Lunar lobate scarps are small-scale linear structures formed by shallow thrust fault activity on the lunar surface, and their spatial distribution and stress characteristics provide essential clues for understanding the late-stage tectonic evolution of the Moon. At present, the correlation mechanism between the stress characteristics of lobate scarps and their radar scattering response remains unclear. In this study, lobate scarps in the Apollo Basin are considered as the research focus, and scattering characteristic parameters are extracted from Mini-RF images. Subsequently, the Coulomb software is used to invert the distribution characteristics of the regional stress field based on high-resolution digital elevation models. On this basis, the correspondence between radar scattering characteristics and stress is analyzed using statistical correlation methods, revealing the underlying relationship between tectonic activity and surface material response. The main conclusions are as follows. (1) The strain near the fault is markedly higher than that in the surrounding areas, with the maximum shear strain concentrated in the fault dip direction. Volumetric strain indicates volume expansion within the fault hanging wall, whereas volume contraction is observed at both ends and at the center of the sliding surface. (2) The scarp face and hanging wall regions exhibit stronger scattering responses in terms of the circular polarization ratio and partial polarization decomposition parameters, suggesting higher degrees of fragmentation, boulder exposure, or structural complexity in these areas. However, the scattering differences are also influenced by the combined effects of surface roughness, incidence geometry, and subsequent modification processes. (3) The regression analysis of scattering parameters and stress fields, based on multiple linear regression and random forest algorithms, indicates that the correlation between scattering characteristics and stress is likely nonlinear, governed by the combined effects of topographic conditions, surface roughness, and local structures. Overall, this study develops an exploratory radar-topography joint analysis framework for lobate scarps in the Apollo Basin to evaluate whether a quantifiable statistical correspondence exists between scattering features and stress indicators, as well as to provide supplementary evidence for studies of shallow tectonic activity under similar geological settings.
Small Unmanned Aerial Vehicle (UAV)-borne distributed Tomographic Synthetic Aperture Radar (TomoSAR) systems exhibit remarkable residual time-varying baseline errors due to the limited precision of the position and orientation system on small UAV platforms. These errors critically degrade the performance of three-Dimensional (3D) target reconstruction. Compared with airborne repeat-pass 3D Synthetic Aperture Radar (SAR), distributed TomoSAR mounted on small UAVs imposes stricter compensation accuracy requirements for time-varying baseline errors because of the altitude constraints of the carrying platform. Under the conditions of low signal-to-noise ratio and substantial time-varying baseline errors, existing estimation methods often fail to provide stable and reliable results. In this paper, a two-step time-varying baseline error estimation method based on image azimuth displacement is proposed. The method sequentially estimates the low-frequency component through the co-registration of the master and slave images and the high-frequency component using a multisquint algorithm. Iterative refinement is applied to enhance estimation accuracy. The experimental results obtained from real C-band small UAV-borne distributed TomoSAR data demonstrate that, compared with the enhanced multisquint processing method, the proposed method considerably reduces the root mean square of differential interferometric phases across most channels, thereby effectively improving interchannel coherence. In addition, the elevation-direction standard deviation of the reconstructed point cloud is reduced from 5.16 to 1.33 m, and the height reconstruction error of building targets is less than 0.5 m, validating the effectiveness and superiority of the proposed method.
A large-scale Vision-Language Model (VLM) pre-trained on massive image-text datasets performs well when processing natural images. However, there are two major challenges in applying it to Synthetic Aperture Radar (SAR) images: (1) The high cost of high-quality text annotation limits the construction of SAR image-text paired datasets, and (2) The considerable differences in image features between SAR images and optical natural images increase the difficulty of cross-domain knowledge transfer. To address these problems, this study developed a knowledge transfer method for VLM tailored to SAR images. First, this study leveraged paired SAR and optical remote sensing images and employed a generative VLM to automatically produce textual descriptions of the optical images, thereby indirectly constructing a low-cost SAR-text paired dataset. Second, a two-stage transfer strategy was designed to address the large domain discrepancy between natural and SAR images, reducing the difficulty of each transfer stage. Finally, experimental validation was conducted through the zero-shot scene classification, image retrieval, and object recognition of SAR images. The results demonstrated that the proposed method enables effective knowledge transfer from a large-scale VLM to the SAR image domain.
