The combination of Integrated Sensing and Communication (ISAC) and Mobile Edge Computing (MEC) enables devices to simultaneously sense the environment and offload data to the base stations (BS) for intelligent processing, thereby reducing local computational burdens. However, transmitting raw sensing data from ISAC devices to the BS often incurs substantial fronthaul overhead and latency. This paper investigates a three-tier collaborative inference framework enabled by Integrated Sensing, Communication, and Computing (ISCC), where cloud servers, MEC servers, and ISAC devices cooperatively execute different segments of a pre-trained deep neural network (DNN) for intelligent sensing. By offloading intermediate DNN features, the proposed framework can significantly reduce fronthaul transmission load. Furthermore, multiple-input multiple-output (MIMO) technology is employed to enhance both sensing quality and offloading efficiency. To minimize the overall sensing task inference latency across all ISAC devices, we jointly optimize the DNN partitioning strategy, ISAC beamforming, and computational resource allocation at the MEC servers and ISAC devices, subject to sensing beampattern constraints. We also propose an efficient two-layer optimization algorithm. In the inner layer, we derive closed-form solutions for computational resource allocation using the Karush-Kuhn-Tucker conditions. Moreover, we design the ISAC beamforming vectors via an iterative method based on the majorization–minimization and weighted minimum mean square error techniques. In the outer layer, we develop a cross-entropy-based probabilistic learning algorithm to determine an optimal DNN partitioning strategy. Simulation results demonstrate that the proposed framework substantially outperforms existing two-tier schemes in inference latency.

Synthetic Aperture Radar (SAR) tomography is an essential method for acquiring data used in the reconstruction of urban three-dimensional (3D) models. With advancements in SAR technology, polarization has been integrated into tomography for accurately identifying and locating target structures. Recent studies focus on independently enhancing the ability of compressed sensing (CS) or spectral estimation methods to utilize polarimetric data. Since urban areas contain both small-scale man-made objects and large-scale homogeneous or distributed targets, using either of these two methods alone cannot balance accuracy with the preservation of 3D details. To address this, this study presents an adaptive polarimetric SAR (PolSAR) tomography method based on scattering mechanism classification, which leverages the strengths of both CS and spectral estimation techniques. In this study, we focus on the inversion of a single scatterer, without any attempt to separate multiple scatterers. The proposed method classifies pixels into four categories based on their scattering mechanisms, typically corresponding to manmade and distributed targets. The processing algorithms for CS and spectral estimation are then selected automatically based on the classification. For the pixels processed using spectral estimation, an adaptive window is also employed to compute the covariance matrix. Furthermore, the method employs an optimal polarization basis projection technique to effectively utilize polarization information during the tomography process. Experimental results show that this approach significantly improves both reconstruction accuracy and structural preservation.

Global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) can improve the monitoring interval to one day due to the using of navigation satellites. Meanwhile, the low signal-to-noise ratio (SNR), poor image resolution, and the random focus position offsets cause large interferometric phase noise. In this letter, an interferometric phase noise reduction algorithm is proposed for GNSS-based InBSAR based on adaptive edge detection and temporal area filtering. An improved edge detection algorithm is adopted to solve the overlapping of resolution cells and phase interference caused by poor resolution. Then, to compensate the random focus position, an area filtering algorithm is proposed to find the temporal supporting area of persistent scatterers (PSs). Finally, the principal phase is extracted to reduce the interferometric phase error. The raw data are used to indicate the effectiveness of the proposed algorithm, and the best monitoring accuracy can reach millimeter level.

Spaceborne Synthetic Aperture Radar (SAR) Along-Track Interferometry (ATI) serves as a primary approach to measure the Total Surface Current Vector (TSCV) of the ocean. However, single spaceborne SAR systems are constrained by limited observation perspectives, leading to challenges in measuring two-dimensional (2D) TSCV. To address the 2D TSCV inversion issue, a bistatic SAR that utilizes Geosynchronous SAR (GEO SAR) as the radiation source and Low Earth Orbit SARs (LEO SARs) as passive receivers is proposed. This bistatic SAR configuration outperforms existing bistatic SAR ATI systems in terms of cost efficiency as well as inter-satellite synchronization simplicity. For the GEO-LEO SAR system, we develop new interferometric signal models and processing methods to enable one-dimensional (1D) and 2D TSCV estimation. Secondly, to enhance measurement performance, a system configuration optimization algorithm that considered both imaging and ATI performances is applied. Optimization results demonstrate that a larger GEO SAR elevation angle improves estimation accuracy. Finally, based on the currently operating L-band GEO SAR system LSAR4-01, optimization and full-link ATI simulations are conducted, verifying the effectiveness of the optimization algorithm and highlighting the potential design of LEO SAR orbits for high accuracy ATI observations. Simulations achieve an imaging resolution of <100 m and a 2D TSCV inversion bias of 0.1 m/s. The influence of errors on simulation accuracy is also discussed in depth.

