Radio Frequency Interference (RFI) would lead to degradation of image quality for synthetic aperture radar (SAR) systems, resulting in a waste of observation resources. RFI source localization provides crucial prior information for RFI mitigation and spectrum management, and traditional RFI localization methods for multichannel SAR system suffer from localization ambiguity. This paper derives the RFI model for dual-channel SAR and investigates the mechanisms underlying localization ambiguity. A localization framework is proposed based on the varying characteristics of RFI with platform motion and the slant range difference model between dual-channels. This approach achieves localization by implementing mutual constraints among alternative optimization models. Moreover, an RFI filtering scheme is developed to facilitate the mutual cancellation of RFI between the two channels. The performance of the proposed method is validated using simulated RFI and real-measured RFI scenarios of Chinese Lutan-1 mission. The results indicate that the proposed method effectively mitigates the localization ambiguity, demonstrating high-precision localization performance. Moreover, it can reduce echo amplitude distortion and preserve degrees of freedom while removing RFI effectively.

LuTan-1 (LT-1) is the first civil L-band synthetic aperture radar (SAR) satellite constellation and comprises two identical satellites. LT-1 is designed to fulfill two main requirements, one of which the main tasks is to provide digital surface model (DSM) products covering the areas that are not available using optical satellites. The second task is to provide deformation products to support the geohazard monitoring task. For the first task, LT-1 provides a novel noninterrupted imaging technology to facilitate phase synchronization. For the second task, the orbit maintenance is implemented using a newly proposed in-plane offset semimajor axis and out-of-plane control triggered strategy. Besides, we provide the system performance analysis for the two main tasks. Finally, the first results are produced with root-mean-square-error (RMSE) of the DSM less than 0.7 m in the flat region and 6.7 m in the mountainous region. The RMSE of the first deformation products is less than 2.7 mm in the test region. The results indicate a favorable potential for terrain mapping and deformation monitoring applications.

Due to existence of the long along-track baseline (ATB) among the different satellites in a distributed space-based radar (DSBR) system, a large number of grating lobes appear in radar returns, causing the non-continuous detection phenomenon of an air moving target (AMT). To solve this problem, in this paper, a novel approach for ATB distribution design in a DSBR system is proposed. In the proposed algorithm, to reduce the amount of spatial ambiguity points located at the main-lobe region and achieve the best air moving target indication (AMTI), the maximum target detectability ratio (TDR) in the main-lobe region is chosen to be the criteria for the optimal ATB design. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBR system.

Mosaic mode synthetic aperture radar (SAR) combines the spotlight or sliding spotlight mode with a ScanSAR mode to accomplish the high azimuth resolution and large imaging swath SAR imaging. This paper studies the problem of the frequency modulated continuous wave (FMCW) SAR imaging with a Mosaic mode, where the imaging algorithms for pulse radars are not suitable for a FMCW SAR. To deal with this issue, this paper proposes a FMCW Mosaic mode SAR imaging algorithm based on modified polar formation algorithm (PFA), which considers the radar motion during the signal transmission and performs the SAR imaging operation for spotlight mode in each Mosaic unit. The simulation results of point targets verify the effectiveness and feasibility of the proposed method, which may provide a valuable reference for the application of Mosaic system to miniaturized platforms.

Compared with a monostatic inverse synthetic aperture radar (ISAR) imaging system, a bistatic ISAR (Bi-ISAR) system offers more comprehensive target information, along with higher system security and resistance to interference. However, the inherent geometric configuration of Bi-ISAR will introduce the linear-geometry distortion (LGD), causing the target to appear sheared in shape and impacting subsequent target recognition. To deal with this issue, in this paper, a novel deep learning-based algorithm is proposed to realize the LGD correction. In the proposed algorithm, a neural network called DoubleUNet is employed for the accurate semantic segmentation of target's ISAR image. Then the initial target ISAR image is transformed into two-dimensional point clouds based on the estimated radar parameters. Finally, the linear coupling relationship between azimuth and range dimensions is removed, beneficial to the target ISAR shape restoration and subsequent recognition. Simulation experiments validate the proposed algorithm.