The Ring-Moat Dome Structure(RMDS)is a small-scale domical landform that develops on the lunar mare basalt surface.With an average height of only 3-4 m and a mean diameter of approximately 200 m,these features display a low-relief morphology.Owing to their abundance,RMDSs have become an important subject for understanding lunar volcanic activity and magmatic thermodynamic processes.Although optical remote sensing observations support a volcanic origin for RMDSs,alternative hypotheses have been proposed.Therefore,confirming their genesis requires further geological evidence.Microwave radar provides a distinct advantage in probing the subsurface structure and physical properties of the Moon owing to its penetrative capability.However,to date,no studies have examined the microwave radar scattering characteristics of RMDSs.This paper therefore analyzes the radar backscatter power and Circular Polarization Ratio(CPR)characteristics of RMDSs using S-band(12.6 cm wavelength)data acquired by the spaceborne Miniature Radio Frequency(Mini-RF)instrument and the ground-based Arecibo radar.The study area is a region densely populated with RMDSs on the mare surface within the Mare Tranquillitatis basin.The results indicate the following:(1)RMDSs exhibit low backscatter power,which is highly consistent with the weak radar scattering predicted by the foamy magma genetic model.According to this model,the primary cause of the low-intensity radar echo is the presence of submillimeter-scale fine-grained materials within the uppermost～2-3 m depth range.(2)Influenced by impact cratering,topographic variations,and mass wasting,some RMDSs display localized enhancement of backscatter to varying degrees;however,prolonged space weathering diminishes this effect.(3)The mean statistical values of the radar backscatter coefficient and the CPR for RMDSs are remarkably close to those of pyroclastic deposits,indicating that their surface physical properties are analogous and that the particle size of their surface materials should be comparable to those of pyroclastic materials,which are predominantly fine-grained particles in the micrometer-to-millimeter range.These findings further confirm that multiband,high-resolution,and multipolarization radar data can yield richer geological evidence for the detailed investigation of RMDSs.This not only deepens the understanding of RMDS formation and evolutionary mechanisms but also provides a valuable reference for future microwave remote sensing exploration of lunar volcanic landforms.
In locating ground moving radiating sources, traditional passive positioning methods, such as Direction of Arrival (DOA), often rely on long-term observation and filtering, resulting in low positioning efficiency. Existing synthetic aperture-based positioning methods are primarily designed for stationary radiating sources, making high-precision positioning of moving sources difficult. To address this limitation, this paper proposes synthetic aperture-based fast positioning and velocity estimation methods for moving radiating sources under single- and dual-station positioning systems, respectively. The proposed methods establish an instantaneous slant range model of the radiating source and derive the mapping relationship between the positioning parameters (position and velocity) and the imaging parameters. Specifically, in the single-station scenario, the traditional second-order slant range model is extended to third order, and a third-order chirp rate is introduced to supplement the degrees of freedom, thereby enabling simultaneous estimation of position and velocity. In the dual-station scenario, an additional observation station is used to introduce two new imaging parameters, thereby further improving the rapidity and accuracy of positioning. To address the multi-solution problem inherent in the positioning equations, this paper proposes true-solution determination criteria for the single- and dual-station systems and presents an initialization strategy to ensure a unique solution for dual-station positioning. Furthermore, the paper analyzes how various factors affect the positioning accuracy of single- and dual-station models, compares the performance of the proposed single- and dual-station passive positioning models, and verifies the effectiveness of the proposed algorithms through simulations.
