The emerging high-altitude platform station (HAPS, such as the near-space airship) is located in the stratosphere at a height around 20 km and has great potential in remote sensing due to its insensitivity to severe weather conditions and the advantage of long dwell time over observation area of interest. Traditionally, airborne Doppler radars employ fixed-angle symmetric coplanar analysis technique (FA-SCAT) for 3-D wind field (3D-WF) retrieval in severe weather. However, FA-SCAT fails in HAPS-borne weather radar due to its low velocity (usually <15 m/s) and subsequently low spatial coverage. In this letter, a novel multiangle asymmetric coplanar analysis technique (MA-ASCAT) for 3D-WF retrieval applied to HAPS-borne phased array radar is proposed. It utilizes multiple observations of the same target bin to retrieve 3D-WF based on the multiple-angle data collected by multiple HAPSs at multiple observation positions. The MA-ASCAT performance is deduced theoretically, which is used to optimize the systematic scanning strategy. The method is validated through simulation and real data experiments. Results demonstrate that MA-ASCAT achieves superior detection coverage and accuracy compared to FA-SCAT.

Radial velocity data of Doppler weather radars are crucial for wind field retrieval and nowcasting, but multi-fold velocity aliasing in short-wavelength radar systems (e.g., X/Ku-band) severely restricts their application. Conventional velocity dealiasing methods relying on velocity continuity fail in such scenarios due to unreliable initial reference velocity determination. To resolve this challenge, the Morphology-based Zero Iso-Doppler Contour (Zero-Isodop) Extraction and Velocity Correction Algorithm (MZI-VCA) is proposed for multi-fold velocity-aliased weather radar. The method first constructs a large-scale uniform horizontal wind model to characterize aliased zero-isodop distributions, then applies morphological operations combined with Depth-First Search (DFS) for candidate isodop extraction in Plan Position Indicator (PPI) images, and finally identifies the zero-isodop through spatial structure metrics. Leveraging the extracted zero-isodop, dealiasing is performed under spatial continuity constraints. Validations using both simulated multi-fold aliasing data (derived from S-band radar) and Ku-band weather radar observations demonstrate that MZI-VCA achieves superior performance compared to the traditional dealiasing method UNRAVEL. MZI-VCA achieves 100% dealiasing success rate in correcting double- and triple-fold aliasing, with a success rate of 90.74% across all diverse multi-fold scenarios tested in 108 simulated volume scans. In contrast, UNRAVEL fails to resolve multi-fold aliasing, exhibiting only a 6.92% Probability of Detection (POD). The systematic method establishes a new framework for resolving multi-fold velocity aliasing in short-wavelength radar systems.

Animal migration poses risks to human health and economic stability, highlighting the need for effective monitoring. Weather radars are essential tools for monitoring migratory insects and birds. While S-band radars can accurately distinguish insect and bird echoes, X-band radar, offering higher resolution, has not been sufficiently explored, limiting its use in aerial ecological monitoring. In this paper, joint observational experiments were conducted to evaluate insect and bird echoes from S-band and X-band weather radars. The results show significant overlap in the polarization features on X-band radar, making existing algorithms unsuitable for X-band data. To address this issue, a point-surface feature fusion method is proposed. This approach extracts polarization variables to construct point-scale features for initial classification with statistical models. A residual network captures surface-scale morphological features, which are integrated with the point-scale recognition results. Finally, a feature fusion module generates the final classification. The method achieves a mean intersection-over-union (mIoU) of 84.56% and demonstrates high accuracy and robustness in historical data tests. This study enhances X-band radar’s ability to differentiate between insect and bird echoes, providing a new solution for aerial ecological monitoring.

Wind profile information can be used to study high-altitude background wind field or to monitor near-surface strong winds, which plays an important role in revealing the characteristics of large-scale weather systems. Velocity-azimuth display (VAD) is the most widely used method for wind profile retrieval in Doppler radar, in which the linear wind field is assumed and the Doppler velocity information uniformly sampled at equal azimuth intervals is used. However, for severe convective weather systems such as typhoons and tornadoes, the spatial distribution of wind fields is complex and the assumption of linear wind field may be not satisfied. Besides, when the weather system is not evenly distributed around the radar or concentrated in a certain direction, the uniform sampling at equal azimuth intervals cannot be guaranteed, resulting in the decrease of retrieval accuracy. In this paper, a wind profile retrieval method based on non-uniform sampling VAD (nVAD) technique is proposed. By establishing a nonlinear spatial distribution model of complex wind fields and using the radial Doppler velocity information non-uniformly sampled at azimuth, the traditional VAD can be modified and the wind profile can be obtained using the numerical analysis where the radar observations are acquired at a set of different elevation angles. The nVAD technique is not limited by the truncated Taylor expansion and the nonlinear wind field can better represent the real complex wind field structure, and the non-uniform sampling method is more suitable for the case with missing measurement. The verification results show that the nVAD technique can effectively retrieve the wind profile for complex wind field when the Doppler velocity is sampled non-uniformly and has better performance than the traditional VAD technique.

In recent years, the rainfall estimator that combines the specific attenuation A and the specific differential phase${K} _{\text {DP}}$for X-band radar has been concerned and developed. However, the constraints of empirical coefficients and insufficient resolution of A and${K} _{\text {DP}}$estimates, as well as the uncertainties caused by the unknown shapes of raindrops, pose challenges to the estimator in maintaining accuracy of rainfall estimates. The high correlation between the differential reflectivity${Z} _{\text {DR}}$and the raindrop shape helps to mitigate the uncertainties associated with the variations of drop size distribution (DSD) and the unknown shapes of raindrops. However, as a power measurement,${Z} _{\text {DR}}$is inevitably affected by radar miscalibration, partial beam blockage (PBB), and bias from wet radome, which hinders its application for rainfall estimation. The backscattering differential phase$\delta _{\text {hv}}$is also strongly dependent on raindrop shape and is not affected by the above negative factors, so it has the potential to be the substitute for${Z} _{\text {DR}}$. Unfortunately, reliable method for estimating$\delta _{\text {hv}}$in rain is currently lacking. This article reviews an adaptive and high-resolution (HR) method for estimating A and${K} _{\text {DP}}$called adaptive and high-resolution empirical coefficient conditioning (AHRCC), and based on the outputs of AHRCC, proposes a method for estimating$\delta _{\text {hv}}$accurately, which mainly reduces the cumulative bias caused by path integral. In addition, an algorithm for rainfall estimation based on A,${K} _{\text {DP}}$, and$\delta _{\text {hv}}$is proposed to reduce the overestimation of rainfall caused by DSD variations and raindrop shape uncertainties, and the potential of retrieving characteristic raindrop sizes by$\delta _{\text {hv}}$is also explored.

