Search Results (600)

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

Compact multi-channel airborne radar stations increasingly rely on an LED-based visible light communication (VLC) service link under radio-frequency spectrum restrictions and strict end-to-end delay constraints. Despite the directional nature of optical links, the VLC channel remains vulnerable to active optical interference and signal injection; furthermore, when an AI-enabled integrity monitor is embedded into the receiver, the AI decision layer becomes a direct target of evasion and online poisoning. This paper proposes a lightweight, interpretable AI-based attack detection architecture in which a Poisson photon-counting observation model is used to form physically consistent features over the preamble and control-sequence interval, while the final decision is produced by an AI ensemble combining a monotonic logistic detector and a one-class detector. The considered threat profile includes sustained illumination and synchronized flashes (jamming/blinding), spoofing via false preambles, replay of recorded fragments, and online data poisoning during self-calibration. The adequacy of solutions is assessed using the detection probability P_D (ensemble: P_D ≥ 0.90 for DC-jamming mean-count increment Δλ_DC ≈ 7.56, pulsed-interference mean-count increment Δλ_pulse ≈ 12.89, and spoofing signal-scaling factor α ≈ 1.02), the false-alarm probability P_FA = 0.045, and the per-packet end-to-end latency (bounded by the observation-window duration LΔT = 20 μs, where window length L = 20 and interval duration ΔT = 1 μs), which confirms real-time CPU operation without GPU acceleration. Full article

This paper presents a broadband compact omnidirectional UHF antenna–radome package for airborne applications, where reliable communication, low observability, and electromagnetic compatibility are critical. Omnidirectional radiation is essential for maintaining consistent radio communication links regardless of aircraft attitude during flight. Conventional blade antennas achieve such coverage but suffer from increased aerodynamic drag, higher radar cross-section (RCS), and limited conformal integration capability. To address these limitations, a low-profile hybrid structure combining a patch radiator with a broadband monocone is proposed. An integrated metallic ground wall reduces sensitivity to installation surroundings, suppresses structural interference, and enhances RF (Radio Frequency) compatibility. The antenna achieves an electrical size of 0.555λ × 0.555λ × 0.072λ at 338 MHz. A lightweight sandwich-structured glass fiber-reinforced plastic (GFRP) radome is fabricated to ensure mechanical robustness and environmental protection while maintaining negligible RF loss. Measurements confirm a −10 dB bandwidth of 32.7%, stable omnidirectional radiation, and gain exceeding −0.2 dBi throughout the operating band with peak realized gain of 2.6 dBi within the investigated frequency range. The radome causes negligible gain degradation, with insertion loss below 0.4 dB. The proposed package provides a practical solution for airborne platforms requiring compact, broadband, and installation-resilient antenna systems. Full article

Weather radars enable the monitoring of airborne insects over spatial scales ranging from hundreds to thousands of kilometers. Quantitative information on migration intensity and spatial distribution can be obtained from weather radar observations. However, insect biological characteristics cannot be directly retrieved from weather radar, which limits species identification. Among the parameters associated with insect species, body length describes an important aspect of individual morphological characteristics. In this study, we propose a method for estimating the mean body length of airborne insect populations based on S-band polarimetric weather radar observations. A theoretical relationship is established between mean body length, insect number density, and the specific differential phase, which is defined as the range derivative of the phase difference between horizontally and vertically polarized radar signals. Electromagnetic simulations are conducted to analyze the relationship between specific differential phase and mean body length under known number density. Furthermore, the method is validated using joint observations from weather radar and entomological radar. The experimental results show that, for insects with body lengths ranging from 5 to 25 mm, the proposed method achieves a mean absolute percentage error (MAPE) of 10.75% in body length estimation, demonstrating its capability to accurately estimate the mean body length of insect populations. This study provides data support for species identification in current weather-radar-based insect monitoring and shows promise for applications in large-scale pest early warning, species classification, and ecological dynamics research. Full article

The covariance matrix performs a vital role for space-time adaptive processing (STAP) in airborne multiple-input multiple-output (MIMO) radar. As is known, the clutter-plus-noise covariance matrix (CPNCM), reflecting the statistical characteristics of radar echo, is a key component for MIMO-STAP. Commonly, an ideal CPNCM is impossible to obtain, and it must be estimated with sufficient snapshots. According to the RMB rule, MIMO-STAP requires many snapshots since MIMO radar has a high degree-of-freedom (DoF) due to its orthogonal transmit waveform. However, this is hard to satisfy in practice. This paper develops a novel covariance matrix estimation method under limited snapshots in airborne MIMO-STAP radar. Motivated by the random matrix theory, the proposed method enhances the CPNCM estimation by noise and clutter sample eigenvalues adjustment (EA). Concretely, the sample eigenvalues of noise are adjusted as noise power, and the ones of clutter are adjusted through minimizing the radar output power. Then, with the sample eigenvectors and adjusted sample eigenvalues, an effective CPNCM is formulated, and EA-MIMO-STAP is implemented reliably. Multiple experiments demonstrate that EA-MIMO-STAP has superior performance and robustness. Full article

