Search Results (607)

Stereo radargrammetry using Synthetic Aperture Radar (SAR) images is a powerful technique for all-weather 3D topographic measurements. However, conventional methods based on local template matching often struggle to establish accurate correspondences in mountainous or vegetated areas due to severe SAR-specific geometric modulations. In this paper, we propose a novel high-accuracy stereo radargrammetry framework by introducing RoMa, a robust Transformer-based deep learning model, for dense SAR image matching. Optical pre-trained deep learning models often suffer from a domain gap. To overcome this limitation, we develop an automated pipeline to construct a patch-based SAR image dataset using a reference Digital Surface Model (DSM) and an SAR projection model. By fine-tuning RoMa on this dataset, the model effectively adapts to the complex non-linear deformations of SAR images. Furthermore, unlike conventional methods, our approach establishes correspondences directly on the original slant-range images without requiring ground-range projection, thereby avoiding image quality degradation caused by pixel interpolation. Experimental results using airborne Pi-SAR2 images demonstrate that the fine-tuned RoMa significantly outperforms conventional methods, achieving an 82.86% matching accuracy at a 10-pixel threshold. In the 3D measurement evaluation, the proposed method achieves the lowest elevation mean error ($-1.24$ m) and the highest inlier ratio (74.1%), proving its effectiveness in generating accurate, dense, and wide-area 3D point clouds even in challenging terrains. Full article

Synthetic Aperture Radar (SAR) is one of mainstream remote sensing techniques, offering all-weather, day-and-night operational capabilities. However, throughout the processes of signal transmission, propagation, and reception, it is difficult to ensure that the amplitude and phase of the SAR signal strictly follow a linear frequency modulation (LFM) characteristic. The resulting signal distortion often leads to main lobe broadening and sidelobe elevation, degrading the focusing performance of SAR images. Traditionally, this issue has been addressed primarily through SAR system internal calibration and pre-distortion compensation, which makes it challenging to maintain the signal in an ideal state over the long term. At the same time, many simplified SAR systems also lack an internal calibration design, such as low-cost UAV-borne SAR payloads. In this paper, we propose a novel signal distortion compensation method based on SAR image data. Without relying on SAR system calibration signals, this method estimates and compensates for signal distortion directly using SAR image data, thereby improving SAR image focusing performance, achieving a resolution closer to the theoretical bandwidth and lower sidelobe. The processing and analysis of both manned and unmanned airborne SAR image data and calibration signals demonstrate that the proposed method effectively compensates for signal distortion phases, achieving performance comparable to that of real-time calibration-signal-based methods. Full article

Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. Full article

Conventional Interferometric Synthetic Aperture Radar (InSAR)-based sub-canopy topography retrieval models often suffer from insufficient characterization of scattering mechanisms, strong nonlinearity, and poor parameter convergence. To address these issues, this study proposes an improved Interferometric Water Cloud Model (IWCM) that integrates Fractional Vegetation Cover (FVC) to retrieve sub-canopy topography. The proposed method accounts for both volume and ground scattering and introduces FVC as a constraint to improve the model’s physical realism. In addition, this study utilizes InSAR observations derived from TanDEM-X dual-baseline data, which enhance the information content of the measurements by providing multiple independent interferometric observations. A two-step nonlinear least squares optimization strategy is further employed to enhance the convergence of model parameter estimation. The proposed method was validated in the forested region of Genhe City, Inner Mongolia. Airborne LiDAR-derived surface elevation data were used for assessment. The results indicate that, compared with the original InSAR-derived Digital Elevation Model (DEM), the accuracy of the retrieved sub-canopy topography improves by 39.04%. Furthermore, compared with the previously proposed Normalized Difference Vegetation Index (NDVI)-based method, under their respective optimal initial extinction coefficient conditions (${\mu }_0$), an additional accuracy improvement of 11.69% is achieved. These results demonstrate that the proposed method effectively reduces the influence of the forest canopy on interferometric phase observations and improves the capability of sub-canopy topography reconstruction in complex forest environments. The method also provides a new approach for dual-baseline and multi-baseline InSAR-based sub-canopy topography retrieval. Full article