Polarimetric weather radar measurements vary nonlinearly with changes in radar frequency and scanning elevation. In comparative observation experiments between Ku-band weather radar and CINRAD/SA radar, there were systematic errors in the polarimetric data of the two radars, the reason was the difference of frequency and elevation angles of them. Because the observation data of the ground-based Ku-band weather radar is small, it cannot cover most of the precipitation. So the T-matrix method was used to simulate the differential reflectivity factors of the Ku-band and S-band radars at different elevation angles, and the BP neural network was trained based on the simulation data to realize the conversion of the differential reflectivity factor from S-band to Ku-band to correct the systematic errors. Using the measured data, it was verified that the BP neural network can correct the systematic errors between the differential reflectivity factors of the two radars, improve the data consistency of them, and provide possibility for data fusion of S-band and Ku-band weather radars.

Computerized Ionospheric Tomography (CIT) serves as a crucial method for ionospheric monitoring, playing a significant role in space environment surveillance and earthquake prediction. Existing CIT techniques are mostly based on the Global Navigation Satellite System (GNSS) system, constrained by the distribution of receivers. CIT based on geosynchronous SAR (GEO SAR) presents a solution by leveraging Persistent Scatterer (PS) points within the scene. However, current CIT techniques using GEO SAR typically utilize PS points from a single SAR image. This paper introduces a novel approach – a GEO SAR-based long-term joint multi-image CIT method. This method enhances the exploitation of satellite data, offers a broader range of observation angles, and improves tomography accuracy. Finally, through a CIT experiment involving three GEO SAR images, the electron density distribution is derived with a time resolution of 10 minutes. The results align with the International Reference Ionosphere (IRI) data, validating the feasibility and advantages of the proposed method. It is noteworthy that the proposed method caters to the tomography observation mode of a single satellite and is adaptable to each satellite within a satellite formation.

Alternate transmission and alternate reception (ATAR) mode, as a crucial polarization mode, enables a single channel to be utilized in dual-polarization weather radars, which reduces the weight, power, and cost of the radar. The ATAR mode has a broad application based on high-altitude platforms with limited load and power. The interval between co-polarization signals is twice the pulse repetition time (PRT) in the ATAR mode. Thus, the Nyquist velocity interval is half of the one existing in the traditional radars. This hampers the application of the ATAR mode in severe weather observations. In this letter, a velocity de-aliasing algorithm is proposed, where lag-1 cross-correlations between adjacent radials are utilized to calculate the velocity estimates. This extends the Nyquist velocity interval of the ATAR model twofold compared to that of the traditional algorithm which uses lag-1 autocorrelations between adjacent radials. In addition, the constraints of the proposed algorithm are analyzed. The proposed algorithm is validated via X-band PWR data, and the results show that compared to the true value, the biases of the dealiased velocities in the ATAR mode are within ±1m/s for 96.9% of the range bins. This method is also suitable for use with the alternate transmission and simultaneous reception (ATSR) mode.

Spectrum width (SW) is crucial for warning of severe weather, which is commonly estimated from a ratio of lag-0 to lag-1 autocorrelations (R0/R1). For traditional weather radars, the estimators with higher lags autocorrelation functions (ACFs) have been developed to enhance the performance at low signal-to-noise ratio (SNR) and narrow SW. In the alternate transmission mode, the interval between co-polarimetric signals is twice the pulse repetition time (PRT). Thus, the estimators for traditional radars cannot be applied to the ones with alternate transmission of horizontal and vertical polarized wave (AHV) modes. This article introduces the cross correlation functions (CCFs) to derive multi-lag estimators by least-squares Gaussian fitting of ACFs and CCFs. Moreover, multi-lag estimators are prone to saturate at large SWs, so a hybrid estimator combines the traditional estimator and the multi-lag estimators by comparing the predicted SWs with the SW thresholds. The predicted SWs are calculated by the R0/R2 estimator, and the standard deviation (SD) of the R0/R2 estimator is introduced to identify saturated SWs. SW thresholds at different SNRs and correlation coefficients (CCs) are obtained by minimizing the evaluation metric, which is the weighted sum of biases, SDs, and invalid estimate ratios. Finally, the hybrid estimator is validated via Ku-band weather radar data (surveillance scan mode). The results show that compared with the traditional estimator, the hybrid estimator reduces biases and SDs by about$0.1\sim 0.2$m/s at SNR$0.5\sim 1$m/s using the hybrid estimator when the radar operates in the Doppler scan mode.

Owing to the independence of the specific differential phaseKDPfrom attenuation and miscalibration, it is often applied in attenuation estimation (i.e. estimation of the specific attenuationAfromKDP). However, the measured differential phase ΨDPincludes the backscattering differential phase δhvand noise, decreasing the accuracy ofKDPestimation. Additionally, considering that the coefficient γ of theA-KDPempirical relation is sensitive to temperature and drop size distribution, there is also concern over ana priorifixed value for γ. Especially at higher-frequency bands such as X-band, largeKDPcan amplify the biases caused by inaccurate γ. AHR and ZPHI are methods for estimating phase and attenuation respectively. Their opposite inputs and outputs create a strong coupling between the two methods, allowing both methods to simultaneously reduce the impact of contaminated ΨDPon estimation. Meanwhile, their high resolution makes it possible to conditioning γ adaptively at range resolution scale according to different rainfall situations. In this work, a method that combines the individual and common characteristics of AHR and ZPHI is proposed to improve the accuracy of phase and attenuation estimation by adaptively conditioning γ at high resolution, as well as reducing the biases of δhvand noise. The improved effects of this method are assessed with a typical storm event observed by a polarimetric X-band weather radar in Netherlands, and its performances are further evaluated with simulations comprehensively. The results show that this method can improve accuracy of both phase and attenuation estimation significantly, especially at largeKDP.

