between the wind speed measurements of the two radars was ~0.6, which occurred near 82–84 km. Seasonal analysis further indicated better consistency in the winter and spring months, while the summer months exhibited reduced correlations, especially for zonal wind measurements. Systematic biases between the two instruments were also identified, with minimal intercept offsets observed from April to October. This study is valuable in the development of high-quality, long-term MLT wind field datasets for atmospheric research and numerical model validation. Full article

Ground-penetrating radar surveys on structures have a wide range of applications, and they are very useful in solving engineering problems: from detecting reinforcement, studying concrete characteristics, unfilled joints, analyzing brick elements, detecting water content in building bodies, and evaluating structural deformation. They generally pursued small investigation areas with measurements made in direct contact with target structures and for small depths. Detecting deep piles presents specific challenges, and surveys conducted from the ground level may be unsuccessful. To reach great depths, medium-low frequencies must be used, but this choice results in lower resolution. Furthermore, the pile signals may be masked when they are located beneath massive reinforced foundations, which act as an electromagnetic shield. Finally, GPR equipment looks for differences in the dielectric of the material, and the signals recorded by the GPR will be very weak when the differences in the physical properties of the investigated media are modest. From these weak signals, it is difficult to identify information on the differences in the subsurface media. In this paper, we are illustrating an exploration on plinth foundations, supported by drilled piles, submerged in soil, extensive, deep and uninformed. Pulse GPR prospecting was performed in common-offset and single-fold, bistatic configuration, exploiting the exposed faces of an excavation around the foundation. In addition, three velocity tests were conducted, including two in common mid-point and one in zero-offset transillumination, in order to explore the range of variation in relative dielectric permittivity in the investigated media. Thanks to the innovative survey on the excavation faces, it is possible to perform profiles perpendicular to the strike direction of the interface. The electromagnetic backscattering analysis approach allowed us to extract the weighted average frequency attribute section. In it, anomalies emerge in the presence of drilled piles with four piles with an estimated diameter of 80 cm. Full article

Ground-penetrating radar (GPR) is an important geophysical technique in subsurface detection. However, traditional numerical simulation methods such as finite-difference time-domain (FDTD) face challenges in accurately simulating complex heterogeneous mediums in real-world scenarios due to the difficulty of obtaining precise medium distribution information and high computational costs. Meanwhile, deep learning methods require excessive prior information, which limits their application. To address these issues, this paper proposes a novel two-step forward modeling strategy for GPR data of metal pipes. The first step employs the proposed Polarization Self-Attention Image Translation network (PSA-ITnet) for image translation, which is inspired by the process where a neural network model “understands” image content and “rewrites” it according to specified rules. It converts scene layout images (cross-sectional schematics depicting geometric details such as the size and spatial distribution of underground buried metal pipes and their surrounding medium) into simulated clutter-free GPR B-scan images. By integrating the polarized self-attention (PSA) mechanism into the Unet generator, PSA-ITnet can capture long-range dependencies, enhancing its understanding of the longitudinal time-delay property in GPR B-scan images. which is crucial for accurately generating hyperbolic signatures of metal pipes in simulated data. The second step uses the Polarization Self-Attention Style Transfer network (PSA-STnet) for style transfer, which transforms the simulated clutter-free images into data matching the distribution and characteristics of a real-world underground heterogeneous medium under unsupervised conditions while retaining target information. This step bridges the gap between ideal simulations and actual GPR data. Simulation experiments confirm that PSA-ITnet outperforms traditional methods in image translation, and PSA-STnet shows superiority in style transfer. Real-world experiments in a complex bridge support structure scenario further verify the method’s practicability and robustness. Compared to FDTD, the proposed strategy is capable of generating GPR data matching real-world subsurface heterogeneous medium distributions from scene layout models, significantly reducing time costs and providing an efficient solution for GPR data simulation and analysis. Full article

Synthetic Aperture Radar (SAR) is one of the few instruments capable of providing high-resolution global two-dimensional (2D) measurements of ocean waves. Since 2014 and then 2016, the Sentinel-1A/B satellites, whenever operating in a specific wave mode (WV), have been providing ocean swell spectrum data as Level-2 (L2) OCeaN products (OCN), derived through a quasi-linear inversion process. This WV acquires small SAR images of 20 × 20 km footprints alternating between two sub-beams, WV1 and WV2, with incidence angles of approximately 23° and 36°, respectively, to capture ocean surface dynamics. The SAR imaging process is influenced by various modulations, including hydrodynamic, tilt, and velocity bunching. While hydrodynamic and tilt modulations can be approximated as linear processes, velocity bunching introduces significant distortion due to the satellite’s relative motion with respect to the ocean surface and leads to constructive but also destructive effects on the wave imaging process. Due to the associated azimuth cut-off, the quasi-linear inversion primarily detects ocean swells with, on average, wavelengths longer than 200 m in the SAR azimuth direction, limiting the resolution of smaller-scale wave features in azimuth but reaching 10 m resolution along range. The 2D spectral partitioning technique used in the Sentinel-1 WV OCN product separates different swell systems, known as partitions, based on their frequency, directional, and spectral characteristics. The accuracy of these partitions can be affected by several factors, including non-linear effects, large-scale surface features, and the relative direction of the swell peak to the satellite’s flight path. To address these challenges, this study proposes a novel quality control framework using a machine learning (ML) approach to develop a quality flag (QF) parameter associated with each swell partition provided in the OCN products. By pairing collocated data from Sentinel-1 (S1) and WaveWatch III (WW3) partitions, the QF parameter assigns each SAR-derived swell partition one of five quality levels: “very good,” “good,” “medium,” “low,” or “poor”. This ML-based method enhances the accuracy of wave partitions, especially in cases where non-linear effects or large-scale oceanic features distort the data. The proposed algorithm provides a robust tool for filtering out problematic partitions, improving the overall quality of ocean wave measurements obtained from SAR. Moreover, the variability in the accuracy of swell partitions, depending on the swell direction relative to the satellite’s flight heading, is effectively addressed, enabling more reliable data for oceanographic studies. This work contributes to a better understanding of ocean swell dynamics derived from SAR observations and supports the numerical swell modeling community by aiding in the refinement of models and their integration into operational systems, thereby advancing both theoretical and practical aspects of ocean wave forecasting. Full article

Accurate ground deformation monitoring with interferometric synthetic aperture radar (InSAR) is often hindered by tropospheric delays caused by atmospheric pressure, temperature, and water vapor variations. While models such as ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) provide first-order corrections, they often leave residual errors dominated by small-scale turbulent effects. To address this, we present a novel variational autoencoder with clustering (VAE-clustering) approach that performs unsupervised separation of atmospheric and deformation signals, followed by noise component removal via density-based clustering. The method is integrated into the MintPy pipeline for automated velocity and displacement time-series retrieval. We evaluate our approach on Sentinel-1 interferograms from three case studies: (1) land subsidence in Mashhad, Iran (2015–2022), (2) land subsidence in Tehran, Iran (2018–2021), and (3) postseismic deformation after the 2021 Acapulco earthquake. Across all cases, the method reduced the velocity standard deviation by approximately 70% compared to the ERA5 corrections, leading to more reliable displacement estimates. These results demonstrate that VAE-clustering can effectively mitigate residual tropospheric noise, improving the accuracy of large-scale InSAR time-series analyses for geohazard monitoring and related applications. Full article