Radar signal deinterleaving is a critical technology in electronic intelligence and electronic support measures systems. The classical histogram-based method, although valued for its simplicity, is susceptible to deceptive jamming under counter-reconnaissance parameter design. This study proposes a deinterleaving method that is resistant to such deception. The main contributions are as follows: a frame period detection mechanism compatible with pulse missing rates from 0% to 50% is established through theoretical derivation; by integrating autocorrelation and the overlap rate, accurate frame period identification is achieved, which effectively distinguishes interference disguised as fixed Pulse Repetition Intervals (PRI) and prevents interference with the deinterleaving process; moreover, a coherent discrimination mechanism is introduced to handle scenarios with similar parameters and to accommodate fixed, staggered, sliding, and wobulated PRI modulation—within a unified framework. Experimental results show that the performance of histogram-based methods degrades severely in the presence of counterreconnaissance parameters, with maximum performance dropping to 0, while the proposed method maintains a minimum performance of 96.5%. Meanwhile, the proposed method reaches a minimum performance of 95.31% in parameter-similar scenarios. The proposed method remains effective against the four modulation types, whether counterreconnaissance parameters are present or not. It demonstrates antideception capability against counterreconnaissance design, strong generalization across modulation types, and reliable performance in parameter-similar scenarios, thereby greatly improving the deinterleaving reliability in complex electromagnetic environments and offering important implications for the development of electronic warfare systems.
Pulse Doppler radar provides all-weather operational capability and enables simultaneous acquisition of target range and velocity through Range-Doppler (RD) maps. In near-vertical flight scenarios, the geometric structure of RD maps implicitly encodes key platform motion parameters, including altitude, velocity, and pitch angle. However, these parameters are strongly coupled in the RD domain, making effective decoupling difficult for traditional signal-processing-based inversion methods, particularly under complex terrain and near-vertical incidence conditions. Although recent advances in deep learning have shown strong potential for motion information sensing, multitask learning in this context still faces challenges in achieving both real-time performance and high estimation accuracy. To address these issues, this study proposes a novel network architecture, termed Range-Doppler Map Fusion Network (RDMFNet), that performs multirepresentation information fusion via shared encoders and parallel decoders, along with a two-stage progressive training strategy to enhance parameter estimation accuracy. Experimental results show that RDMFNet achieves estimation errors of 14.447 m for altitude, 4.635 m/s for velocity, and 0.755° for pitch angle, demonstrating its effectiveness for high-precision, real-time perception.
The Moon’s shallow subsurface structure provides crucial insights into its geological evolution, material composition, and space weathering processes. With the acquisition of extensive radar datasets from recent lunar exploration programs, such as the Chang’E missions, high-resolution characterization of the lunar regolith’s stratigraphic and physical properties has become a focus and challenge in lunar science. Conventional radar layer identification and tracking methods often suffer from instability in complex scattering environments, because of their sensitivity to noise and subsurface heterogeneity. To address these limitations, this study proposes an automatic layer-tracking algorithm based on a dynamic search center (DSC) approach. This algorithm employs a Gaussian-weighted prediction mechanism to balance historical trajectory trends with local signal responses and uses a multifeatured fusion decision scheme to enhance tracking robustness under noisy conditions. Numerical simulations demonstrate that, with a search radius l = 20 and a historical window n = 20, the algorithm achieves a layer identification error of less than 2% for shallow strata (<140 ns). Meanwhile, for deep layers (>170 ns), with considerable signal attenuation, incorporating an edge-direction weighting term reduces the tracking error by over 30%. When applied to lunar penetrating radar data from the Chang’E-4 mission, the proposed method successfully realizes automatic stratigraphic tracing in lunar radar profiles, producing layer boundaries that are highly consistent with previous interpretations. Simulation and in-situ results confirm that the DSC-based algorithm accurately delineates real subsurface interfaces across media and structural morphologies, effectively suppressing noise, while maintaining smooth trajectories. Overall, the proposed method achieves low manual dependence, high robustness, and high precision in automatic radar layer tracking, thereby providing a valuable reference for analyzing radar data from upcoming missions such as Chang’E-7 and Martian shallow-subsurface explorations.
