In azimuth multichannel synthetic aperture radar (AMC-SAR) systems, channel phase errors can induce ghost targets. The estimation of signal parameters by the rotational invariance technique (ESPRIT) assumes that the principal signal subspace corresponds to the steering vector at zero Doppler frequency. However, this assumption is frequently violated in practice, resulting in performance degradation. To address this issue, a robust ESPRIT (RESPRIT) algorithm is proposed, which integrates initial ESPRIT-based phase error estimation with residual phase error correction through image contrast maximization. Both simulation and real airborne data experiments demonstrate the effectiveness and robustness of the proposed method.

This letter aims to assess the applicability of synchronous wind and wave observations using Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) during tropical cyclones (TCs) in the North West Pacific (NWP). From 2023 to 2024, 28 GF-3 images featuring visible TC eyes were available for this study. The well-known machine learning method, denoted as eXtreme Gradient Boosting (XGBoost), is implemented to retrieve sea surface wind and significant wave height (SWH) from dual-polarized GF-3 images acquired in Wide ScanSAR (WSC) mode. The inversion is supported by 20 GF-3 images collocated with re-constructed winds based on European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) data and Japan Meteorological Agency (JMA) best-track data along with SWH simulations from the WAVEWATCH-III (WW3). Wind speeds inverted from additional 8 GF-3 images are validated against Soil Moisture Active Passive (SMAP) measurements, yielding a root mean squared error (RMSE) of 3.31 m/s, a correlation (COR) of 0.80, and a scatter index (SI) of 0.29. The SWHs inverted from 8 images are compared with Haiyang-2 (HY-2) altimeter data and WW3 simulations, showing an RMSE of 0.74 m, a COR of 0.83, and an SI of 0.25. Our study demonstrates the robustness of GF-3 SAR in monitoring extreme sea states.

Ocean velocities are essential for understanding how the ocean influences and responds to climate dynamics, making their accurate reconstruction crucial for both climate modeling and predictions. However, reconstructing interior ocean velocities remains a significant challenge due to the sparse distribution of velocity observations and the ocean’s complex dynamics. In this study, we introduce an efficient methodology for reconstructing interior ocean velocities by combining sea surface satellite data—including sea surface height (SSH), temperature, wind, and current—with Argo velocity observations, using the dynamic mode decomposition (DMD) technique. DMD offers the advantage of reducing the dimensionality of interior velocity fields, helping to address the limitations caused by sparse observations. The reconstructed velocity for the Southern Pacific Ocean (SPO) was validated against Argo and acoustic Doppler current profiler (ADCP) velocities, showing a strong correlation than GLORYS12V1 velocities. In particular, the reconstructed velocities have a mean correlation coefficient of 0.78 for the zonal component and 0.74 for the meridional component above 1000 m. Additionally, the reconstructed flow field exhibits a coherent pattern that closely aligns with the eddies observed in SSH. This research significantly contributes to the Global Ocean Monitoring and Observing Program by enhancing both the accuracy and resolution of ocean velocity measurements.

Sea-surface velocity (SSV) and sea-surface height (SSH) are among the most crucial parameters in an oceanic dynamic environment. Using spaceborne interferometric synthetic aperture radar (InSAR) to obtain high-resolution, large-scale survey area, high observation frequency, and high-precision ocean dynamic parameters is advantageous. However, the hybrid baseline InSAR phase data include components from both SSV and SSH, making it challenging to distinguish them and affecting inversion accuracy without additional information. The PIE-1 constellation, the world’s first four-satellite distributed InSAR system, was successfully launched into orbit on March 30, 2023. This constellation can form multiple interferometric pairs by combining two satellites, enabling the potential to extract SSV and SSH from hybrid phase signals simultaneously. In this study, two pioneering and fundamental works were conducted: 1) an integrated current-height inversion model was developed based on the multichannel likelihood (ML) function, with error analysis performed according to PIE-1 parameters and 2) the detailed data processing scheme for the first simultaneous inversion of SSV and SSH based on spaceborne InSAR data was presented. The inversion results were compared to Doppler centroid analysis (DCA)-derived Doppler velocity and reference data from the ESA’s Copernicus Marine Service (CMEMS). Both qualitative and quantitative comparisons validated the effectiveness and accuracy of the inversion results. This approach represents an effective technique for simultaneous inversion of SSV and SSH in future multibaseline spaceborne/airborne InSAR systems.