This letter proposes a novel airborne synthetic aperture radar (SAR) motion compensation (MOCO) method based on the optimal weighting Doppler frequency modulation rate estimation. According to the traditional map-drift (MD) algorithm, the Doppler frequency modulation rate corresponding to the range units can be estimated through azimuth sub-aperture signals. The utilization rate for range units and the contributions of the strong scattering units can be improved by using the proposed optimal weighting on the signal amplitude, increasing the estimation accuracy of the Doppler frequency modulation rate. The platform motion error can be effectively estimated through fitting the Doppler frequency modulation rate and each initial slant range based on least square processing. Real-measured high-resolution airborne SAR data validate the effectiveness and feasibility of the proposed algorithm.

Range ambiguity is a common issue for spaceborne synthetic aperture radar (SAR) systems, leading to image quality degradation and subsequent interpretation accuracy. Existing range ambiguity suppression methods mainly rely on raw echo domain processing with specific requirements on prior knowledge. However, most users can only obtain single-look-complex(SLC) products instead of raw echo products. Raw echo obtained from SLC through an inverse focusing process will waste additional time and space resources. This paper proposes a novel scheme to detect and suppress range ambiguity in SLC products to deal with this deficiency. The proposed method designs a detector to extract and suppress the focused range ambiguity based on the characteristic difference between the desired signal and range ambiguity in the fractional transform domain of the SLC image. Experimental results on real measured L-band spaceborne interferometry SAR system verify the detection performance of the proposed method, which is beneficial for subsequent land monitoring applications.

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.

In this letter, a high-resolution SAR imaging algorithm based on a modified omega-K algorithm is proposed. The proposed algorithm first multiplies the reference signal. Then, the azimuth non-stationary phase error (ANSPE) is considered after performing the interpolation process based on the generalized frequency scale transformation. Finally, a well-focused SAR image can be obtained after compensating the ANSPE and correcting the residual spatial variant range migration by utilizing the range segmentation technique. The simulation results of point targets and the real-measured airborne high-resolution SAR data simultaneously verify the effectiveness and feasibility of the proposed algorithm.

Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified$\varepsilon $-insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.

As a wideband radar system, synthetic aperture radar (SAR) may conflict with several electromagnetic systems. These signals may severely interfere with SAR image quality. Numerous previous researches focused on the interference suppression problem, among which semiparametric methods, such as low-rank recovery methods, have been verified to have state-of-the-art (SOTA) performance. However, semiparametric methods are restricted by extremely strong interferences when the signal-to-interference-and-noise ratio (SINR) exceeds the ability upper bound of semiparametric methods. In recent years, multichannel SAR (MC-SAR) systems have been widely used for more applications, where multiple antennas are mounted along the azimuth or in elevation. Adaptive digital beamforming (DBF) is a classic spatial filtering method to focus energy in the expected direction and suppress unexpected interferences. Its performance is determined by the array manifold and the interference-impinging angle. In this article, we propose a novel space-time-combined method that takes advantage of both low-rank recovery methods in the 2-D time domain and the adaptive DBF method in the spatial domain. Specifically, we construct a single optimization problem to unify both kinds of methods. The alternating direction of the multiple multiplier (ADMM) framework is leveraged with a closed-form solution for each step. Multiple experiments are provided to demonstrate the effectiveness of the proposed method.

Due to the existence of the time synchronization error, the correlation coefficient between the main satellite and auxiliary satellite severely declines, thus deteriorating the clutter suppression ability and aerial moving target indication (AMTI) performance in a distributed space-based early warning radar (DSBEWR) system. In this paper, a novel algorithm is proposed to estimate and calibrate the linear time synchronization error. In the proposed algorithm, the rough linear time synchronization error range is firstly estimated according to the Doppler offset between the same spatial channel in the main satellite and the auxiliary satellite. After obtaining the searched rough error range, the linear time synchronization error can be accurately obtained by finding the max correlation coefficient between the main satellite and auxiliary satellite after performing time synchronization error compensation. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBEWR system.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-BiSAR) has the advantages of wide beam coverage, long exposure time, and superior system flexibility. However, it is a challenge for GEO-BiSAR to efficiently acquire SAR images both with high resolution and wide swath due to the severe range and azimuth spatial variances. To deal with this issue, a modified Omega-K method is proposed in this paper. In the proposed algorithm, an equivalent range model associated with GEO-BiSAR configuration is built, where the equivalent parameters of the airborne radar receiver absorb the phase parameters corresponding to the GEO transmitter. Then, the Stolt interpolation is performed to realize the range-azimuth decoupling, achieving the linearization between range and frequency variables. Finally, a well-focused SAR image could be obtained after residual spatial variance error compensation. Simulations are presented to demonstrate the effectiveness of the proposed method.

