Search Results (76)

Time-modulated metasurfaces offer a novel technical approach for actively modulating and reconstructing radar target characteristics through their dynamic control of electromagnetic waves. However, existing SAR feature reconstruction methods based on metasurfaces are typically constrained by a one-to-one mapping mechanism where “a single metasurface unit corresponds to a single scattering center”. This results in low reconstruction efficiency and limited flexibility, hindering high-fidelity simulation of complex multi-scatterer targets. Therefore, this paper proposes a variable frequency-phase modulation method on time-modulated metasurfaces for SAR feature reconstruction. The core concept of this method involves decomposing complex targets into discrete scattering centers. By employing a “frequency-modulated continuous-phase modulation” strategy, a tailored modulation scheme is designed for each time-modulated metasurface, generating multiple adjustable false scattering center arrays in both the range and elevation dimensions of SAR imagery. Experimental results demonstrate that this method can effectively reconstruct SAR signatures highly similar to the original target, with similarity metrics exceeding 0.9. This study marks the first systematic application of frequency-modulation techniques to SAR signature reconstruction, breaking through the inherent limitations of traditional one-to-one mapping. It provides a novel theoretical framework and technical solution for achieving efficient, flexible, and high-fidelity simulation of complex target electromagnetic signatures, holding significant application value in fields such as radar countermeasures and signature camouflage. Full article

In recent decades, the requirement for materials with excellent electromagnetic wave (EMW) absorption properties has been steadily expanding. Developing and designing multifunctional hybrid absorbers featuring diverse components and synergistic loss mechanisms have become a significant research field. MOF materials feature abundant heterogeneous interfaces and high porosity, and their derivatives exhibit superior magnetic effects. They can enhance EMW absorption through multiple scattering and reflection. These merits enable them to satisfy the demands of diverse EMW absorption applications. Therefore, this work summarizes the investigations and applications of MOF derivatives in EMW absorption. The EMW absorption mechanisms of MOF derivatives are thoroughly investigated from the aspects of precursor design, framework construction, and compounding with reinforcing phases. Meanwhile, the research progress of related materials is summarized, including multi-component MOF-derived EMW absorbers, MOF-derived biomass composite absorbing materials, and MOF-derived conductive polymer composite absorbers. In addition, the subsequent progress of EMW absorbers shows promising prospects. The various deficiencies of MOF-derived absorbers in current research are also analyzed. It is expected to provide more systematic and thorough guidance for the future investigations in related fields. Full article

High-Resolution Range Profile (HRRP)-based space object classification is severely limited by aspect sensitivity. Inspired by the intrinsic complementarity between HRRP and LiDAR point clouds, this work investigates the feasibility and effectiveness of fusing these two modalities to address this limitation. We propose the Point-HRRP-Net framework. This framework employs dual-stream extractors to independently encode HRRP electromagnetic signatures and 3D point cloud geometric topologies. Subsequently, a Bi-Directional Cross-Attention (Bi-CA) mechanism is designed to fuse the two modalities. To enable information interaction, this mechanism utilizes point-to-point attention to correlate radar scattering features with 3D geometric points, thereby constructing a comprehensive target representation. Due to data scarcity, we constructed a paired simulation dataset for evaluation. Experimental results demonstrate that the proposed framework consistently outperforms its constituent single-modality baselines. The model achieves 57.67% accuracy on the 180° split and demonstrates generalization capability to unseen viewpoints. Ablation studies further validate the efficacy of the Bi-CA mechanism and the selected feature extractors. Finally, we assess the potential sim-to-real discrepancies and evaluate deployment feasibility across various hardware platforms. Full article