Recent advances in deep learning have significantly improved the quality of precipitation nowcasting. Current approaches are either based on deterministic or generative models. Deterministic models perceive nowcasting as a spatiotemporal prediction task, relying on distance functions like L2-norm loss for training. While improving meteorological evaluation metrics, they inevitably produce blurry predictions with no reference value. In contrast, generative models aim to capture realistic precipitation distributions and generate nowcasting products by sampling within these distributions. However, designing a generative model that produces realistic samples satisfying meteorological evaluation indexes in real-time remains challenging, given the triple dilemma of generative learning: achieving high sample quality, mode coverage, and fast sampling simultaneously. Recently, diffusion models exhibit impressive sample quality but suffer from time-consuming sampling, severely hindering their application in nowcasting. Moreover, samples generated by the U-Net denoiser of current denoising diffusion model are prone to yield poor meteorological evaluation metrics such as CSI. To this end, we propose a spatiotemporal Transformer-based conditional diffusion model with rapid diffusion strategy. Concretely, we incorporate an adversarial mapping-based rapid diffusion strategy to overcome the time-consuming sampling process for standard diffusion models, enabling timely nowcasting. Additionally, a meticulously designed spatiotemporal Transformer-based denoiser is incorporated into diffusion models, remedying the defects in U-Net denoisers by estimating diffusion scores and improving nowcasting skill scores. Case studies of typical weather events such as thunderstorms, as well as quantitative indicators, demonstrate the effectiveness of the proposed method in generating sharper and more precise precipitation forecasts while maintaining satisfied meteorological evaluation metrics.

Deep learning (DL)-based precipitation nowcasting algorithms have garnered significant attention in recent years. However, the presence of variable spatial scales in precipitation patterns poses challenges for methods that solely focus on capturing spatiotemporal correlations at a single scale. Moreover, current DL-based algorithms tend to model short-term (e.g., 10-min time span) rainfall locally neglecting long-term, global (e.g., 2-h time span) life-cycle evolution. Furthermore, widely used pixel-wise losses are prone to produce low effective-spatial-resolution predictions. To this end, we introduce a multiscale deformable transformer network to leverage echo contexts from image patches of varying spatial scales. Meanwhile, a multihead deformable self-attention mechanism is introduced for capturing precipitation spatiotemporal dynamics in a global manner. Moreover, to improve the spatial resolution of predictions, the Fourier space regularization and adversarial losses are proposed by narrowing the discrepancy of the Fourier spectra of predictions and references. Thanks to the introduced loss function, our model generates highly effective spatial-resolution predictions with abundant details. Extensive experiments on two real datasets show the substantial superiority of our method in terms of critical success index (CSI) compared to recent competitive approaches. At the same time, our predictions have more realistic precipitation details and significantly better fidelity. For example, on a vertically integrated liquid (VIL) product dataset, compared to baseline methods, our approach reduces the Fréchet inception distance (FID) value by a factor of$2\sim 4$while improves the CSI score by 3%~5% approximately.

This paper proposes an enhanced framework for reconstructing three-dimensional (3-D) building model from airborne tomographic synthetic aperture radar (TomoSAR) point clouds which involves four crucial steps: progressive facade detection, extraction of roof points, extraction of roof outlines and reconstruction. Firstly, an efficient and robust building facade detection is undertaken by adopting a progressive detection method. Then, to extract roof points, the region growing procedure is improved by utilizing an adaptive neighborhood and robust normals of points. Subsequently, the α-shape algorithm is executed and enhanced through Delaunay triangulation network to extract fine outline points. Finally, roof outlines are regularized, and the building models get reconstructed. The proposed approach is tested using airborne TomoSAR point clouds in the Emei area located in the Sichuan province of China. The results show that the proposed method can reconstruct 3-D building models with better shapes compared to classical methods.

This paper analyzes the reliability of the reflectivity data from the Ku-band airship-borne weather radar (AR) based on the observations of four precipitation processes using the AR and China's S-band new generation weather radar (CINRAD/SA) between 2022 and 2023. When observing relatively uniform precipitation, the standard deviation of the reflectivity data from the AR is consistently more than 80% lower than the theoretical 1dB. This indicates that the reflectivity data from the AR exhibits minimal fluctuations and satisfies the design requirements of the radar. Furthermore, when observing convective precipitation, the echo structures observed by the AR are generally consistent with those observed by CINRAD/SA. However, the AR has a higher range resolution, enabling it to capture more detailed precipitation structures. Lastly, the average deviation between the reflectivity data from the AR and the matching data from CINRAD/SA is less than 1dB, indicating good linear consistency. This suggests that the reflectivity data from the AR is reliable.

Designing repeat ground track (RGT) synthetic aperture radar (SAR) constellations for achieving rapid revisits over key areas is essential to employ spaceborne differential interferometric synthetic aperture radar (D-InSAR) technology in Earth observation missions such as geological disaster monitoring and prediction. In this paper, the features of average revisit time (ART) maps are first introduced and investigated, and then an efficient and resource-friendly approach to calculate the ART of constellations is proposed. On this basis, a systematic method for designing an RGT constellation is provided, incorporating lookup-table-based optimization. Once the requirements of the expected RGT constellation, the incident angle of sensors on the constellation, and the orbital elements of the seed satellite in the constellation are given, the range of the optimal inclination and longitude of the ascending node (LAN) of the seed satellite can be found and then the entire constellation is determined. The proposed method enhances the efficiency of revisit time analysis and avoids the repeated modeling when the observation requirements change. Therefore, it is applicable not only prior to launch but also guides orbital maneuvering to adjust constellation configuration for an effective response to sudden disasters, etc. Finally, multiple RGT constellation design tasks are presented to demonstrate the proposed method.