Optical and synthetic aperture radar (SAR) image registration faces challenges from nonlinear radiometric distortions and geometric deformations caused by different imaging mechanisms. This paper proposes a coarse-to-fine registration algorithm integrating geographic information constraints with cross-modal feature consistency mapping. The coarse stage employs imaging geometry-based coordinate transformation with airborne navigation data to eliminate scale and rotation differences. The fine stage constructs a multi-scale phase congruency-based feature response aggregation model combined with rotation-invariant descriptors and global-to-local search for sub-pixel alignment. Experiments on integrated airborne optical/SAR datasets demonstrate superior performance with an average RMSE of 2.00 pixels, outperforming both traditional handcrafted methods (3MRS, OS-SIFT, POS-GIFT, GLS-MIFT) and state-of-the-art deep learning approaches (SuperGlue, LoFTR, ReDFeat, SAROptNet) while reducing execution time by 37.0% compared with the best-performing baseline. The proposed coarse registration also serves as an effective preprocessing module that improves SuperGlue’s matching rate by 167% and LoFTR’s by 109%, with a hybrid refinement strategy achieving 1.95 pixels RMSE. The method demonstrates robust performance under challenging conditions, enabling real-time UAV-based multi-sensor fusion applications. Full article

Aimed at the elevation ambiguity problem in array synthetic aperture radar (SAR) three-dimensional imaging, this paper proposes a novel ambiguity-resolving method based on wavefront modulation. By introducing measured plasma lens modulation phases and constructing an array SAR signal echo model incorporating wavefront modulation, the method effectively overcomes the physical size limitations of traditional array antennas. Theoretical analysis demonstrates that wavefront modulation significantly reduces the grating lobe level of the array pattern, equivalently increasing the number of array channels and thereby shortening the shortest baseline length, which enhances the system’s maximum unambiguous height. At the signal processing level, an observation equation based on compressed sensing is established, and target reconstruction is achieved using the Orthogonal Matching Pursuit (OMP) algorithm. Monte Carlo simulation results indicate that under the same signal-to-noise ratio conditions, when the observation range is extended to twice the theoretical maximum unambiguous height, the proposed method maintains a reconstruction success rate of over 95%, whereas the traditional method’s reconstruction success rate drops rapidly below 40% once the maximum unambiguous range is exceeded. This study also investigates the 3D reconstruction of spatial point targets and a rectangular building, with the analysis of their theoretical ambiguous positions confirming the method’s effectiveness in suppressing ambiguous targets in the vicinity of spatial point targets as well as in front of and behind the structure. This study provides a new technical approach to overcoming antenna size constraints on airborne platforms, with significant application value in fields such as digital elevation model construction and urban 3D imaging. Full article

Recently, when forming radar video frames for surface mapping, group-interacting compact onboard radar systems (CORS) are increasingly being utilized. In this context, for the cooperative functioning of the group, each compact radar should use its own unique marked signal as the probing signal. This signal must be distinguishable in the common channel and should not destructively affect the probing signals emitted by other radars within the group. This organization allows for associating the marked signals reflected from the underlying surface with specific CORS in the group. This requirement arises because each compact onboard radar in the group emits a single probing signal and then receives all reflected signals from the surface emitted by the other CORS in the group. Such an organization of the group-based system of technical vision requires the search for and study of specialized marked code structures used for phase modulation of probing signals to identify them in the shared radar channel. The study focuses on the search for new complex M-sequences with lower sidelobe levels of the normalized autocorrelation function compared to traditional M-sequences. This is achieved by replacing the traditional alphabet of positive and negative ones with an asymmetric set consisting of complex numbers. Using numerical methods and computer simulations, the optimal complex values of the sequence with the minimum sidelobe level in the autocorrelation function are determined. In addition to correlation properties, the phase-modulated signals generated based on the new marked sequences are also investigated. The results obtained open new possibilities for the construction of a group-based technical vision system, enabling cooperative surface probing among all CORS in the interacting group. Full article