By transmitting the constant pulse repetition interval (PRI) sequence, the traditional synthetic aperture radar (SAR) system will suffer from the loss of coverage in elevation. Thus a staggered-SAR system is developed in recent years, which employs the continuously PRI-varied sequence, making the blind ranges locate at different regions in different receive instant. However, the nonuniformly sampled signal will cause ambiguities in the SAR imaging result. To deal with this issue, in this paper, a novel signal restoration method based on kernel regression is proposed to resample the nonuniform signal. Real-measured spaceborne SAR data is used to validate the proposed method.

Next-generation SAR imaging system demands wide-swath and high resolution to observe Earth's surface, promoting the development of the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system working in terrain observation by progressive scans (TOPS). However, Doppler ambiguities will occur in this system, causing the imaging performance severely degrading. To address this issue, in this paper, a novel Doppler ambiguous suppression method for an HRWS-TOPS SAR system based on the orthogonal projection subspace is developed. The effectiveness of the proposed method is verified by both the simulated and real SAR data.

For a spaceborne or an airborne synthetic aperture radar (SAR) system, it may be easily interfered with radio frequency interferences (RFIs) due to the increase demand for frequency occupation. The RFIs introduce artifacts in the focused SAR imagery, resulting in high noise floor and erroneous interpretations. Previous works on narrowband RFI mitigation mainly focused on single-channel SAR systems, while with the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multi-channel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). In this paper, we propose a novel interference-mitigation model and analyze the low-rank property of the narrowband RFI for HRWS SAR systems. Then, we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the RFIs by solving the low-rank recovery problems of RFIs. The real airborne SAR data is used to demonstrate the effectiveness of the proposed method.

The spaceborne high-resolution wide-swath synthetic aperture radar (HRWS-SAR) system generally does not meet the optimal SAR imaging configuration, and thus, it is necessary to apply digital beam-forming filtering technology to restore the nonuniformly sampled signal into the uniform grids. However, in practice, because of the influences of temperature, receiving machine, and other error factors, the channel errors may possibly exist, causing the SAR image to be smeared. To address this issue, this letter proposes a novel algorithm based on the hybrid artificial fish school algorithm–glowworm swarm optimization–genetic algorithm (AFSA-GSO-GA) to address the channel imbalance issue. First, coarse HRWS-SAR imaging processing is performed to obtain the positions of the Doppler ambiguity components. Then, according to the designed cost function, the hybrid AFSA-GSO-GA algorithm is used to realize the channel phase error estimation. Finally, a well-focused SAR image can be obtained after channel balance. The effectiveness of the proposed method is validated by both simulated and real SAR data.

The rapid development of the modern electronic counter-countermeasures (ECCMs) has made the conventional inverse synthetic aperture radar (ISAR) associated with the regular signal waveforms more vulnerable and unreliable. To deal with this issue, the complex waveform designment with staggered pulse sampling is developed in an ISAR system in this letter. In the proposed method, the generalized time-scaled transform (GTST) is adopted to effectively accomplish the irregular signal reconstruction and linear phase decoupling. After that, a set of matched filtering functions are established and interspersed in the imaging processes to accomplish the subsequent motion compensation, target imaging, and range and cross-range scaling. Finally, a satisfied ISAR imagery corresponding to the real size of a moving target can be recovered. The simulation and real-measured radar data processing results are applied to demonstrate the effectiveness of the proposed method.

In this letter, we propose a novel range and Doppler sampling mismatch compensation method for moving target detection, which can effectively improve the output signal-to-noise ratio (SNR) of a moving target. In the proposed method, after performing the target coherent integration by using the well-known Keystone transform (KT), the range and Doppler sampling mismatch errors (SMEs) are estimated and compensated based on the constructed optimization model with the consideration of the change rate of a moving target peak amplitude. In order to improve the computational efficiency, the Hooke–Jeeves method is applied to achieve the optimal solution of the constructed optimization problem, thus efficiently solving the target energy diffusion problem caused by the SMEs. Simulated experiment is presented to verify the effectiveness and feasibility of the proposed method.