The hybrid along-track (AT)/cross-track (XT) synthetic aperture radar interferometry (InSAR) system enables the measurement of radial sea surface velocity, where solving it through phase unwrapping (PU) is intrinsically ill-posed due to the coupling of velocity-induced and height-induced phase components. Conventional hardware-based methods impose stringent and costly system requirements, while auxiliary-data-dependent approaches are error susceptible. Under this condition, we innovatively propose an along-track two-stage programming approach (AT-TSPA) multibaseline (MB) PU method within the three-pass differential InSAR (DInSAR) framework. AT-TSPA addresses the ill-posedness by making use of the normal baseline diversity, thus enabling accurate and robust retrieval of sea surface velocity without relying on hardware upgrades or external auxiliary data. Both theoretical analysis and experimental results demonstrate that AT-TSPA is effective and practical for accurately measuring sea surface velocity under complex oceanic conditions. AT-TSPA is also applicable to other fields involving coupled velocity and height phase components, such as sea ice velocity estimation. Furthermore, AT-TSPA extends the theory of TSPA-based MB InSAR from terrestrial to oceanic domains, thereby advancing the applicability and practicability of well-posed InSAR techniques.

For the traditional 1-D nadir altimetry measurements, the spatial resolution of the sea surface height (SSH) measurements is restricted. However, the new wide-swath altimeters like Surface Water and Ocean Topography (SWOT) are anticipated to enhance spatial resolution and thereby improve the accuracy of derived marine gravity anomalies, particularly in high-frequency signals. The study investigates the efficacy of L2 KaRIn Beta data of SWOT in recovering high-frequency marine gravity signals. CryoSat-2 data was used for comparison. The first-order difference of shipborne gravity data along ship tracks was used as the reference to investigate the high-frequency accuracy of gravity derived from SWOT and CryoSat-2 at different point spacings, respectively. The research results show that compared to CryoSat-2 satellite data, when the point spacing is less than 2 km or greater than 6 km, the accuracy improvement is relatively small, less than 3.0%. However, within the 2–4 km range, the accuracy improvement ranges from 6.8% to 7.9%. When the point spacing is between 4 and 6 km, the accuracy improvement ranges from 5.0% to 6.7%. Therefore, when the point spacing is greater than 2 km and less than 6 km, the accuracy improvement is obvious. The results show that SWOT wide-range altimeter satellite has obviously improved the accuracy of traditional altimeter gravity inversion in high frequency (especially in the range of 2–6 km).

Predicting the 3-D ocean temperature field is a significant task that helps to understand global climate change and the state of ocean motion. Lots of numerical and data-driven models are used to predict ocean temperatures. However, these methods are restricted to the time-sequence prediction of discrete points or rely on convolutional layers to inefficiently capture local spatial dependencies for spatio-temporal prediction. In this letter, we propose a deep learning model named SA-PredRNN that combines attention mechanisms and predictive recurrent neural networks to capture global positional correlations and spatio-temporal features. Global gridded Argo temperature data with Barnes objective analysis (BOA-ARGO) are used to predict the future 3-D ocean temperature. The average RMSEs of the proposed model are promoted by at most 11% and 10%, which indicates that the SA-PredRNN model has better performance than the other baseline models.

This article proposes a configuration for a multiple input-multiple output (MIMO) along-track interferometry (ATI) synthetic aperture radar (SAR) satellite system designed for measuring ocean surface velocity vectors. The satellite system consists of a main satellite and at least two secondary satellites. The main satellite is equipped with dual antennas for transmitting and receiving echoes, while the secondary satellites are equipped with single antennas for receiving echoes. Based on the configuration, we explore key issues related to MIMO ATI SAR for measuring ocean surface velocity vectors to form a comprehensive solution. The issues include the current measurement principle, methods for assessing velocity vector accuracy, optimal baseline determination, carrier frequency selection, echo separation methods, and sources of phase error. The corresponding methods are derived in this article to address the challenges posed by the geometry of bistatic observations in beam steering and digital beamforming (DBF) processing. In addition, a system parameter design method based on optimizing ocean surface velocity measurement accuracy is proposed, eliminating the need for repeated trade-offs on individual parameters. Finally, the article presents an example of a high-precision wide-swath ocean surface velocity vector measurement system based on the configuration. This system achieves an ocean surface velocity accuracy of 3 cm/s, a swath coverage of 200 km, and a product resolution of$1\times 1$km2 in wind speeds above 3 m/s.