Space-surface bistatic synthetic aperture radar (SS-BSAR) system consists of a spaceborne synthetic aperture radar (SAR) transmitter and ground-based receivers. This system has the advantage of multiple angles for observation, which can improve imaging capability and deformation measurement dimensions by differential interferometric SAR (D-InSAR). With the increasing number of spaceborne SARs and various corresponding working modes, processing SS-BSAR data with public ephemeris (generally inaccurate) and unknown signal parameters is significant for the full use of illuminators. This noncooperative status will result in interferometric phase errors in repeat- pass SS bistatic InSAR (SS-BInSAR), leading to deterioration of deformation retrieval accuracy. To address this, this article focuses on the method and processing of noncooperative repeat-pass SS-BInSAR. First, a repeat-pass SS-BSAR interferometric model was established. Based on this, the impacts of time synchronization errors and orbit errors on repeat-pass interferometric phase are modeled. Furthermore, an end-to-end compensation approach is proposed for accurate interferometric processing. This approach includes accurate estimation for signal parameters, interferometric phase error elimination, and digital elevation models (DEMs) fusion recovered from multiple observations. Finally, a repeat-pass SS-BInSAR experiment utilizing the Chinese Lutan-1 as the transmitter is carried out to verify our methods. The results show a centimeter-level accuracy of deformation measurement by a single InSAR pair, indicating a great potential of SS-BInSAR in deformation retrieval.

Polarimetric Synthetic Aperture Radar (PolSAR) holds significant utility in classification through exploiting polarization information. However, many sensors are dual-pol, and their capabilities of classification are limited by the absence of polarization information. This paper focuses on dual-pol classification, proposing a two-step dual-band classification strategy for the Wishart classifier. The first step involves leveraging two bands to category the strong scattering pixels. And the second step entails optimizing the classification of weak scattering pixels using interferometry coherence. We use practical data from the C and L bands, employing Support Vector Machine (SVM) as a comparative experiment to validate the effectiveness of our strategy. The total accuracy and Kappa of the Wishart classifier finally increase by 3.92% and 0.05, respectively, indicating the effectiveness of our strategy. In addition, the second step also independently improves the performance of SVM, suggesting its versatility. This study can offer a novel perspective for dual-pol and multi-band classification.

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.

Spaceborne synthetic aperture radar differential interferometry (D-InSAR) can measure large-scale surface deformation. When the deformation is negligible, the interferometric phase can be used to estimate the differential tropospheric delay (DTD). Nevertheless, a single satellite can only obtain the integrated DTD along the line-of-sight direction. Spaceborne simultaneous multiangle synthetic aperture radar (SSMA-SAR) observes a scene by multiangle spaceborne SAR satellites at the same time. It can measure integrated DTD from different viewing angles by D-InSAR, which will help realize the tomographic inversion of differential tropospheric refractivity (DTR) to obtain its spatial 3-D distribution. However, the inversion performance is sensitive to phase errors in interferograms, troposphere conditions, and system configurations. To address these issues, this article establishes an SSMA-SAR tropospheric tomography model, analyzes the error sources in the inversion, and proposes an optimization configuration design method for SSMA-SAR tropospheric tomography based on the nondominated sorting genetic algorithm II (NSGA-II). The simulation results show that SSMA-SAR has good potential to achieve high-accuracy 3-D DTR, with more than 70% improvement with the optimized configuration. Within the north latitude range of 0°–55°, the system can obtain high-precision 3-D DTR measurements and achieves submillimeter integrated tropospheric delay accuracy in the zenith direction with subkilometer resolution.