Synthetic aperture radar (SAR), as a wideband radar system, may be subject to strong interferences with a variety of signals. The narrowband interference (NBI) exemplifies the most typical of its kind, such as the form of radio frequency interference (RFI). With the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multichannel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). Previous interference methods focused on single-channel SAR systems and few research works for MC-SAR systems. In this article, we first derive a new interference-mitigation model for HRWS SAR systems and conclude that the low-rank property of the NBI is suitable for MC-SAR systems. Then we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the NBIs by solving the low-rank recovery problems of NBIs. Also, the MC-SAR system errors are further taken into account as a measure for our method’s practical applicability. Finally, the real SAR data is used to demonstrate the effectiveness of the proposed method.

Radio frequency interference (RFI) is a significant threat to accurate microwave remote sensing. The RFI signals manifest themselves in unpredictable locations and patterns in the image, which will cause measurement distortion, image degradation, or even lead to wrong retrievals of the geophysical parameters. Accurate detection of RFI artifacts is a prerequisite step to preserve the overall quality of remote sensing quality. In this paper, a semantic cognitive enhancement network for RFI signature detection is proposed. It employs an encoder-decoder architecture, which incorporates the atrous spatial pyramid pooling, Depthwise convolution, and self-attentional mechanism. Rather than detecting the existence of RFI artifacts for an entire image, the proposed scheme can realize RFI recognition in a pixel-wise manner without setting predefined thresholds. Extensive experimental results on diverse scenarios in Sentinel-1 images with various RFI types are provided, which demonstrates robust detection performance for both strong and weak interference without requiring a large number of training samples.

In this article, an effective clutter suppression algorithm is presented for the space-borne azimuth multiantenna high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, which is based on the null-space technique. First, the echo of bistatic geosynchronous-low earth orbit (GEO-LEO) azimuth multiantenna HRWS SAR-ground moving target indication (GMTI) system is utilized to deduce the coarse-focused image of moving targets and clutter, where the Chirp Fourier transform (CFT) in azimuth is involved. Then, the matrix form can be utilized to describe the coarse-focused image of the multiantenna SAR system and the corresponding covariance matrix can be estimated. After that, the null-space is constructed using the covariance matrix corresponding to clutter, where at least a redundant channel freedom is required. Since the null-space vector is orthogonal to signal-space vector, it can be used to suppress the clutter. As an equivalent phase is brought by the slant velocity, the moving targets’ echo can be preserved during clutter suppression. Then, the optimization and suboptimization vectors for clutter suppression are introduced. It is worth noting that the proposed clutter suppression algorithm is robust for the antenna mismatch, that is, the antenna mismatch in phase and the corresponding position error. Finally, the theoretical investigations are validated using some simulation experiments, where the experiments for bistatic GEO-LEO and single-platform azimuth multiantenna HRWS SAR-GMTI system are both included. In addition, the real measured single-platform azimuth multiantenna HRWS SAR data experiments are also performed.

To meet the needs of the Internet of Things, every edge device is equipped with the functions of data collection, analysis, calculation, communication, and intelligence. Based on the consumption pattern of offline experience and online purchase, and considering the impact of product quality differences, product defects, and offline service level on customers' purchasing behavior, this paper uses the model (Multinominal Logit Model) to research customers' choice behavior and online product pricing. This paper takes the pricing of dual-channel retailers in different channels as the background, and how to maximize the retailer's profit as the goal, establishes the loss cost model of customer returns, and analyzes the influence of quality problem returns on the optimal pricing and profit of retailers in different channels. The study found that the offline service level remains at 0.24 and retailers can obtain the best profit; the optimal price decreases with the online product quality and the optimal profit increases. In the omni-channel environment, customers can buy products according to their own utility and preferences freely switch between various channels, retailers in the face of customer return this situation, can start from their own interests, provide appropriate service level, reasonable control product quality, make the optimal pricing, maximize their own profits. This study expands the theory of online product pricing from the perspective of customers behavior, provides a more flexible pricing mechanism for enterprises, and speeds up the development and application of intelligent edge computing systems.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel high-resolution and wide-swath (HRSW) synthetic aperture radar (SAR) system. First, a spatial correlation function is constructed to remove the range cell migration and Doppler broadening of ground static targets. Then, the iterative adaptive approach (IAA) is applied to iteratively estimate the along-track baseline with high precision. Finally, a high-resolution SAR image can be obtained based on the estimated baseline. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