The swath of the conventional spaceborne synthetic aperture radar (SAR) is parallel-to-orbit, making it inefficient to observe long curved terrain, like coastlines, railways, etc.. Imaging long curved terrains with the terrain matching (TM) curved swath is a promising technique for efficient data acquisition. The key feature is the employment of a long curved swath matching with the orientations of the long curved terrains. This paper reports the first demonstration of the spaceborne SAR TM curved imaging with the LJ2-01 satellite. A TM curved swath of 161.7 km is imaged with an azimuth resolution of 0.6 m. The main technical contributions are: First, a new electrical-mechanical-combined beam control method is proposed to achieve uniform azimuth resolution; Second, a new non-uniform pulse repetition frequency sequence is used to mitigate the data loss caused by the violent spatial variation of the slant range; Third, a new swath-adaptive sub-aperture time-domain imaging algorithm is proposed for efficient TM curved swath imaging. These innovations contribute to successful data acquisition and imaging of the spaceborne SAR TM curved imaging with the LJ2-01 satellite.

Compared with the broadside imaging mode in multichannel SAR system, high-squint imaging mode in azimuth would be much more flexible to achieve high-resolution and wide-swath (HRWS) imaging and greatly improve the efficiency of application for Earth dynamic monitoring. However, the high-squint angle would result in more complex problem of spectrum folding after down-sampled, which would pose new challenge to the unambiguous signal reconstruction for multichannel SAR system. In this paper, the processing procedure of the unambiguous signal reconstruction for high-squint multichannel SAR is proposed. Then, an onboard experiment based on Chinese GF-3 dual receiving channel (DRC) spaceborne SAR system were successfully carried out to validate the practicality of high-squint imaging mode for multichannel SAR system in azimuth in January 2022. Experimental data validates the effectiveness of the proposed processing procedure.

This letter introduces a reliable three-dimensional reconstruction for buildings using a small data stack in synthetic aperture radar tomography (TomoSAR) based on a revised Reference Network (RN) method. The RN method can realize atmospheric phase error correction by persistent scatterer (PS) phase differencing. However, when dealing with a limited number of images in a small data stack, the inaccurate inversion of relative elevation for local PS arcs can result in error propagation during network integration, leading to compromised tomography results. To address this issue, we have refined the traditional RN method by introducing a new geometric constraint between PSs. In the proposed method, we utilize the relative elevation estimated from PS arc differential signal to accurately determine the spatial distance between PSs. PS arcs that exceed the geometric constraint are eliminated to assist in adjusting the reference network. The effectiveness of this optimization method is demonstrated through TomoSAR experiments conducted on a high-resolution TerraSAR-X SAR data stack in Shenzhen, China. Consequently, the method presented in this letter is highly suitable for processing small data stacks compared to the previous RN method.

High-squint multichannel synthetic aperture radar (SAR) in azimuth can overcome the constraint of minimum antenna area in the traditional single-channel classical SAR system, and thereby it would be much more flexible to achieve high-resolution and wide-swath (HRWS) imaging than broadside SAR. However, for the high-squint multichannel SAR imaging mode, the squint angle causes Doppler centroid varying with the range frequency, which can further result in much more complicated problem of the spectral folding after down-sampled with low PRF. Meanwhile, the Doppler centroid frequency is indispensable for the estimation of phase bias and unambiguous signal reconstruction among received channels. In this paper, the impact of high-squint angle on the aliased Doppler spectrum and Doppler centroid is analyzed in detail. Then, a novel approach to phase bias estimation as well as the underlying signal processing in the case of high-squint multichannel SAR imaging mode in azimuth is proposed. Simulation results validate the effectiveness of the proposed processing flowchart for high-squint multichannel SAR.

Synthetic aperture radar (SAR) sometimes needs to be imaged with small time bandwidth product (TBP) for fast observation. As a real-time image algorithm, back-projection algorithm (BPA) is efficient for fast observation. However, compared with the frequency-domain algorithm, the influence of small TBP (STBP) on BPA has not been fully discussed. Thus, the performance of the original BPA on STBP SAR imaging is analyzed and an improved BPA for STBP imaging is proposed in this letter. Theoretical analysis indicates that STBP will cause the point spread function (PSF) distortion and azimuth spectrum ambiguity to the BPA imaging result. To deal with those problems, two parameters of the original BPA are modified in the improved BPA. The accumulating azimuth angle (AAA) is optimized first to calibrate the PSF distortion. Then, based on the azimuth-ambiguity-to-signal ratio (AASR), a BPA meshing criterion related to TBP is proposed to diminish azimuth spectrum ambiguity. The simulation and real data results demonstrate that the improved BPA is effective on undistorted PSF recovering and unambiguity spectrum maintaining, even when the TBP is down to 4.