The errors of time and frequency synchronization in bistatic synthetic aperture radar (SAR) can have serious impacts on SAR images, if not compensated well. In this paper, we analyze the influence of time synchronization error on bistatic geosynchronous SAR (GEO SAR) imaging. First, the GEO SAR echo signal model under the influence of time synchronization error is established, through which the impacts of the constant, linear and random time synchronization errors on imaging are analyzed theoretically. Second, the theoretical analyses were verified by simulations. In addition, through simulations, the influences of the time synchronization error on low earth orbit SAR (LEO SAR) and GEO SAR are compared. The results show that the impacts of the constant, linear and random terms on GEO SAR are the same, greater and slighter compared with those on LEO SAR, respectively. This study provides a theoretical basis for future research on the time synchronization technology of multi static GEO SAR.

Traditional interferometric synthetic aperture radar (InSAR) is based on broadside looking geometry and parallel tracks. With the increase of the orbit height in spaceborne SAR and the development of SAR constellations, InSAR data of a region can be acquired in complex geometry, especially squint beam steering and unparallel tracks. For the sake of optimal InSAR system design and data processing, it is necessary to model the geometric decorrelation in complex geometry. This letter derives an accurate analytical model of geometric decorrelation of SAR interferometric pairs for general SAR observation geometry. Nonidentity of impulse responses and nonorthogonal sidelobes are the main features hindering the model derivation in the complex geometry case. An impulse response-fitting method is proposed, where nonorthogonal bases are adopted to suit the features and, thus, accurately analyze the geometric decorrelation. Simulation results verify the analytical model. It is found that unparallel tracks will introduce an extra geometric decorrelation factor. Compared to cases of parallel tracks, unparallel tracks always worsen the geometric decorrelation and cannot be neglected.

A GEOsynchronous Spaceborne-Airborne Bistatic Synthetic Aperture Radar (GEO SA-BSAR) system has been proved to be a significant tool for moving targets monitoring. Due to the special geometry model of the GEO SA-BSAR system, there is a complex relative movement between the moving target and the bistatic radar, leading to an additional phase modulation of the echo and further, causing moving targets to be smeared in the SAR image. Recently, Deep Neural Network (DNN) shows great potential in rapid image recovery. However, most image recovery methods based on DNN concentrate on the whole image, which limits the imaging performance of sparse targets. In this letter, we propose a DNN framework with similarity constraints for GEO SA-BSAR moving target imaging. This DNN-based method optimizes the cosine similarity of azimuth signals between the ground-truth image and the predicted image in the loss function to recover the azimuth position and focusing characteristics of the sparse targets. Extensive experimental results prove that the proposed model can quickly obtain GEO SA-BSAR moving target images with small training datasets compared with some counterparts.

Doppler weather radar observations of strong convective weather phenomena, e.g., tornadoes and supercells, showed that their Doppler spectra could deviate from the Gaussian shape. Classical method for identification of non-Gaussian spectral signal (NGSS) is based on spectral width and high-order spectral moments (HOSM). The DFT processing is needed to obtain the power spectrum density to calculate HOSMs, increasing the computation complexity. Besides, power, mean velocity, and spectral width are often obtained using autocorrelation method, i.e., pulse-pair-processing (PPP) method which is generally considered to be the most efficient estimator, where a Gaussian spectrum is assumed. Thus, a bias in the mean velocity and spectral width will occur if the Doppler spectrum deviates from the Gaussian shape. In this article, a generalized PPP (GPPP) method, which calculates HOSMs using autocorrelation function directly, is proposed. It outputs two additional identification parameter products, including skewness and kurtosis, which can directly identify NGSS and contribute to select the appropriate spectral moment estimation algorithm for Gaussian-or-not spectral signal. Compared with the classical method, the NGSS identification based on the GPPP method has a lower computation complexity since the DFT processing is avoided and has a better performance especially for low signal-to-noise ratio conditions. Besides, the clustering algorithms including expectation maximization and K-means algorithms are investigated for the adaptive spectral moment estimation of NGSS for the first time. The numerical simulation experiments and verifications based on real weather radar data of an actual supercell are implemented to confirm the feasibility and superiority of proposed method.

Nowadays deep learning based weather radar echo extrapolation methods have competently improved nowcasting quality. Current pure convolutional or convolutional recurrent neural network based extrapolation pipelines inherently struggle in capturing both global and local spatiotemporal interactions simultaneously, thereby limiting nowcasting performances, e.g., they not only tend to underestimate heavy rainfalls’ spatial coverage and intensity but also fail to precisely predict non-linear motion patterns. Furthermore, the usually adopted pixel-wise objective functions lead to blurry predictions. To this end, we propose a novel motion-guided global-local aggregation Transformer network for effectively combining spatiotemporal cues at different time scales, thereby strengthening global-local spatiotemporal aggregation urgently required by the extrapolation task. First, we divide existing observations into both short and long term sequences to represent echo dynamics at different time scales. Then, to introduce reasonable motion guidance to Transformer, we customize an end-to-end module for jointly extracting Motion Representation of Short and Long term echo sequences (MRS, MRL), while estimating optical flow. Subsequently, based on Transformer architecture, MRS is used as queries to retrospect the most useful information from MRL for an effective aggregation of global long-term and local short-term cues. Finally, the fused feature is employed for future echo prediction. Additionally, for the blurry prediction problem, predictions from our model trained with an adversarial regularization achieve superior performances not only in nowcasting skill scores but also in precipitation details and image clarity over existing methods. Extensive experiments on two challenging radar echo datasets demonstrate the effectiveness of our proposed method.