For multi-channel synthetic aperture radar of ground moving target imaging (SAR GMTIm), the moving targets in SAR image domain are sparse after clutter suppression, which provides the possibility of using sparse approach. In this paper, a reweighted sparse algorithm of SAR GMTIm is proposed to improve the imaging performance. Intuitively, both the magnitude and interferometric phase can exhibit the moving target signatures by applying displaced phase center antenna (DPCA) technique and along-track interferometry (ATI), respectively. So a hybrid metric of magnitude and interferometric phase is constructed to be as the weights of the reweighted sparse approach. Compared with the unweighted approach, the proposed reweighed approach can effectively improve the sparse imaging performance. Finally, experiments using measured data are performed to confirm the effectiveness of the proposed algorithm.

Spaceborne bistatic synthetic aperture radar (SAR) system with an inclined geosynchronous (GEO) illuminator and a low-earth-orbit (LEO) receiver is capable of providing a vast area of surveillance and fine spatial resolution, which presents huge potentials for future earth observation. In this letter, inclined GEO-LEO azimuth multichannel SAR (MC-SAR) system for high-resolution wide-swath imaging is investigated. Starting from modeling geometry of inclined GEO-LEO bistatic MC-SAR, the signal model is analyzed in detail for the first time, and the equivalent positions of the received channels are obtained. Then, we found the spatial-variant residual phase error cannot be compensated accurately by conventional effective phase center (EPC) processing. Moreover, because of the complex bistatic configuration, the frequency-domain imaging algorithms become extremely difficult to achieve a well-focused and phase-preserved image. Meanwhile, the time-domain back-projection algorithm (BPA) faces with a technical challenge due to the nonuniform sampling in azimuth. To address these issues, a bistatic weighted BPA (BWBPA) for GEO-LEO MC-SAR is derived and presented. Without the procedure of EPC, the BWBPA can suppress azimuth ambiguities effectively and yield phase-preserved SAR images. Simulated data results show the validity of the presented method.

Phase unwrapping, a process of recovering the real phase from ambiguous one, is one of key techniques for interferometric synthetic aperture radar (InSAR) data processing. The larger interferograms make the phase unwrapping to be a more challenging task. A novel fast phase unwrapping method is proposed in this paper which is very effective in dealing with the large scale datasets. Several sub-networks consisting of residues are constructed in the method. Therefore, smaller networks make the execution more efficient using minimum cost flows(MCF) algorithm. In addition, the method takes an effective strategy to balance all the sub-networks in order to ensure a feasible solution to each sub-network. Experiment with real data demonstrates the effectiveness of the proposed method.

When combined with digital beam-forming (DBF) techniques, multichannel synthetic aperture radar (SAR) systems can achieve high-resolution and wide-swath SAR imaging. However, inevitable channel biases will degrade the performance of DBF in practice. To address this problem, a novel channel phase bias estimation algorithm is proposed in this letter. Theoretical analysis reveals that the signal of the first channel, which is considered as the reference channel, can be reconstructed from the signals of other channels, regardless of noise and signals outside the Doppler bandwidth. In the presence of phase biases, there is a reconstruction error after the cancellation by subtracting this reconstructed signal from the original signal. However, by minimizing the reconstruction error, the channel phase biases can be precisely estimated. The effectiveness of the proposed algorithm is validated by the experimental results.

In multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) systems, Doppler centroid (DC) is essential for SAR focusing. However, conventional phase-dependent DC estimators for single-channel SAR systems face many problems in multichannel HRWS SAR systems because of the azimuth undersampling of each channel and channel mismatches in phase. To estimate the baseband DC, the spatial cross-correlation coefficients (SCCCs) of multichannel HRWS SAR signals are exploited, and a novel baseband DC estimator named SCCC method is proposed in this letter. Validation of the proposed method is demonstrated with the real airborne multichannel SAR data.