Traditional synthetic aperture radar (SAR) ground moving target detection (GMTD) technology mostly relies on multichannel data, which requires higher complexity of the radar system. This letter proposes a subband interferometry (SBI)-based algorithm for single-channel SAR GMTD. First, the echo signal is divided into high and low bands containing overlapping parts before the imaging process, then the subbands are imaged separately, and the SBI algorithm is used to achieve the interferometric phase of the subbands. It can be observed that the moving target has the characteristics of phase reversal in the interferometric phase image, and graphics operations such as median filtering or morphological processing can be used to detect the moving target. In this letter, the relationship between the subbands coincidence, the correlation coefficient (CC) of SBI, and the phase of moving target after SBI processing are derived. The real airborne SAR data verify the feasibility and effectiveness of the algorithm.

The inherent magnitude and phase errors between channels in the multichannel synthetic aperture radar/ground moving target indication (SAR/GMTI) system will affect the clutter suppression and moving target detection performance of the system. To solve this problem, this letter proposes an adaptive channel balancing algorithm based on the 2-D Gaussian kernel function. First, the iterative reweighted least squares (IRLS) algorithm is used to estimate and compensate the inherent linear phase error that varies with the Doppler frequency in the range-Doppler domain. Then the Gaussian statistical analysis of the residual phase errors is performed in the image domain after the azimuth processing. According to the coupling characteristics of the range and azimuth of the SAR image, the correlation coefficient and the standard deviations of the phase errors in the range and azimuth directions are computed, and a 2-D Gaussian kernel function is constructed to estimate and compensate the residual phase errors. Finally, a mean filter is used to compensate the 2-D magnitude errors. This algorithm can eliminate the magnitude and phase errors and improve the coherence between different channels. The real airborne SAR data demonstrate the effectiveness of the proposed algorithm.

There is an inherent tradeoff between range swath width and azimuth resolution in the traditional spaceborne single-channel synthetic aperture radar (SAR). The range swath can be extended with reduced pulse repetition frequency (PRF), but this leads to the decrease of the azimuth resolution of SAR images. It is difficult for traditional SAR imaging methods that follow the Nyquist sampling theorem to obtain high-resolution and wide-swath imaging results simultaneously. Considering the influence of transmit pulse occlusion and the nadir echo on the selection of PRF in spaceborne SAR, a random PRF variation pulse transmission pattern that is constrained by the coverage diagram is designed in this article for wide-swath imaging. The minimum interval length between adjacent transmitted pulses is set to be larger than the Nyquist sampling interval, thus offering the capability to widen the range swath of spaceborne SAR imaging results. Then, the fast iterative shrinkage thresholding-like algorithm (FISTA-like) is performed on the nonuniformly sampled data to achieve sparse reconstruction of the high-resolution imaging results. Compared with the existing SAR imaging methods, the proposed method realizes high-resolution and wide-swath imaging for spaceborne single-channel SAR with lower hardware implementation difficulty. Simulations and experiments on measured spaceborne SAR data demonstrate the effectiveness of the proposed method.

In the inshore region, the azimuth ambiguity of land clutter is the main factor, which reduces the clutter suppression performance for the multichannel synthetic aperture radar (SAR) system. To address this problem, we propose a multifeature tensor discriminant alternating optimization and affine invariant Riemannian (TDAO-AIR) classification approach. First, the correlation coefficient between channels is obtained to characterize the normalized SAR image amplitude. Next, multilook interferometric processing is performed to reduce the effect of Speckle noise and acquire multilook interferometric complex images. At the same time, we extract the correlation coefficient gradient and the multilook interferometric phase gradient in different spatial directions to characterize the spatial structure. Next, we construct a feature tensor (FT), including correlation coefficient between channels, multilook interferometric phase, and spatial structural gradient information for each range-Doppler unit (RDU). The TDAO algorithm is proposed to extract the core FT (CFT) by optimizing the objective function and getting the projection matrix. Then, the CFT is unfolded along the feature dimension, and the feature covariance matrix (FCM) is constructed for each RDU. With the AIR distance measure, the inshore region is divided into different clutter regions. Finally, in the azimuth ambiguous clutter region, independent and identically distributed (IID) training samples are selected to estimate the clutter covariance matrix (CCM), and robust adaptive clutter suppression is performed in the SAR image domain. The experiments on simulated and real measured data by TerraSAR-X demonstrate that the proposed method can accurately identify and suppress the azimuth ambiguity in the inshore region.