Recently, frequency-modulated continuous-wave (FMCW) radar-based hand gesture recognition (HGR) using deep learning has achieved favorable performance. However, many existing methods use extracted features separately, i.e., using one of the range, Doppler, azimuth, or elevation angle information, or a combination of any two, to train convolutional neural networks (CNNs), which ignore the interrelation among the 5-D time-varying-range-Doppler-azimuth-elevation feature space. Although there have been methods using the 5-D information, their mining of the interrelation among the 5-D feature space is not sufficient, and there is still room for improvements. This article proposes a new processing scheme of HGR based on 5-D feature cubes that are jointly encoded by a 3-D fast Fourier transform (3-D-FFT)-based method. Then, a CNN is proposed by building two novel blocks, i.e., the spatiotemporal deformable convolution (STDC) block and the adaptive spatiotemporal context-aware convolution (ASTCAC) block. Concretely, STDC is designed to cope with hand gestures’ large spatiotemporal geometric transformations in the 5-D feature space. Moreover, ASTCAC is designed for modeling long-distance global relationships, e.g., relationships between pixels of the feature at the upper left corner and lower right corner, and exploring the global spatiotemporal context, in order to enhance the target feature representation and suppress interference. Finally, our presented method is verified on a large radar dataset, including 19 760 sets of 16 common hand gestures, collected by 19 subjects. Our method obtains a recognition rate of 99.53% on the validation dataset and that of 97.22% on the test dataset, which is significantly better than state-of-the-art methods.

A long aperture time is required to achieve a high signal-to-noise ratio and high azimuth resolution in geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR) system for moving target indication (MTI). The range walk migration because of the target's motion cannot be ignored for such a long time, and the second-order range model fails for the moving target. In this paper, an adaptive MTI method is proposed for GEO SA-BSAR with a long aperture time, ensuring the moving target's detection and location. Firstly, an adaptive spatial filter modified by the accurate GEO SA-BSAR multichannel signal model is applied to clutter suppression and beamforming. Next, we adopt the generalized Radon-Fourier transform to maximize the signal-to-noise ratio of the moving target with unknown motion parameters. Then, the moving target can be detected, and its position has been obtained. Finally, the simulation experiments are conducted to show the effectiveness of our technique.

Geosynchronous Synthetic Aperture Radar (GEO SAR) has advantages of a short revisit time and large coverage for the scene of interest, so lots of theories and analysis toward the GEO SAR have been developed. However, GEO SAR systems may generate radio frequency interference (RFI) to a low earth orbit SAR (LEO SAR), causing a decrease in Signal-to-Interference-plus-Noise Ratio (SINR) of SAR images. In order to evaluate the GEO-to-LEO RFI effect on imaging, we deduce the formulas of the RFI power and image SINR, and verify them by comparing them with numerically evaluated results from simulated images. Based on the formulas, we evaluate SINRs of LEO SAR images for different bistatic scattering coefficients. The results show that when the target forms a specular bistatic scattering geometric relationship with a GEO SAR and a LEO SAR, LEO SAR image quality is poor, with a SINR worse than 5 dB, but the RFI effects can be neglected in other cases.

Coherence-based synthetic aperture radar (SAR) tomography (TomoSAR) exploits the complex coherences of SAR images to achieve 3-D imaging. Utilizing two-sensor spaceborne SAR formation flying to realize coherence-based TomoSAR has attracted increasing attention because temporal decorrelation-free interferograms can be constructed; therefore, TomoSAR has excellent potential for inverting the vertical structures of natural scenes such as forests and glaciers. However, low earth orbit (LEO) TomoSAR is disadvantaged by limited data and nonuniform sampling in the elevation direction. Geosynchronous (GEO) TomoSAR can overcome these limitations owing to its short revisit time of no more than 24 h. For the first time, this article discusses coherence-based TomoSAR exploiting GEO SAR formation flying. The benefits of GEO-formation coherence-based TomoSAR, including the low cost of slave satellites, rich data sets, and uniform sampling, are noted. The key problems of system design, including the formation design and data acquisition, are discussed. A formation design method based on the minimum along-track baseline is proposed that can realize uniform elevation sampling. The geometric correlation of a general SAR observation geometry is derived; on this basis, an optimal data acquisition method based on the optimal height measurement Cramer-Rao lower bound (CRLB) is proposed. Finally, the performance of GEO-formation coherence-based TomoSAR is analyzed; in particular, the ambiguity height in the altitude direction, the Rayleigh resolution in the altitude direction, and the theoretical optimal geometric correlation are evaluated. Finally, computer simulations validate the proposed formation design method, data acquisition scheme, and performance analysis formula.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR), consisting of GEO transmitter and airborne receiver, has stable coverage for a long time and benefits moving target detection. The performance of GEO SA-BSAR moving target indication (GEO SA-BSAR MTI) system is investigated. This work provides the first analytical connection between target detection performance and configuration parameters based on the optimum output signal to clutter and noise ratio (SCNR) criterion. Furthermore, a theoretical analysis of motion parameter estimation is stated in terms of the Cramer-Rao lower bounds (CRLB). The SCNR loss and CRLB provides a necessary design configuration for GEO SA-BSAR MTI system. Finally, the optimized configuration is provided by comparing the performance.

In this paper, we develop a simulator that could simulate polarimetric data of airship-borne typhoon observation radar. We use the Weather Research and Forecast (WRF) model to generate a `realistic' typhoon phenomenon, combined with the T-matrix method to calculate the scattering properties. Mixtures of rain, snow, graupel and ice relative to typhoon phenomenon are considered. Polarimetric observables also depend on the angle of the incident radar beam, so we analyzed the influence of incident angles on radar observables. The simulated polarimetric observables showed a good agreement with those in previous literature.

Multistatic geosynchronous synthetic aperture radar (GEO SAR) utilizes multiple satellites' transmitted and received signals simultaneously to generate multiple phase centers (PCs) and to reduce the synthetic aperture time and the power budget. Two key problems of this SAR system are the design of the PC array configuration to satisfy the resolution requirements and the suppression of the grating lobes that are inherently introduced by the PC array. The resolution of the SAR can be analyzed using generalized ambiguity functions (GAFs). However, the current research on the multistatic SAR resolution and grating lobes is mostly based on specified configurations and numerical simulation methods, which cannot establish the relationship between the system parameters and the resolution. This article proposes an analytical analysis method for the multistatic GEO SAR GAF-based on the array spatial ambiguity function (ASAF). First, gradient analysis is used to obtain analytical expressions for ASAF and the multistatic SAR GAF. On this basis, an analytical resolution expression is obtained, and an orbital element design method that considers the Earth's rotation is proposed. In addition, the lobe positions are analytically expressed based on the geometry, and the grating lobes are suppressed by designing the optimal integration time such that the null depth of the velocity ambiguity function (VAF) coincides with the grating lobe positions. Finally, simulation results at various positions for various orbit types demonstrate the accuracy of the GAF approximation formula and the satisfactory performance of the optimal time expression in suppressing the grating lobes.