Driven by complex electromagnetic environments and multi-target collaborative detection needs, enhancing the overall effectiveness of radar networks through autonomous coordination technology has become a key research area in radar collaborative surveillance. Extensive research has been conducted worldwide, yielding substantial advances in theoretical development, technical validation, and equipment application. This paper systematically discusses the foundational concepts and main features of autonomous coordination in radar networks, examining the primary technical challenges faced during implementation and performance optimization. It also reviews recent notable research findings and technological strategies, focusing on collaborative architecture design, sensing, intelligent decision-making and control, and autonomous evolution. Finally, this paper offers an outlook on future trends in the field and provides references for related theoretical research and practical applications.
Formed by the cooling and solidification of flowing lava during volcanic activity, lunar lava tubes are considered promising candidates for future lunar bases due to their stable and protective roofs. However, these tubes are typically buried hundreds of meters to kilometers beneath the surface, making direct detection extremely difficult. Current detection methods mainly rely on radar and gravity anomaly analysis. However, the resolution of orbital radar is insufficient to distinguish similar subsurface structures, whereas in situ lunar penetrating radar is limited by a small detection range and vulnerability to near-field interference. Gravity anomaly detection also performs poorly when identifying tubes oriented north-south or with roofs narrower than a kilometer. Skylights serve as critical indicators for locating subsurface tubes and can be identified through optical imagery and infrared radiation thermal anomalies. However, optical images are constrained by illumination conditions, making full three-dimensional reconstruction of skylights difficult. Infrared data are further limited by penetration depth and spatial resolution (320 m×160 m), which hinders the detection of subsurface thermal anomalies and the assessment of the thermophysical properties of materials at the pit floor. To address these challenges, this paper explores the feasibility of detecting skylight thermal anomalies using microwave radiation. Owing to its penetration capability and sensitivity to dielectric properties, this approach can probe subsurface thermal features and effectively determine the material composition of the pit floor. However, a significant scale disparity exists between the kilometer-scale resolution of current data and the relatively small size of skylights. Therefore, enhancing the detection capability of passive microwave methods for 100-m-scale skylights remains a critical issue that requires immediate attention.
This study proposes a processing framework based on Mutual Information Entropy (MIE) and an improved probability hypothesis density filter to address the key challenges—high clutter density and low detection probability—in Passive Bistatic Radar (PBR) target tracking. First, statistical differences in the correlation between target and clutter points, as well as between reference models, are quantified as mutual information entropy values, which are then used to eliminate clutter points. Second, the classical probability hypothesis density filter is improved through dynamic weight compensation, mitigating particle weight degeneration and reducing the deletion of false targets. This approach effectively resolves issues such as track fragmentation and target loss caused by discontinuous measurements with random intervals under low detection probability. The effectiveness of the proposed framework was verified through simulation experiments, and field test data demonstrated that the proposed method achieves good target-tracking performance in practical applications.
Low-altitude targets, represented by rotor unmanned aerial vehicles, can typically adopt a slow-cruise mode. As a result, their echoes fall within the Doppler Blind Zone (DBZ) and evade radar detection and tracking. The cluttered low-altitude environment adds to further complexity. To address this issue, this study proposes a method grounded in the framework of random finite set and designed for tracking slow-moving targets with a low-altitude surveillance radar. Inspired by the Bayesian occupancy filter, the proposed method initially models the radar Field of View (FoV) as a grid map. It is uniformly partitioned along the angle-range axis, ensuring that each cell captures a specific segment of the FoV. Then, adaptive filtering parameter modules are meticulously designed by leveraging the distinct dynamic characteristics of slow-moving targets and ground clutter. Subsequently, a probability hypothesis density filter is deployed to conduct unified filtering on the grid map situated within the DBZ. The final step involves the use of clustering methods to extract information about the target of interest. Simulation results validate the effectiveness, robustness, and superior performance of the proposed method across typical surveillance scenarios involving multiple slow-moving targets, noise, and clutter.