Certain types of unmanned aerial vehicles (UAVs) represent convenient platforms for remote sensing observation as well as low-altitude targets that are themselves monitored by other devices. In order to study remote sensing grayscale and radar cross-section (RCS) in an example drone, we present a fusion framework based on remote sensing imaging and electromagnetic scattering calculations. The results indicate that the quadcopter drone shows weak visual effects in remote sensing grayscale images while exhibiting strong dynamic electromagnetic scattering features that can exceed 29.6815 dBm² fluctuations. The average and peak RCS of the example UAV are higher than those of the quadcopter in the given cases. The example freighter exhibits the most intuitive grayscale features and the largest RCS mean under the given observation conditions, with a peak of 51.6186 dBm². Compared to the UAV, the small boat with a sharp bow design has similar dimensions while exhibiting lower RCS features and intuitive remote sensing grayscale. Under cross-scale conditions, grayscale imaging is beneficial for monitoring UAVs, freighters, and other nearby boats. Dynamic RCS features and grayscale local magnification are suitable for locating and recognizing drones. The established approach is effective in learning remote sensing grayscale and electromagnetic scattering features of drones used for observing freighters. Full article

Synthetic aperture radar (SAR), with its unique imaging principle and technical characteristics, has significant advantages in surface observation and thus has been widely applied in tasks such as object detection and target classification. However, limited by the lack of labeled SAR image datasets, the accuracy and generalization ability of the existing models in practical applications still need to be improved. In order to solve this problem, this paper proposes a spaceborne SAR image simulation technology and innovatively introduces the concept of bounce number map (BNM), establishing a high-resolution, parameterized simulated data support system for target recognition and classification tasks. In addition, an electromagnetic scattering characteristic-enhanced dual-branch network with simulated image guidance for SAR ship classification (SeDSG) was designed in this paper. It adopts a multi-source data utilization strategy, taking SAR images as the main branch input to capture the global features of real scenes, and using simulated data as the auxiliary branch input to excavate the electromagnetic scattering characteristics and detailed structural features. Through feature fusion, the advantages of the two branches are integrated to improve the adaptability and stability of the model to complex scenes. Experimental results show that the classification accuracy of the proposed network is improved on the OpenSARShip and FUSAR-Ship datasets. Meanwhile, the transfer learning classification results based on the SRSDD dataset verify the enhanced generalization and adaptive capabilities of the network, providing a new approach for data classification tasks with an insufficient number of samples. Full article

Deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) methods exhibit a tendency to overfit specific operating conditions—such as radar parameters and background clutter—which frequently leads to high sensitivity against variations in these conditions. A novel electromagnetic reconstruction feature alignment (ERFA) method is proposed in this paper, which integrates electromagnetic reconstruction with feature alignment into a fully convolutional network, forming the ERFA-FVGGNet. The ERFA-FVGGNet comprises three modules: electromagnetic reconstruction using our proposed orthogonal matching pursuit with image-domain cropping-optimization (OMP-IC) algorithm for efficient, high-precision attributed scattering center (ASC) reconstruction and extraction; the designed FVGGNet combining transfer learning with a lightweight fully convolutional network to enhance feature extraction and generalization; and feature alignment employing a dual-loss to suppress background clutter while improving robustness and interpretability. Experimental results demonstrate that ERFA-FVGGNet boosts trustworthiness by enhancing robustness, generalization and interpretability. Full article

Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability essential for conventional recognition algorithms. To address this problem, we propose a versatile front-end Multi-Feature Extraction and Spatial Transformation Network (MFE-STN), specifically designed for the task of discriminating between true targets and deceptive false targets created by SAR jamming, which can be seamlessly integrated with existing CNN backbones without architecture modification. MFE-STN integrates three complementary operations: (i) wavelet decomposition to extract the overall geometric features and scattering distribution of the target, (ii) a manifold transformation module for non-linear alignment of heterogeneous feature spaces, and (iii) a lightweight deformable spatial transformer that compensates for local geometric distortions introduced by deceptive jamming. By analyzing seven typical parameter-mismatch effects, we construct a simulated dataset containing six representative classes—four known classes and two unseen classes. Experimental results demonstrate that inserting MFE-STN boosts the average F1-score of known targets by 12.19% and significantly improves identification accuracy for unseen targets. This confirms the module’s capability to capture discriminative signatures to distinguish genuine targets from deceptive ones while exhibiting strong cross-domain generalization capabilities. Full article