Despite the increasing interest in geosynchronous synthetic aperture radar (GEO SAR) systems, the ground moving target indication and motion parameters estimation aspect have never been addressed in GEO SAR scenarios. In this article, we tackle the issue of multiple moving target velocity estimation in GEO SAR. We develop new closed-form expressions that relate both Doppler centroid and Doppler rate to the target motion parameters in GEO SAR by considering the specific features of a geosynchronous orbit. Furthermore, we propose a new velocity estimation algorithm that combines the nonuniform cubic phase function (NU-CPF) algorithm with the newly developed models to estimate the moving target's two velocity components. Moreover, based on the above-mentioned technique, we further propose a solution to address the multiple moving targets' problem. Simulation results along with an estimation accuracy analysis are provided to demonstrate the effectiveness of the proposed multitarget GEO SAR velocity estimation technique.

Geosynchronous spaceborne-airborne bistatic multichannel synthetic aperture radar (GEO-SABM SAR) is a form of bistatic SAR constructed by a GEO transmitter and an airborne multichannel receiver. The frequent coverage of earth observations in GEO-SABM SAR provides a great advantage in SAR-ground moving target indication (SAR-GMTI). However, the bandwidth of the azimuth spectrum in this configuration, which is larger than the pulse repetition frequency (PRF), makes the whole spectrum fold into several subsegments, i.e., azimuth spectrum aliasing (ASA) occurs. In addition, azimuth Doppler ambiguity (ADA) caused by the larger radial velocity of the moving target may occur simultaneously. To address these problems, an improved velocity SAR (VSAR)-based azimuth spectrum reconstruction method for moving targets is proposed. The influence of ASA and ADA on the signal of the moving targets is analyzed, and a signal model after coarse imaging with static target parameters is deduced. From the signal model, each subsegment has different Doppler frequencies and can be extracted based on improved VSAR. Meanwhile, the Doppler ambiguous integer has a definite relationship with the slope of the range migration of the subsegments after a range cell migration correction (RCMC) based on the static target parameters and can be estimated by this relationship. Furthermore, based on the extracted spectrum subsegments and estimated Doppler ambiguous integer, the azimuth spectrum can be reconstructed. Finally, the effectiveness of the proposed method is verified by the numerical experiments.

Geosynchronous SAR tomography (GEO TomoSAR) is a technique that combines geosynchronous SAR (GEO SAR) with SAR tomography. It can make full use of the advantages of short revisit time and large observation area of GEO SAR to achieve timely and accurate three-dimensional (3D) reconstruction of targets. However, GEO SAR is severely affected by the temporal-spatial variant ionosphere, which causes image shift and induces residual phase between repeated orbital data. These can seriously affect tomographic performance. In the paper, the GEO TomoSAR signal model affected by the ionosphere is established based on the temporal-spatial variant total electron content (TEC) model of background ionosphere. Then the influence of ionosphere on different scatterers projected into the same pixel in GEO SAR images is analyzed, and the expression of residual phase error between repeated orbit images is given. After analysis, the influence of the background ionosphere on the GEO TomoSAR system mainly includes the relative offset of the scattering target and the imaging defocus in the elevation direction. Finally, the experimental verification was completed using US-TEC data.

Geosynchronous synthetic aperture radar (GEO SAR) has great potentials in synthetic aperture radar ground moving target indication (SAR-GMTI) due to its high time resolution and wide swath coverage. But the signal-to-noise ratio (SNR) of echo is too low and multichannel configuration cannot be realize in GEO monostatic SAR. Therefore, geosynchronous spaceborne-airborne bistatic multichannel synthetic aperture radar (GEO-SABM SAR) can realize enhanced SNR and multichannel configuration. In this paper, we establish the slant range model for GEO-SABM SAR and give the VSAR processing flowchart. Finally, the numerical experiments is given to verify the effectiveness of proposed method.

Existing ship inverse synthetic aperture radar (ISAR) imaging algorithms are not applicable, when the signal-to-noise ratio (SNR) is low, for the translational motion that cannot be well compensated by existing algorithms. To achieve ship ISAR imaging with low SNR, a ship ISAR imaging algorithm based on the generalized radon-Fourier transform (GRFT) is proposed in this paper. Considering not only the rotational motion but also the translational motion between the radar and the ship, the proposed algorithm uses the GRFT to simultaneously compensate the time-variant range envelopes and the Doppler phase. Thus, the signal coherence is fully utilized, and the coherent integration of the ship's multicomponent echo signal is realized. Subsequently, to overcome the problem of the heavy computational load and improve the efficiency of the proposed algorithm, the scheme of cascaded GRFTs that consists of the coarse GRFT and the subsequent fine GRFT is adopted. The coarse GRFT with large search ranges and intervals is aimed at obtaining the real ranges of ship scatter points' motion parameters. Based on the coarse GRFT result, the fine GRFT with small search ranges and intervals is performed to efficiently obtain the coherent integration result. Then, based on the coherent integration result, the constant false alarm rate (CFAR) detection is performed to obtain the desired scatter points and their amplitudes and motion parameters, and the multicomponent signal is reconstructed. Finally, based on the reconstructed multicomponent signal, the high-quality instantaneous ship ISAR image can be obtained. Computer simulations and experiment results validate the effectiveness of the proposed algorithm.