A maritime multimodal data resource system provides a foundation for multisensor collaborative detection using radar, Synthetic Aperture Radar (SAR), and electro-optical sensors, enabling fine-grained target perception. Such systems are essential for advancing the practical application of detection algorithms and improving maritime target surveillance capabilities. To this end, multi-source maritime target data are collected using SAR, radar, visible-light cameras, and infrared cameras mounted on shore-based, airborne, and other platforms. The data were labeled by performing automatic correlation registration and manual correction. According to the requirements of different tasks, multiple task-oriented multimodal associated datasets were compiled. This paper focuses on one subset of the overall resource system, namely the Dual-Modal Ship Detection (DMSD), which consists exclusively of visible-light and infrared image pairs. The dataset contains 2163 registered image pairs, with intermodal alignment achieved through an affine transformation. All images were collected in real maritime environments and cover diverse sea conditions and backgrounds, including cloud, rain, fog, and backlighting. The dataset was evaluated using representative algorithms, including YOLO and CFT. Experimental results show that the dataset achieves an mAP@50 of approximately 0.65 with YOLOv8 and 0.63 with CFT, demonstrating its effectiveness in supporting research on optimizing bimodal fusion strategies and enhancing detection robustness in complex maritime scenarios.
Ground Control Points (GCPs) are essential for improving the positioning accuracy of remote sensing imagery. Their spatial distribution and geometric quality directly affect the reliability of orthorectification. GCPs serve as a critical foundation for ensuring the accuracy of multi-source image fusion, change detection, and quantitative inversion. However, traditional corner reflector deployment presents high costs and implementation difficulties, struggling to meet global application demands. Additionally, existing heterogeneous control points (such as optical imagery and laser altimetry data) exhibit significant modal differences relative to Synthetic Aperture Radar (SAR) imagery, which affects their ability to balance accuracy and robustness. To address these challenges, this study proposes an automatic control point extraction method for high-resolution SAR imagery based on multi-source data. Furthermore, a high-precision orthorectification framework is established using control chips. The method leverages the characteristics of widely distributed pole-like artificial features in urban environments: these features exhibit a body-shadow collaborative structure in optical imagery and a cross-shaped strong scattering response in SAR imagery. First, open-source airport runway data are used to correct Google optical imagery, establishing a planar reference framework. Next, initial positioning optimization for stereo SAR images from ascending and descending orbits is achieved by jointly adjusting optical-SAR and stereo SAR image matching points. Finally, road and parking lot vector data are utilized to extract regions of interest, where strong scattering points are identified using a signal-to-clutter ratio detection algorithm. Three-dimensional spatial coordinates of control points are obtained via point target analysis and stereo positioning techniques. After correcting residual planar errors in stereo SAR images using control point coordinates, control chip data for ascending and descending orbit SAR images are generated. Validation experiments using GaoFen-3 SAR images from multiple regions show that the 3D positioning accuracy of control points extracted from spotlight mode stereo SAR imagery reaches the submeter level. Orthorectification of test images using extracted control points and control chips significantly improves positioning accuracy, as verified by corner reflectors and airborne LiDAR point cloud-based ground truth. Positioning errors are 1.78 pixels (spotlight mode), 1.09 pixels (ultrafine stripmap mode), and 0.82 pixels (fine stripmap mode), corresponding to improvements of 47.2%, 49.3%, and 37.4%, respectively, compared to traditional optical reference image matching correction methods. This study introduces crowdsourced information to assist SAR control point extraction and ascending/descending orbit SAR control chip construction, overcoming the accuracy limitations of optical reference image matching correction. The proposed method provides a scalable approach for high-precision positioning and joint processing of high-resolution SAR imagery.