The nonuniform raw data due to the varying pulse repetition interval (PRI) and the loss of echo pulses inevitably introduce azimuth grating lobes in the low-oversampled staggered synthetic aperture radar (LS-SAR) images, which result in ghost artifacts. In this paper, a deconvolution-based grating lobes reduction method for LS-SAR images is proposed to improve image quality. Firstly, the position-invariant property of azimuth grating lobes is theoretically analyzed and verified, and the LS-SAR image on the same range cell is mathematically modeled as the convolution between the scattering scene and the point spread function (PSF) of the LS-SAR imaging system, accompanied by the additive noise. Then, the PSF is numerically calculated according to the LS-SAR sampling strategy, the measured azimuthal antenna pattern, and the BP (Back Projection) imaging method. Finally, based on the Lucy–Richardson (LR) iterative deconvolution principle, the recovery of observed scenes and grating lobes reduction can be simultaneously achieved by deconvoluting the LS-SAR image with the acquired PSF. Both simulated experiments with point-array targets and real SAR images, as well as validation experiments with airborne measured LS-SAR data, demonstrated the effectiveness of the proposed method. Full article

Traditional angle estimation for sum-and-difference monopulse radar systems is predominantly designed for non-coherent sources or relies on fixed closed-form solutions. However, in the presence of coherent sources, these methods often suffer from performance degradation due to data rank deficiency or unavoidable suppression of target power. To address these limitations, this paper presents a single-snapshot angle estimation method for coherent sources by leveraging the angular super-resolution and ranging capabilities of monopulse radar to achieve 3D imaging in the range-angle domain. The approach utilizes the monopulse ratio spatial steering vector as a search vector and projects the received data onto its orthogonal subspace. By exploiting the coupling characteristics between signal polarization and angle, a cost function is constructed to validate the feedback of the search vector. Theoretical analysis demonstrates that for dual-target scenarios, the cost function reaches its minimum precisely when the search vector aligns with a target’s steering vector, enabling the accurate estimation of both targets’ angles. Furthermore, the polarization-angle coupling constraint reduces the 2D angular search space to a 1D line, significantly lowering computational complexity. Simulation results indicate that the method effectively resolves dual targets under single-snapshot conditions and maintains robust performance even with significant energy disparities. Finally, 3D localization of multiple airborne point targets is achieved by integrating 2D angular information with range data, validating the potential of the method for advanced radar imaging and positioning. Full article

In the context of an increasingly chaotic electromagnetic environment, the problem of multisensor data fusion for tracking airborne maneuvering targets has garnered significant attention and applications. In low-to-no power scenarios, certain sensors exhibit measurement inaccuracies, and the disparity in measurement precision among networked sensors leads to data inequality. This results in poor fusion accuracy in the multisensor fusion process, particularly when prior weights are unknown. To address the aforementioned problems, this study first redefines the motion model of airborne maneuvering targets by capturing the complexity of the trajectory of the target. Subsequently, a modeling framework for low-to-no power scenarios is established using a one-transmitter three-receiver radar system. In this model, the Signal-to-Noise Ratio (SNR) of the two sensors was intentionally reduced to simulate data inequality. Finally, a distance velocity (DV) fusion algorithm was designed based on the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) algorithm. Specifically, after the state extraction step of the SMC-PHD filter algorithm, the final estimated target was obtained in two steps: judgment and weighted summation. The simulation results demonstrate the effectiveness of the proposed algorithm in improving fusion accuracy and robustness in dynamic environments and under real electromagnetic interference. Full article

In airborne synthetic aperture radar (SAR) imaging, slant-range errors vary across the swath, making phase errors range-dependent. However, the conventional phase gradient autofocus (PGA) method assumes a range-invariant phase model and becomes unreliable when range-dependent phase errors are pronounced. Although range-partitioned PGA can substantially improve focusing performance, it may still introduce block-dependent azimuth shifts after compensation, causing geometric distortion in the focused image. To address this problem, this paper proposes a lightweight post-autofocus distortion-correction method for SAR images processed by range-partitioned PGA. Instead of re-estimating the full residual phase, the method operates on the block-wise phase-error estimates after global linear-phase removal, extracts the distortion-related linear trend using a sliding-window fitting strategy, converts it into azimuth-shift profiles, and performs sinc-based realignment. The proposed method is validated using both simulation and real unmanned aerial vehicle (UAV) SAR data. Experimental results demonstrate that the method effectively corrects geometric distortion while preserving the focusing gain achieved by range-partitioned PGA. In two representative real-data regions, the azimuth misalignment is reduced from 20 pixels to 3 pixels and from 34 pixels to 2 pixels, respectively. Full article