Synthetic aperture radar (SAR) tomography (TomoSAR) techniques exploit multipass acquisitions of the same scene with slightly different view angles, and allow generating fully 3-D images, providing an estimation of scatterers' distribution along range, azimuth, and elevation directions. This paper extends TomoSAR to geosynchronous SAR (GEO TomoSAR). First, the potential and performance of GEO TomoSAR were analyzed from the perspective of orbital perturbation and the resulting large spatial baseline. Then, the rotation-induced decorrelation problems induced by the along-track baseline component were analyzed. In addition, the optimized acquisition geometry and tomographic processing flow were given, and the computer simulation verification was also completed. Finally, the equivalent validation experiment based on Beidou inclined geosynchronous orbit (IGSO) navigation satellite was carried out to demonstrate the feasibility and effectiveness of GEO TomoSAR. The experimental system employs the Beidou IGSO satellite as illuminator of opportunity and a ground system collecting and processing reflected echoes. This is the first time to employ the data from repeat-track Beidou IGSO satellites for tomographic processing. The 3-D imaging of the urban area using this experimental system was presented and then verified using LiDAR cloud data as reference. The results show that GEO TomoSAR can form the baseline of the order of hundreds of kilometers in elevation, which has the ability to achieve a resolution of 5 m in elevation.

Geosynchronous SAR (GEO SAR) has better coverage and revisiting performance than the low Earth orbit SAR. Thus GEO SAR can implement the repeat-pass interferometry as its revisit time of 1 day and the orbit drift (forming baseline) induced by perturbation. In comparison, the twin GEO SAR formation configuration in which the slave satellite repeats the master satellite' track can realize shorter revisit time and adjustable baseline by tuning the orbit elements. In this paper, a validation experiment based on the Chinese Beidou IGSO navigation satellites is demonstrated to validate the feasibility and effectiveness of this concept. The validation experiment consists of data acquisition of the Beidou IGSO satellites signals, equivalent pre-processing, image processing and interferometry processing. In the end, we obtain the high-precision experimental results of deformation inversion, validating the feasibility of twin GEO SAR interferometry/differential interferometry.

An L-band geosynchronous synthetic aperture radar (GEO SAR) will be inevitably affected by ionosphere scintillation because of its low carrier frequency. Meanwhile, compared with the low Earth orbit (LEO) SAR, a higher orbit of GEO SAR makes it have a longer integration time and a longer operation time within the susceptible regions of ionospheric scintillation. Thus, its imaging is more sensitive to ionospheric scintillation, and the corresponding degradation will have a different pattern. However, few works are focused on the quantitative analysis of the ionospheric scintillation impacts on L-band SAR. Moreover, the parameters of ionospheric irregularities utilized in the analyses are hard to be determined. In this paper, we first deduced the azimuth point-spread function with the consideration of both the amplitude and phase scintillation. Then, based on the measurable statistical parameters of ionospheric scintillation, performance specifications, including azimuth resolution, azimuth peak-to-sidelobe ratio (PSLR), and azimuth integrated sidelobe ratio (ISLR) are obtained to fully evaluate the impacts. The analysis suggests that in GEO SAR imaging, the azimuth ISLR severely deteriorates, whereas degradations of the azimuth resolution and PSLR are negligible. Finally, the simulations and a real ionospheric scintillation monitoring experiment by employing Global Positioning System satellites receivers were conducted, verifying the conclusions that the serious degraded contrast and focus quality of the images are brought by the raised azimuth ISLR.

In the above paper[1]there are errors in several places: 1) the first lines of text at the top left of page 3; 2)equations (2),(11), (12), (13), and(15); and 3)Fig. 3. Their correct forms are presented here.

The 3-D deformation retrieval is significant for the accurate evaluation of geologic disasters (e.g., earthquakes and landslides). Multiple-aperture interferometry (MAI) is an effective method to obtain 3-D deformation, combined with the cross-heading tracks synthetic aperture radar (SAR) data. However, because of the limitations of the low earth orbit SAR, a long satellite revisit time, small common areas of the cross-heading tracks data, and the unsatisfied along-track deformation measurement accuracy usually exist in the traditional MAI 3-D deformation retrieval. Geosynchronous SAR (GEO SAR) runs in the geosynchronous orbit, which has the advantages of a large observation area and a short revisit time. This paper focuses on 3-D deformation retrieval by GEO SAR MAI processing. Aiming at the high orbit and the squint looking of GEO SAR, the accurate expressions of the along-track deformation, 3-D deformation, and the errors in GEO SAR MAI processing are given. The distortions and their correction in the MAI interferogram brought by the geometrical difference between the forward- and backward-looking interferograms and the multicycles flat-earth and topographic phases are given. Moreover, an optimal subaperture selection method based on minimum position dilution of precision is proposed. Finally, the effectiveness of the proposed method is validated by simulations and the experiment of BeiDou-2 inclined geosynchronous orbit navigation satellite. The theoretical analysis and the experimental results suggest centimeter-level and even millimeter-level deformation measurement accuracy could be obtained in 3-D by GEO SAR MAI processing.

The ionospheric scintillation induced by local ionospheric plasma anomalies could lead to significant degradation for geosynchronous earth orbit synthetic aperture radar (SAR) imaging. As radar signals pass through the ionosphere with locally variational plasma density, the signal amplitude and phase fluctuations are induced, which principally affect the azimuthal pulse response function. In this paper, the compensation of signal amplitude and phase fluctuations is studied. First, space-variance problem of scintillation is addressed by image segmentation. Then, SPECAN imaging algorithm is adopted for each image segment, because it is computationally efficient for small imaging scene. Furthermore, an iterative algorithm based on entropy minimum is derived to jointly compensate the signal amplitude and phase fluctuations. Finally, a real SAR scene simulation is used to validate our proposed method, where both the simulated scintillation using phase screen technique and the real GPS-derived scintillation data are adopted to degrade the imaging quality.