Ocean currents play a critical role in global climate regulation. Synthetic aperture radar (SAR) provides high-resolution observational support for ocean current detection by measuring Doppler shifts; however, SAR Doppler shifts contain multiple contributing components. To accurately retrieve ocean currents from these data, nongeophysical contributions must be precisely corrected, and wind- and wave-induced Doppler shifts must be accurately estimated. This paper proposes a machine learning-based method for modeling such shifts and retrieving ocean currents from Sentinel-1 SAR data. First, nongeophysical contributions in the SAR Doppler shift are precisely corrected to remove the effects unrelated to ocean motion. Second, backpropagation neural network (BPNN) and eXtreme gradient boosting (XGBoost) models, optimized using the particle swarm optimization algorithm, are developed to describe the nonlinear relationship between the wind-wave Doppler shift and sea-surface wind-wave parameters derived from SAR data. Finally, the corrected Doppler shift is utilized to retrieve ocean surface current velocities. This paper comparatively evaluates the estimation accuracies of the wind-wave Doppler shifts obtained using the BPNN and XGBoost models, as well as the respective influence of each model's performance on the effectiveness of ocean current retrieval. Results indicate that the XGBoost model achieves superior estimation accuracy compared with the BPNN model. The root mean square error (RMSE) of the Doppler shift estimated by the XGBoost model is approximately 4.043 Hz, which is 2.898 Hz lower than that of the BPNN model. Compared with those of the HYCOM current data, the RMSE of the currents retrieved by the XGBoost model is about 0.202 m/s; this value is reduced by 0.122 m/s compared with that of the BPNN model. Validations against the current velocities detected by HF radar show that the RMSE of currents retrieved by the XGBoost model is 0.21 m/s, representing a 16% reduction compared with that of the BPNN model. These findings indicate that the proposed technical approach for ocean current retrieval using spaceborne SAR is highly accurate.
Moving target shadow tracking in Video Synthetic Aperture Radar (Video SAR) faces significant challenges, including strong speckle noise interference, substantial nonrigid target deformation, and a scarcity of supervised training samples. To address these issues, we propose a semi-supervised tracking method that integrates physics-aware spectral spatial attention and kinematic constraints. For the detection stage, we construct a semi-supervised feature enhancement network based on an improved UniMatchV2 architecture. Specifically, to account for the spectral spatial characteristics of SAR images, a Physics-Aware Hybrid Spectral Spatial Attention (PA-HSSA) module is designed and embedded into the high-level feature space of the decoder. This module utilizes its spectral branch to globally suppress wideband high-grequercy interference and its spatial branch to locally anchor the shadow’s geometric structure. A dynamic weight generator is introduced to adaptively fuse the dual-domain features, thereby generating high-quality prediction masks under extremely low annotation ratios. For the tracking stage, we propose a spatiotemporal association framework tailored for semi-supervised uncertainty. The framework includes a kinematic prior gate based on a Linear Gaussian State Space Model (LGSSM) to smooth and correct jittery detection edges. Subsequently, a multidimensional cost matrix integrating kinematic residuals and geometric consistency is built to resolve association ambiguities caused by target maneuverability and deformation. Experimental results on measured data from Sandia National Laboratories (SNL) demonstrate that the proposed method achieves a Multiple Object Tracking Accuracy (MOTA) of 64.19% using only 1/32 of the labeled data, outperforming baseline methods by 6.73%. This method effectively addresses the challenge of robustly tracking weak and small shadows in heavily cluttered backgrounds.