Multichannel synthetic aperture radar (SAR) systems in azimuth can achieve high-resolution and wide-swath imaging. However, the quality of final SAR image can be degraded by the channel mismatch in phase which increases the energy outside the processed Doppler bandwidth (PDB). To address this problem, a calibration algorithm is proposed in this letter by minimizing the energy outside the PDB. Theoretical analysis shows that the presented method can be interpreted as an adaptively weighted least square estimation problem, where the weights are related to the signal-to-noise ratio (SNR) of the echoes from different directions. Simulation results reveal that our method outperforms the conventional methods in the case of quasi-uniform sampling, particularly at the low-SNR region.

For high-resolution wide-swath (HRWS) SAR based on multiple receive apertures in azimuth, this paper proposes a novel imaging approach for moving targets. This approach utilizes the wide bandwidth characteristics of the transmitted signal (multiple wavelengths) to estimate the moving target velocity. First, this paper explains that there is a phase mismatch (PM) between azimuth channels for the echo of a moving target, which depends on range frequency. In order to correct the PM, an algorithm based on local maximum-likelihood minimum entropy is proposed. The linear dependence of the PM on range frequency is employed to estimate the target velocity. Second, after the signal reconstruction in Doppler frequency and the compensation of the PM for a moving target, the estimated target velocity is utilized to implement the linear range cell migration correction and the Doppler centroid shifting. Then, the quadratic range cell migration is corrected by the keystone processing. After that, the focused moving target image can be obtained using the existing azimuth focusing approaches. Theoretical analysis shows that no interpolation is needed. The effectiveness of the imaging algorithm for moving targets is demonstrated via simulated and real measured ship HRWS ScanSAR data.

In this paper, a comparison between four channel error estimation methods for high resolution and wide swath (HRWS) synthetic aperture radar (SAR) systems is present. Three of the methods are based on subspace theory and implemented in Doppler frequency domain, while the fourth is based on the correlation between adjacent samples and implemented in time domain. After a brief overview of the approaches, the performance of each is analyzed with respect to its computational complexity and precondition. Quantitative results are shown using the ground-based real-data.

With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar (SAR) systems are promising in high-resolution wide-swath imaging. However, the mismatch among channels will degrade the performance of DBF. In this letter, two novel methods are proposed to estimate channel errors for multichannel SAR systems in azimuth. The first method is based on the fact that the space spanned by the signal eigenvectors is equal to that spanned by the practical steering vectors. In the second method, the channel errors are directly estimated by the antenna patterns without matrix decomposition and inversion processing. Both the theoretical analysis and experiments demonstrate the effectiveness and efficiency of these two methods.

Incorporated with digital beam-forming processing, multichannel spaceborne synthetic aperture radar (SAR) systems are able to overcome the minimum antenna area constraint and yield high resolution and wide swath (HRWS) images. This letter mainly investigates the performance of the space-time adaptive processing (STAP) approach applied to HRWS SAR imaging. The analytic expressions for the signal-to-noise ratio (SNR) scaling factor and azimuth ambiguity to signal ratio (AASR) are derived and confirmed by the simulated results. Then, the influence of channel errors on HRWS imaging is analyzed in detail.

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is an essential tool for modern remote sensing. To effectively deal with the contradiction problem between high-resolution and low pulse repetition frequency and obtain an HRWS SAR image, a multi-channel in azimuth SAR system has been adopted in the literature. However, the performance of the Doppler ambiguity suppression via digital beam forming processing suffers the losses from the channel mismatch. In this paper, a robust channel-calibration algorithm based on weighted minimum entropy is proposed for the multi-channel in azimuth HRWS SAR imaging. The proposed algorithm is implemented by a two-step process. 1) The timing uncertainty in each channel and most of the range-invariant channel mismatches in amplitude and phase have been corrected in the pre-processing of the coarse-compensation. 2) After the pre-processing, there is only residual range-dependent channel mismatch in phase. Then, the retrieval of the range-dependent channel mismatch in phase is achieved by a local maximum-likelihood weighted minimum entropy algorithm. The simulated multi-channel in azimuth HRWS SAR data experiment is adopted to evaluate the performance of the proposed algorithm. Then, some real measured airborne multi-channel in azimuth HRWS Scan-SAR data is used to demonstrate the effectiveness of the proposed approach.