This paper studies echo simulations of spaceborne millimeter-wave cloud profiling radar (CPR). It employs the cloud reflectivity data from 94GHz CloudSat CPR to construct the real scene and the cloud scattering model. In the simulation, the radar model, the relative motion model, the cloud scattering model, the scattering particle motion model and the atmospheric propagation and attenuation model are established considering the characteristics of the millimeter-wave cloud radar. The US standard atmosphere mode is adopted to calculate the profile of atmospheric attenuations at the different heights. The System Tool Kit (STK) is used to generate the relative geometry and movement. Finally, all the aforementioned models are combined to construct the radar echoes according to the Doviak-Zrnic meteorological echo formulas. The simulation results are verified by comparing input simulation true values and experimental inversion values, and it shows a good consistence.

Since geosynchronous synthetic aperture radar (GEO SAR) has curved trajectories, back-projection algorithm (BPA) greatly fits for its imaging. However, for a scene with height variation, the reference range based on the fixed-height imaging grid under curved trajectories is inaccurate in azimuth back-projection. Resultantly, the GEO SAR image quality will be obviously deteriorated in high resolution imaging. To address the issue, this paper proposed the digital elevation model (DEM)-assisted BPA to realize the accurate high resolution GEO SAR imaging for the scene with height variation. DEM information is utilized to construct the imaging grid in the new method for generating the accurate reference range. Simulation results validate that the proposed method achieves good imaging performance for the scene with height variation.

The tropospheric time variance within the ultra-long integration time of Geosynchronous Synthetic Aperture Radar (GEO SAR) needs to be considered for imaging. Meanwhile, because of the curved trajectory and the very complex geometry relationship between the target position and the satellite, the traditional analysis method of troposphere on low earth orbit (LEO) SAR imaging will no longer be suitable in GEO SAR. In this paper, GEO SAR signal phase errors induced by the time variation of troposphere is obtained. Then, a GEO SAR signal's two-dimension frequency spectrum under the effects of the troposphere is derived for the first time. The GEO SAR 2-D image shift and image defocusing phase errors are derived analytically. At last, the atmospheric refractivity profile (ARP) data provided by the Chinese meteorological satellite FengYun-3 (FY-3) are used and the focusing results are presented to verify the aforementioned analysis.

L-band geosynchronous synthetic aperture radar (GEO SAR) images will most likely deteriorate in the presence of ionosphere scintillation interference due to the low carrier frequency of GEO SAR. Meanwhile, because of the high orbit and the long working time above the region with the active ionosphere, GEO SAR will experience ionospheric scintillation with a higher probability. To make the GEO SAR avoid being interfered by ionospheric scintillation, we propose an orbit-optimization strategy by utilizing the diurnal and geographical pattern of the ionospheric scintillation occurrence in this letter. As the equatorial region is likely to experience ionospheric scintillation during the specified time window from the early evening after sunset to midnight, the orbit can be optimized by tuning the GEO SAR orbit parameters (e.g., a proper time past perigee) to avoid imaging over the equatorial region during the specified time window. Finally, simulation is conducted to verify the effectiveness of the method under the proposed three types of GEO SAR orbits, and the corresponding effective sets of time past perigee are obtained.

One of the GEO SAR's characteristics of long integration time guarantees its fine resolution. But on the contrary, the ultra-long time boosts the multiple influences and then affects the focusing severely. In this paper, a validation experiment is presented to verify the feasibility of GEO SAR imaging under the condition of long integration time of around several hundred or even thousands of seconds. The experiment employs the Chinese Beidou IGSO navigation satellites as the illuminator of opportunity. The receiver is deployed on the top of a building and then a space-surface Bistatic SAR (SS-BISAR) configuration is constructed. A transponder consisting of two antennas and an amplifier is constructed for evaluating the resolution. The images of the transponder and the natural scene are focused well, which can validate the GEO SAR imaging feasibility though the linear track and constant speed assumptions fail.

Geosynchronous Synthetic Aperture Radar (GEO SAR) will be affected by ionosphere scintillation inevitably because it usually works at L band. In this paper, GEO SAR signal model in presence of ionospheric scintillation is proposed. The scintillation sampling model is employed to simulate ionospheric scintillation data. Several GEO SAR imaging simulations in different scintillation cases and a real ionospheric scintillation measurement in Zhuhai district of China are conducted to study the impacts of ionospheric scintillation on GEO SAR in azimuth. The results suggest that ionospheric scintillation will worsen the azimuth resolution, and rise azimuth peak sidelobe ratio (PSLR), and severely deteriorate azimuth integrated sidelobe ratio (ISLR).

Geosynchronous synthetic aperture radar (SAR) (GEO SAR) has the characteristic of long integration time; thus, the time-freezing model assumption of background ionosphere for traditional low Earth orbit (LEO) SAR no longer holds in GEO SAR. Furthermore, the background ionosphere variation within the integration time cannot be omitted either. In this letter, the variation of total electron content within integration time is analyzed and described in detail by using polynomial approximation, and a new GEO SAR signal model influenced by background ionosphere is also proposed. In view of this novel model, the analytical expression of image shift and defocusing phase error are derived in the first place. Then, a quantitative analysis for the image shift and image defocusing in the range and azimuth directions is conducted, and the performance bounds of time-varying parameters of background ionosphere effects on focusing are obtained. Finally, the U.S. Total Electron Content measured data are used to verify the theoretical results of background ionosphere effects on GEO SAR focusing.

Yaw steering is a method of Doppler centroid compensation performed by attitude steering of the satellite platform. However, the conventional yaw steering method does not work well as the satellite orbit height becomes higher, such as with the geosynchronous Earth orbit synthetic aperture radar (GEO SAR) which has 36 000-km orbit height. This letter primarily analyzes the Doppler properties of the GEO SAR in the satellite local coordinate systems, and gives the calculation formulas of the Doppler centroid frequency and instantaneous Doppler bandwidth. Based on the vector analysis, a new calculation method is proposed for the 2-D attitude steering angles in GEO SAR. The new method, considering Earth's rotation and elliptical orbit effects, can accurately compensate the Doppler centroid to zero. Then, according to the 2-D attitude steering angles and the characteristics of GEO SAR, the 2-D phase scan method is put forward to carry out the highly accurate compensation of Doppler centroid, which can avoid the difficulty of rotating and stabilizing the GEO SAR system. The simulations validate the conclusion in this letter.