In multi-channel synthetic aperture radar-ground moving target indication (SAR-GMTI), most radial velocity estimation methods are based on the phase difference between channels. However, the image coregistration and channel phase errors will have a severe impact on the phase difference between channels. Moreover, it deteriorates the performance of the moving target radial velocity estimation. To solve this problem, a robust radial velocity estimation method is proposed using a frequency diverse array-SAR (FDA-SAR) in this letter. By introducing the step frequency, the interferometric phase among channels is a linear function of the Doppler frequency. The radial velocity of moving targets is embedded in the first-order term of the linear function. Meanwhile, the first-order term does not include channel phase error terms. Therefore, the accurate velocity of moving targets is estimated by the first-order coefficient which is solved by the least-squares fitting method. Afterward, according to the analysis and derivation, the proposed method is robust on the condition of image coregistration error. At last, simulations and data analysis illustrate the effectiveness of the proposed method.

This article proposes a novel fusing filter algorithm based on a surface variation anisotropic diffusion (SVAD) filter and a ratio image filter to achieve good speckle reduction and edge preservation. The proposed algorithm can be divided into three steps. First, the proposed SVAD filter effectively calculates the diffusion coefficient of each pixel to obtain filtering results on different scales. Second, the proposed ratio image filter obtains a new denoising result that can effectively recover some details lost with the SVAD filter. Then, the two filtering results are fused to obtain the final despeckling result. Furthermore, the effects of the weighting coefficients of the fusion processing and the number of iterations of the ratio image filter on the final filtering results are analyzed. The proposed algorithm is effectively evaluated by conducting some experiments on the added noise image and real synthetic aperture radar (SAR) images. The experimental results confirm that the proposed method can not only significantly reduce speckle but also effectively preserve the edge information of images.

Geosynchronous synthetic aperture radar (GEO SAR)-related technologies are being mature, and the first GEO SAR satellite is expected to launch in the next ten years. Under this circumstance, some equivalent experiments should be conducted at the current stage to validate some key characteristics or parameters, which could significantly increase the success possibility of the GEO SAR project. To validate the feasibility of GEO SAR imaging with long integration time, which is the most important and fundamental characteristic of GEO SAR, the first helicopter platform-based equivalent GEO SAR experiment with long integration time was performed in Qianxi County of China on May 22, 2019. The integration time of it is 80 s, which is carefully designed to maintain the consistence between itself and the integration time of the GEO SAR. Furthermore, the azimuth signal-to-noise ratio gain with long synthetic aperture time is analyzed. Moreover, the 2-D space-variant motion error introduced by the complex helicopter trajectory and the performances of different imaging algorithms are analyzed to choose the proper imaging algorithms; to overcome the flaws and unclarities of existing algorithms, some improvements are proposed to obtain the well-focused SAR image. What is more, the equivalence of this experiment is also analyzed detailedly to demonstrate the effectiveness of this experiment. At last, the imaging result with synthetic aperture time of 100 s and the comparison between itself and the optic photograph validate the success of this equivalent experiment and the feasibility of GEO SAR imaging with long integration time.

Geosynchronous orbit synthetic aperture radar (GEO SAR) plays an important role in wide-area surveillance and the continuous coverage of areas containing targets of interest. However, the relative position errors of large aperture arrays will distort the antenna pattern, which significantly degrades the target detection performance in a GEO SAR system. To address this issue, most of the conventional calibration methods are focused on the independent errors, without consideration of the parametric error model, which may increase the position estimation errors. To solve this problem, an efficient calibration algorithm for position errors in a GEO SAR is proposed in this letter. For the large antenna arrays, the parametric error model is first established. Then, the calibration method is performed to estimate the parameters of the position error model. Based on this, the accurate position errors can be obtained by the least-squares algorithm. Compared with the conventional methods, the target detection performance of a GEO SAR system can be significantly improved after the precise array position error compensation by the proposed algorithm. Moreover, the proposed algorithm transforms the estimation from 3-D positions to the finite parameters of the error model, which can considerably decrease the computational complexity and obtain a more accurate estimation of the position errors simultaneously. Several simulated results are presented to validate the proposed algorithm for the position error correction in a GEO SAR system.