Radar space target recognition is faced with inherent challenges due to complex electromagnetic scattering properties and limited training samples. Conventional single-modality approaches cannot fully characterize targets due to information incompleteness, and existing multi-modal fusion methods often neglect deep exploration of cross-modal feature correlations. To address this issue, this paper presents a novel multi-modal feature fusion network with fully connected self-attention (MFFN-FCSA) for robust radar space target recognition. The proposed framework innovatively integrates multi-modal radar data, including high-resolution range profiles (HRRPs) and inverse synthetic aperture radar (ISAR) images, to exploit the complementary characteristics comprehensively. Our MFFN-FCSA consists of three modules: the parallel convolutional branches for modality-specific feature extraction of HRRPs and ISAR images, an FCSA-based fusion module for cross-modal feature fusion, and a classification head. Specially, the designed FCSA fusion module simultaneously learns spatial and channel-wise dependencies via a fully connected self-attention mechanism, which enables learning dynamic weights of discriminative features across modalities. Furthermore, our end-to-end MFFN-FCSA model incorporates a composite loss function that combines a focal cross-entropy loss to address class imbalance and a triplet margin loss for enhanced metric learning. Experimental results based on a space target dataset with 10 categories show the high recognition accuracy of our model compared to related single-modality and existing fusion approaches, particularly showing promising generalization capabilities on few-shot and polarization variation scenarios. Full article

Synthetic aperture radar (SAR) target recognition tasks face the dilemma of limited training samples. The fusion of target scattering features improves the ability of the network to perceive discriminative information and reduces the dependence on training samples. However, existing methods are inadequate in utilizing and fusing target scattering information, which limits the development of target recognition. To address the above issues, the multi-level structured scattering feature fusion network is proposed. Firstly, relying on the visual geometric structure of the target, the correlation between local scattering points is established to construct a more realistic target scattering structure. On this basis, the scattering association pyramid network is proposed to mine the multi-level structured scattering information of the target to achieve the full representation of the target scattering information. Subsequently, the discriminative information in the features is measured by the information entropy theory, and the results of the measurements are employed as weighting factors to achieve feature fusion. Additionally, the cosine space classifier is proposed to enhance the discriminative capability of features and the correlation with azimuth information. The effectiveness and superiority of the proposed method are verified on two publicly available SAR image target recognition datasets. Full article

Inverse Synthetic Aperture Radar (ISAR) imaging plays a crucial role in reconnaissance and target monitoring. However, the presence of uncertain factors often leads to indistinct component visualization and significant noise contamination in imaging results, where weak scattering components are frequently submerged by noise. To address these challenges, this paper proposes a Phase-Aware Multi-Scale Transformer network (PA-MSFormer) that simultaneously enhances weak component regions and suppresses noise. Unlike existing methods that struggled with this fundamental trade-off, our approach achieved 70.93 dB PSNR on electromagnetic simulation data, surpassing the previous best method by 0.6 dB, while maintaining only 1.59 million parameters. Specifically, we introduce a phase-aware attention mechanism that separates noise from weak scattering features through complex-domain modulation, a dual-branch fusion network that establishes frequency-domain separability criteria, and a progressive gate fuser that achieves pixel-level alignment between high- and low-frequency features. Extensive experiments on electromagnetic simulation and real-measured datasets demonstrate that PA-MSFormer effectively suppresses noise while significantly enhancing target visualization, establishing a solid foundation for subsequent interpretation tasks. Full article

This paper proposes a novel Global Navigation Satellite System (GNSS)-based remote sensing method for simulating Radar Doppler Map (RDM) features through joint electromagnetic scattering modeling and signal processing, enabling characteristic parameter extraction for both point and ship targets in multi-satellite scenarios. Simulations demonstrate that the B3I signal achieves a significantly enhanced range resolution (tens of meters) compared to the B1I signal (hundreds of meters), attributable to its wider bandwidth. Furthermore, we introduce an Unscented Particle Filter (UPF) algorithm for dynamic target tracking and state estimation. Experimental results show that four-satellite configurations outperform three-satellite setups, achieving <10 m position error for uniform motion and <18 m for maneuvering targets, with velocity errors within ±2 m/s using four satellites. The joint detection framework for multi-satellite, multi-target scenarios demonstrates an improved detection accuracy and robust localization performance. Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