The resolution and accuracy of airborne/spaceborne SAR are continuously improving, making it an effective means for observing ocean dynamic processes and detecting marine targets. In contrast, utilizing its unique orbital angular momentum (OAM) mode, vortex radar does not require temporal accumulation to achieve azimuthal resolution, making it particularly suitable for observing moving sea surfaces. This capability enables stable and continuous monitoring of dynamic ocean scenes. This paper proposes a vortex radar imaging method based on three-dimensional sea surface scattering characteristics: first, a three-dimensional wind-driven sea surface geometric model is established based on the Elfouhaily sea spectrum, and its scattering characteristics under different incident angles, wind speeds, and wind directions are analyzed using the semi-deterministic facet-based two-scale method; then, two-dimensional range-azimuth imaging is achieved through coordinate transformation, echo modeling, pulse compression, and fast Fourier transform (FFT) in OAM mode domain, with the correctness of the imaging algorithm verified through multiple point target imaging results. Finally, simulation results of two-dimensional sea surface vortex imaging under different incident angles are presented, and the influence of wind speed and direction on sea surface vortex imaging is analyzed. The study shows that the vortex imaging system can effectively reflect wave fluctuations and wind direction characteristics, demonstrating the feasibility and potential of vortex radar imaging in oceanographic applications. Full article

Tropospheric delays can be the leading source of error in spaceborne interferometric synthetic aperture radar (InSAR) measurements. Here, we find that the non-uniform troposphere delay features are dependent on the squint angles used for repeat-pass InSAR data acquisitions. Large squint angles cause large along-track shifts in these non-uniform troposphere delay features. By processing the airborne L-band uninhabited aerial vehicle SAR (UAVSAR) data with three different squint angles, we were able to see various non-uniform delay structures of different sizes with varying delays of up to a few centimeters across the observed interferograms. We were also able to estimate the altitude of the effective troposphere delay layers. The understanding of the squint-dependent troposphere delay can help us separate the surface deformation component from the atmosphere delay component in the InSAR phase measurements. A number of methods are proposed for this separation. We used the UAVSAR data and simulated surface deformations to verify these methods. This technique can also be used for spaceborne cases. Full article

The automatic generation of semantic Level of Detail (LOD) 2 models from TomoSAR point clouds is frequently compromised by elevation side-lobes, data sparsity, and inherent geometric distortions. In particular, the energy dispersion caused by side-lobes blurs vertical structures, making the extraction of floor details and accurate floor height estimation significantly challenging. To overcome these limitations, we present a refined reconstruction framework that tightly couples tomographic imaging mechanisms with building geometric priors. For fine-grained vertical reconstruction, we employ a geometry-constrained inverse projection strategy that concentrates scattered energy back onto the building façade to mitigate side-lobe interference. This is complemented by a Global Coherent Integration method, utilizing spectral analysis to robustly recover periodic floor patterns and estimate average floor heights. In the horizontal domain, we address the conflict between noise suppression and feature preservation through a separation-of-axes morphological strategy. Unlike traditional isotropic filtering, this approach processes orthogonal directions independently to bridge data gaps while strictly maintaining sharp building corners and recovering fine substructures. Validated on airborne Ku-band datasets, the proposed method demonstrates the capability to produce topologically complete and semantically rich urban models from sparse radar observations. Full article

Synthetic aperture radar altimeter (SARAL) directs the radar beam toward the nadir point of the flight trajectory. It is capable of capturing elevation variations in the terrain of interest. To ensure that the nadir point remains within the beam coverage under complicated flight attitudes, a wide beamwidth is necessary. However, the wide beamwidth introduces a spatial-variant delay problem with respect to different scatters in the along-track direction, which degrades the accuracy in obtaining the terrain elevation contour. To this end, a spatial-variant Delay-Doppler (SVDD) algorithm is proposed in this paper. The core advantage of the proposed algorithm is that an analytical spectrum is obtained through rigorous mathematical derivation for the wide-beam SARAL geometry. Accordingly, all correction functions are implemented via complicated multiplications without interpolation operations. High computational efficiency is therefore ensured. To address the spatial-variant delay problem, a direct geometric relationship is first established between the Doppler frequency and the azimuthal position. Based on this relationship, the spatial-variant characteristic is mapped from the spatial domain to the Doppler domain. This mapping is then directly employed to construct the spatial-variant delay correction function. At the same time, range walk correction and range curve correction are carried out. In such cases, the variation of the undulating terrain can be recovered from the Delay-Doppler Map (DDM). Both simulated and raw data of the radar altimeter are applied to verify the effectiveness of the proposed SVDD algorithm. Comparisons with the conventional algorithm are also performed to demonstrate the superiority of the SVDD algorithm. Full article