Synthetic Aperture Radar (SAR) is widely used in military and civilian applications, with intelligent target interpretation of SAR images being a crucial component of SAR applications. Vision-Language Models (VLMs) play an important role in SAR target interpretation. By incorporating natural language understanding, VLMs effectively address the challenges posed by large intraclass variability in target characteristics and the scarcity of high-quality labeled samples, thereby advancing the field from purely visual interpretation toward semantic understanding of targets. Drawing upon our team’s extensive research experience in SAR target interpretation theory, algorithms, and applications, this paper provides a comprehensive review of intelligent SAR target interpretation based on VLMs. We provide an in-depth analysis of existing challenges and tasks, summarize the current state of research, and compile available open-source datasets. Furthermore, we systematically outline the evolution, ranging from task-specific VLMs to contrastive-, conversational-, and generative-based VLMs and foundational models. Finally, we discuss the latest challenges and future outlooks in SAR target interpretation by VLMs.
In recent years, bionic super-resolution technology, inspired by biological perception mechanisms, has emerged as a substantial research direction aimed at overcoming the limitations of radar resolution. The Baseband Spectrogram Correlation and Transformation (BSCT) model, which is based on bat hearing, offers a novel approach to enhancing traditional radar resolution. However, the model exhibits inherent limitations, including insufficient multi-target adaptability and the inability to utilize polarization information. To address these problems, this paper proposes a polarization-enhanced bionic super-resolution model: Polarimetric Baseband Spectrogram Correlation and Transformation (P-BSCT) for Mechanical Rotation Polarimetric Radar (MRPR). The primary contributions of this study are as follows: first, the integration of the bat BSCT model with MRPR, thereby enabling the utilization of polarization information and the execution of polarization measurements; second, the proposal of an advanced signal processing method, which overcomes the limitations of the original BSCT in two-target and static scenes, effectively applying to multi-target and moving-target scenarios, and exhibiting no impact on the resolution effect due to signal modulation. P-BSCT has been demonstrated to enhance resolving power by approximately 15 dB under optimal conditions when compared with the original BSCT model. In scenarios involving moving targets, targets exhibiting equivalent polarization scattering properties, and nonlinear FM signals, the resolving performance of P-BSCT remains essentially unchanged, demonstrating notable robustness.
Variations in imaging geometry are the main cause of relative feature distortion in Synthetic Aperture Radar (SAR) images, greatly increasing the difficulty of image matching. Using simulated SAR images as references can remove the feature distortions caused by geometric differences. However, significant differences in scattering characteristics and noise patterns between measured and simulated images still exist. Additionally, since most existing matching algorithms mainly rely on symmetric keypoint detection and descriptor matching, the number and precision of matched points are not optimal. To solve these problems, this paper introduces an asymmetric Local Fitting Consistency (LFC) similarity metric based on the local statistical features of both measured and simulated SAR images. Using this metric, a coarse-to-fine matching framework for airborne and simulated SAR images is designed. Furthermore, terrain features are added to improve keypoint detection diversity, leading to more robust matching between airborne and simulated SAR images. Experimental results show that the proposed LFC-based matching method offers better robustness and accuracy compared to other approaches, significantly surpassing current state-of-the-art algorithms in terms of matching precision and other key metrics.
Low-sidelobe waveforms are fundamental for ensuring the basic detection performance of radars. Designing waveforms with low sidelobes in the range dimension, the velocity dimension, or both, remains a major challenge in radar research. To address the issue of sidelobe suppression in the velocity dimension for coherent pulse trains, this paper proposes a joint design method of variable-pulse-width pulse trains and receive mismatched filtering. The proposed method uses a symmetric positive window function to directly construct both the pulse width sequence and the receive weighting sequence. As a result, the characteristics of the window function’s amplitude spectrum, including low sidelobes and the broadening of the 3-dB mainlobe, are transferred into the mismatched filtering output of the coherent pulse train. Theoretical analysis shows that the proposed method incurs a smaller mismatched Signal-to-Noise Ratio (SNR) loss than when the window function is applied solely for receive mismatched filtering. The effects of window functions and the minimum pulse-width constraint on SNR loss and weak target detection performance under strong target interference are analyzed through simulations, illustrating the advantages of the proposed joint transceiver design method.