As a novel system, multi-dimensional space joint-observation SAR (MSJosSAR) can simultaneously acquire target information across multiple dimensions such as frequency, angle, and polarization. This capability facilitates a more comprehensive understanding of the target and enhances subsequent recognition applications. However, current research on the configuration optimization of multi-dimensional SAR systems is limited, particularly in balancing recognition requirements with observation costs. This limitation has become a major bottleneck restricting the development of MSJosSAR. Moreover, studies on the joint utilization of multi-dimensional information at the scattering center level remain insufficient, which constrains the effectiveness of target component recognition. To address these challenges, this paper proposes a configuration optimization method for MSJosSAR based on the separability of scattering mechanisms. The approach transforms the configuration optimization problem into a vector separability problem commonly addressed in machine learning. Experimental results demonstrate that the multi-dimensional configuration obtained by this method significantly improves the classification accuracy of scattering mechanisms. Additionally, we propose a feature extraction and classification method for scattering centers across frequency and angle-polarization dimensions, and validate its effectiveness through electromagnetic simulation experiments. This study offers valuable insights and references for MSJosSAR configuration optimization and joint feature information processing. Full article

Statistical optimization methods serve as fundamental tools for studying sea-ice-related phenomena in the polar regions. To comprehensively analyze the spatial distributions of ice ridge keels, including the draft and spacing distributions, a statistical optimization model was developed with the aim of determining the optimal keel cutoff draft, which differentiates ice ridge keels from sea ice bottom roughness. By treating the keel cutoff draft as the identified variable and minimizing the relative errors between the theoretical and measured keel spatial distributions, the developed model aimed to seek the optimal keel cutoff draft and provide a precise method for this differentiation and to explore the impact of the ridging intensity, defined as the ratio of the mean ridge sail height to spacing, on the spatial distributions of the ice ridge keels. The utilized data were obtained from observations of sea ice bottom undulations in the Northwestern Weddell Sea during the winter of 2006; these observations were conducted using helicopter-borne electromagnetic induction (EM-bird). Through rigorous analysis, the optimal keel cutoff draft was determined to be 3.8 m, and this value was subsequently employed to effectively differentiate ridge keels from other roughness features on the sea ice bottom. Then, building upon our previous research that clustered measured profiles into three distinct regimes (Region 1, Region 2, and Region 3, respectively), a detailed statistical analysis was carried out to evaluate the influence of the ridging intensity on the spatial distributions of the ice ridge keels for all three regimes. Notably, the results closely matched the predictions of the statistical optimization model: Wadhams’80 function (a negative exponential function) exhibited an excellent fit with the measured distributions of the keel draft, and a lognormal function proved to effectively describe the keel spacing distributions in all three regimes. Furthermore, it was discovered that the relationship between the mean ridge keel draft and frequency (number of keels per kilometer) could be accurately modeled by a logarithmic function with a correlation coefficient of 0.698, despite considerable data scatter. This study yields several significant results with far-reaching implications. The determination of the optimal keel cutoff draft and the successful modeling of the relationship between the keel draft and frequency represent key achievements. These findings provide a solid theoretical foundation for analyzing the correlations between the morphologies of the sea ice surface and bottom. Such theoretical insights are crucial for improving remote sensing algorithms for ice thickness inversion from satellite elevation data, enhancing the accuracy of sea ice thickness estimations. Full article

To study the influence of speed brake deflection on remote sensing grayscale images and the radar cross section (RCS) of aircraft, we present a comprehensive method based on remote sensing modeling and dynamic electromagnetic scattering. The results indicate that grayscale images from ground remote sensing can capture the hierarchical information of various reference objects and water bodies. When the target aircraft enters the observation area, complex ground reference objects may blur the grayscale features of the speed brake. The RCS of the speed brake shows strong dynamic characteristics under the example of the forward azimuth, where the maximum variation can reach 43.433 dBm². When the speed brakes on both sides dynamically deflect, the aircraft’s RCS in the lateral azimuth will fluctuate significantly in the first half of the observation time, and those in the forward and backward azimuths will show clear dynamic characteristics in the second half of the observation time. Low grayscale ground reference and water body boundaries/areas are beneficial for distinguishing the deflection of the deceleration plate. The comprehensive method proposed here is effective for studying remote sensing grayscale images and the dynamic RCS of aircraft under speed brake deflection. Full article