Space-surface bistatic synthetic aperture radar (SS-BSAR) system has the advantage of diverse observation angles due to flexible receiving configuration, thus it plays an important role in SAR multi-angle imaging and three-dimensional deformation retrieval. In this paper, based on the LuTan-1 SAR launched in 2022, we present a SS-BSAR interferometry experiment. First, the SS-BSAR system implementation and some experiment parameters are shown. Second, we introduce the SS-BSAR synchronization scheme and its imaging methods applied in this experiment, and establish a SS-BSAR dual-antenna interferometry model. Finally, we present the imaging and interferometry results of our SS-BSAR system and analyze the experimental result. This is the first result of LuTan-1 SS-BSAR interferometry, which demonstrates the ability for remote sensing observation applications based on the SS-BSAR system.

Geosynchronous Synthetic Aperture Radar(GEO SAR) has become a hot spot because of short revisit time and wide coverage. Compared with single satellite, distributed GEO SAR provides rich observation angles which makes high-accuracy three-dimensional(3D) deformation retrieval possible. However, there are significant differences in the resolution and texture of Interferometric Synthetic Aperture Radar(InSAR) image at different observation angles, which will lead to reduced accuracy of 3D deformation retrieval. In terms of problems above, Pseudo-CycleGAN is proposed in this paper based on phase unwrapping Deep Neural Network(DNN) and CycleGan. It can improve the accuracy of 3D deformation retrieval through texture assimilation of interferogram with high phase accuracy.

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.

Simultaneous multi-angle spaceborne synthetic aperture radar (SAR) can provide spatially diverse SAR images of the same scene without time lags. Through Differential SAR interferometry (D-InSAR), the system can extract accurate multi-dimensional deformations from the mixing differential tropospheric delay (DTD), which generally distorts deformation signals in single interferograms. This paper focuses on the multi-dimensional deformation estimation by simultaneous multi-angle spaceborne D-InSAR. A multi-channel Wiener filter (MWF)-based multi-dimensional deformation and DTD joint estimation method is proposed in this paper. The method can achieve optimal estimation accuracy and reduce the loss of scene details. It was first validated by the simulations based on the system parameters of the future European Space Agency (ESA) Harmony mission. Additionally, the method was confirmed through the utilization of the real TanDEM-X bidirectional (BiDi) SAR data acquired over two scenes in California, USA. We analyzed the performance of the method in the presence of multiple error sources and investigated the impact of different observation geometries on estimation performance. Finally, the results demonstrate the potential of simultaneous multi-angle spaceborne D-InSAR in multi-dimensional deformation measurement. The proposed method is effective in achieving good estimation accuracy and spatial resolution preservation.

With the advantages of high spatial resolution, short repeat-pass cycle, and large observation area in Ka-band distributed geosynchronous (GEO) synthetic aperture radar (SAR) systems, its interferometry (InSAR) measurement can fast retrieve high-resolution and high-accuracy digital elevation model (DEM). However, compared to low-orbit systems, it is more difficult and costly for distributed GEO SAR systems to perform tight formation flying at such a high orbit altitude, and, therefore, atmospheric effects, which bring non-stationary and non-Gaussian phase errors, should be taken into account in its repeat-pass InSAR. To address the problems above, a spatial-temporal joint particle filter-based method (ST-PF) for DEM generation by distributed GEO InSAR is proposed in the paper. The proposed ST-PF method is validated under several stationary and non-stationary atmospheric conditions through simulation experiments, and high-accuracy and high-resolution DEMs are obtained. Moreover, the ST-PF method can withstand severe non-linearity in interferometric phases, which result from a relatively small ambiguity height in Ka-band. It is also tested that the mean square error (MSE) of the retrieved DEM is consistent with the posterior Cramer-Rao bound (pCRB) of the estimation problem, showing the validity and the accuracy of the proposed ST-PF method. With various processing parameters, errors, and scene types tested, the method shows good robustness in different conditions.

S-band spaceborne synthetic aperture radar (SAR) systems were relatively rare since most launched missions worked in L-, C- or X-bands. An S-band spaceborne SAR should have a better interferometric coherence and penetration capability compared to a C-band spaceborne SAR, which is not fully known. In this paper, we analyze the interferometric coherence based on NovaSAR-1 data in a case in Peth, Australia. Typical regions of interest (ROIs) were selected and the performance was compared to C-band Sentinel-1 data. The results show that S-band spaceborne SARs have better penetration towards shallow and sparse vegetation regions and higher temporal coherence than C-band systems.

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.