Based on monolithic microwave integrated circuit (MMIC) technology, this paper presents the design and implementation of a low-cost, highly integrated Ka-band sixteen-channel transmit/receive (T/R) module, specifically tailored to meet the application requirements of phased array antennas in airborne and spaceborne radar systems, satellite communications, and 5G/6G millimeter-wave networks. The proposed module employs an MMIC-based single-channel dual-chip discrete architecture, optimally integrating amplitude-phase multifunction chips and transmit-receive multifunction chips in terms of both fabrication process and performance characteristics, achieving a favorable balance between high performance and high-integration density. Using low-cost, low-temperature co-fired ceramic (LTCC) substrates, full-silver conductive paste, and a nickel–palladium–gold plating process, a novel “back-to-back” thin-slice packaging technique is presented to improve integration, lower manufacturing costs, and boost long-term reliability. Furthermore, the design incorporates glass insulators and a direct array interconnection scheme, which significantly minimizes transmission losses and reduces interface dimensions. The final module measures 70.3 mm × 26.2 mm × 10.9 mm and weighs only 34 g. Experimental results demonstrate a transmit output power of at least 23 dBm, a receive gain exceeding 26 dB, and a noise figure below 3.5 dB, achieving a 22.5–58% reduction in volume per channel while maintaining competitive RF performance. To improve testing effectiveness and guarantee data consistency, an automated radio frequency (RF) test system based on Python 3.11.5 was also developed. This work provides a practical technical approach for the engineering realization of Ka-band phased array systems. Full article

Unmanned aerial vehicle Synthetic Aperture Radar (UAV SAR) is inevitably affected by hardware performance and complex electromagnetic environments, resulting in noise in the radar echo signal. This causes image blurring and loss of detail, severely limiting the detection performance and imaging quality of UAV SAR. High-repetition-rate UAV SAR can achieve high signal-to-noise ratio (SNR), but the SAR data volume grows exponentially, posing a challenge for large-scale data processing. Furthermore, in the case of high repetition rate, downsampling methods are needed to reduce the amount of raw data, which leads to a decrease in the echo SNR, thus significantly affecting SAR image details. Existing SAR signal processing methods typically involve a series of processing steps on the raw echo data, such as azimuth and range direction processing. However, these traditional methods still have limitations in improving the SNR, especially in complex environments or when the target signal is weak, where their effectiveness is often unsatisfactory. To address these issues, this paper first analyzes the SNR gain in SAR echo data processing and proposes a phase-compensated parameter-adjusted Chebyshev filtering algorithm to improve the SNR of SAR echoes. The algorithm first utilizes azimuth Chebyshev filtering to avoid spectral aliasing during downsampling and fully leverages navigation information provided by the airborne platform to accurately compensate for phase changes between pulses. Then, it employs parameter-adjusted Chebyshev filtering and coherent superposition techniques to combine multiple adjacent pulses into a single pulse with a higher SNR. Finally, the enhanced pulses are combined into a new two-dimensional matrix for subsequent pulse compression and imaging processing. This method can improve the echo SNR while reducing the amount of echo data, minimizing the loss of the original echo SNR and reducing the memory footprint of subsequent imaging processing, thus effectively improving data processing efficiency. The effectiveness of the algorithm is verified through simulation and actual measurement data. Full article

With the increasing demand for three-dimensional positioning in Synthetic Aperture Radar (SAR) systems, multi-view SAR technology is rapidly evolving. Airborne-distributed SAR systems, benefiting from multi-platform collaborative observation, flexible baseline configuration, and synchronous imaging, have become an ideal solution for realizing this technology. However, the flight paths of these platforms are not optimal, and the airborne navigation equipment also suffers from measurement errors, which severely deteriorates the multi-view SAR target positioning accuracy of the airborne-distributed platforms. Currently, research on this issue remains scarce. This paper is based on the multi-view normalized Range Doppler positioning model, introducing platform position errors to derive the Cramér-Rao Lower Bound (CRLB). A detailed positioning accuracy analysis is conducted for different flight paths and various sources of errors, demonstrating that platform position errors are a primary factor affecting target positioning accuracy. To address this, a target positioning method based on inter-platform ranging information is proposed, which imposes constraints on the position of the airborne-distributed platform using inter-platform ranging data, thereby reducing the dependence of target positioning accuracy on platform position errors and enhancing the robustness of three-dimensional positioning for multi-view SAR targets. The effectiveness of the proposed method is verified using measured data, which reduces the 3D positioning error of the target by nearly 60%. Full article