This letter presents a new approach of digital elevation model generation using circular synthetic aperture radar. The approach is composed of two steps, target separation and height estimation. In step 1, subaperture images are reshaped into vectors and stacked to build a composite matrix. The composite matrix is decomposed into a low-rank matrix and a sparse matrix based on “Semi-Soft Go Decomposition” algorithm. The targets, whose height is equal to the reference imaging height plane, are contained in the low-rank matrix, whereas the targets with other heights are contained in the sparse matrix. In step 2, the sparse matrix is further decomposed into several shifted low-rank matrices, each of which corresponds to the targets sharing one height, based on “Shifted Subspaces Tracking” algorithm. The height of the targets contained in each low-rank matrix is estimated from the shifts of the matrix's subspaces. The effectiveness of the proposed approach is investigated by the Gotcha public release data set.

The successful launch of the GF-4 satellite has greatly enhanced wide-swath and high-frequency imaging capabilities, both of which are urgently needed in the fields of ship detection and monitoring. In this letter, the applicability of GF-4 satellite data for ship recognition and maritime traffic surveillance was investigated. It is concluded that the movement of ships, the velocity and direction, for instance, can be detected by using either a single GF-4 image that has short time lags between bands, or time-series GF-4 images that have a short interval between their acquisition times, implying that GF-4 satellite images have the potentiality for the monitoring of moving ships.

A method of motion status estimation of airborne synthetic aperture radar (SAR) platform in short subapertures via parametric sparse representation is proposed for high-resolution SAR image autofocusing. The SAR echo is formulated as a jointly sparse signal through a parametric dictionary matrix, which converts the problem of SAR motion status estimation into a problem of dynamic representation of jointly sparse signals. A full synthetic aperture is decomposed into several subapertures to estimate the dynamic motion parameters of a platform, and SAR motion compensation is achieved by refining the estimation of the equivalent platform motion parameters, i.e., the azimuth velocity and the radial acceleration of the radar platform, at each subaperture in an iterative fashion. Experimental results based on both simulated and real data demonstrate that: 1) the proposed algorithm outperforms the map-drift algorithm and the phase gradient autofocus algorithm in terms of the imaging quality and 2) compared to the iterative minimum-entropy autofocus, the proposed algorithm produces the comparative imaging quality with less computational complexity in complex motion environment.

The Goldstein filter is the commonly used filter for interferometric synthetic aperture radar (InSAR) phase noise reduction, and its performance is determined by three parameters: the power spectrum filtering parameter α, the patch size of the InSAR phase, and the smoothing window on the power spectrum. However, the noise level of the different regions in an interferogram changes greatly due to the terrain topography. The InSAR phase will be overfiltered in high-coherence regions and be underfiltered with high noise level in low-coherence regions if the unchangeable processing parameters are used. In this paper, we propose an improved InSAR phase filter in frequency domain. First, we use different window sizes to suppress the phase noise, and the window size is determined by the coherence of the central processing pixel. A pixel with higher coherence uses a smaller filtering window size and vice versa. Second, the samples in the filtering window are not independent and identically distributed samples for the terrain topography. To deal with this problem, we use fringe frequency compensation to eliminate the effect of the terrain topography. Third, combining with a newly defined filtering parameter α, we execute the phase filtering procedure pixel by pixel, which is different from the Goldstein filters. Furthermore, we use the Fourier transformation of a small window (generally 3 × 3) to deal with the power spectrum of the InSAR phase patches. Compared with other improved Goldstein filters and spatial-based filters, the effectiveness of the newly improved InSAR phase noise reduction method in frequency domain is investigated by both simulated and real data sets.

For satellites in geosynchronous orbit (GEO) using synthetic aperture radar (SAR), the direction of the satellite velocity vector in the Earth-centered, Earth-fixed (ECEF) coordinate system varies widely in one orbital period. When the velocity vector is approximately parallel to the range direction at equatorial latitudes, the 2-D sidelobes of the generalized ambiguity function (GAF) on the ground are non-orthogonal, which results in a significant deterioration in the ground resolution. To improve the ground resolution, this study investigates an optimal resolution steering (ORS) method for GEO SAR. The ORS method differs from the total zero Doppler steering (TZDS) method, which attempts to minimize the Doppler centroid. In the ORS method, a beam pointing direction is derived based on the constraints of optimal ground resolution. Then, a 2-D roll-pitch strategy is derived to obtain small steering angles, and the beam is steered to the optimal beam pointing direction. Finally, the ORS method is verified with simulations.