Fast observations of rapid surface large-changes are demanded in disaster evaluations and scientific studies. Digital elevation model (DEM) differencing before and after the events is an effective way to retrieve the changes. Owing to a short repeat cycle, geosynchronous synthetic aperture radar (GEO SAR) systems can quickly obtain repeat-pass data and generate postevent DEMs by interferometry. However, interferometric baselines under its quick revisit cases are short, resulting in generating low-accuracy postevent DEMs. Moreover, surface large-changes can bring height ambiguity problems under the single-baseline interferometric processing. In this letter, we address the problem through a multibaseline (MB) processing. Since GEO SAR MB data can derive from the repeat-pass interferometric data of different subapertures and revisits, a subaperture-decomposition-based temporal and spatial MB method is proposed. The simulation results verify the effectiveness of the proposed method, where the quickly generated postevent DEM can help to realize the rapid large-elevation change observations.

Multiple CubeSat altimeters can work independently or corporately to form altimeter constellations. Different configurations of the constellations can acquire distinguished advantages: improved spatial/temporal sampling and high cross-track resolution, which will be helpful for observations of oceanic small-scale structures and weather forecasting. Compared to single conventional altimeters, CubeSat altimeter constellations may achieve better performances with lower costs. To fully understand these systems, this article focuses on the performance analysis and methodology for CubeSat altimeter constellations. Besides the typical analyses of the resolution, revisit, and absolute sea surface height (SSH) accuracy, the performance analysis was conducted by considering the characteristics of multiple measurements provided by CubeSat altimeter constellations. Local and global spatial sampling performances are investigated for various constellations and compared by sampling density and swath size. Moreover, relative SSH accuracy is introduced and evaluated based on the spatial structure functions of errors to effectively evaluate the measurement performance. Related system requirements on power, delta-v, etc., to achieve the performance are also discussed, which ensures that the analysis fits the boundary conditions of implementation. Finally, different concepts of the CubeSat altimeter constellations are compared, where their limitations and possible solutions are also discussed.

Tropospheric delays are one of the main contributors to the interferometric phase in synthetic aperture radar (SAR) interferometry. When the phase contributions from surface deformation, topography, and ionospheric delays are negligible or known, the interferogram can be used to estimate the differential tropospheric delay (DTD), which can help to improve tropospheric delay predictions from weather models andin situmeasurements. In conventional repeat-pass interferometric SAR (InSAR), however, the estimation of the DTD can still be significantly hindered by baseline errors. In addition, a single interferogram provides only relative DTDs, as the delays can be retrieved up to an unknown offset. To address such issues, this article presents a method for the estimation of DTDs on large scales by using repeat-pass simultaneous multi-angle SAR systems. Complementary simultaneous observations of the correlated troposphere from multiple angles are used to retrieve estimates of the absolute DTD and, at the same time, to mitigate the effect of baseline knowledge errors. Finally, a performance evaluation is presented for the Harmony Earth Explorer 10 candidate mission. A centimeter-level absolute accuracy and a submillimeter-level relative accuracy of the DTD estimation are achieved under the multistatic Harmony case when at least one companion satellite has an inter-satellite distance longer than 300 km to provide enough sensitivity.

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.

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.

The work investigates staggered and random PRF (Pulse Repetition Frequency) strategies for a close formation of small Synthetic Aperture Radar (SAR) satellites operating in a multistatic configuration. The satellites are positioned within a fraction of the along-track critical baseline, hence allowing for the application of Displaced Phase Center image formation approaches. The performance of regular and random pulse sampling schemes is in particular assessed for a single-input multiple-output (SIMO) S-Band constellation, whose feasibility is further analyzed in relation to the number of satellites and their antenna size.

This paper debates the performance of the HARMONY (STEREOID) ESA EE-10 candidate mission in measuring the two-dimensional (2D) terrain deformation. Thanks to its Stereo configuration, where the two passive spacecrafts span a large along-track baseline centered on the Sentinel-1 satellite, a large observation angle diversity in azimuth can be achieved. This theoretically leads to promising deformation performance in the north-south direction component, which will play an extremely important role for the surface displacement analysis in the future.

Ambiguities in short-baseline ATI interferometry need to be treated not as noise that lowers the coherence, but as a source of bias. A mathematical formulation of the interferometric ambiguity model is given, and an approach to correct ambiguities is proposed and illustrated with simulation results.

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.

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.

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.

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.

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).