Robust perception for safety-critical Internet of Things (IoT) applications in dense jamming environments demands advanced radar sensing capabilities with enhanced interference mitigation and array-beampattern optimization. When the target direction overlaps with the jamming region, both a narrower mainlobe and a deeper sidelobe are desirable but hard to meet simultaneously since they both consume array degrees of freedom. This paper investigates the application of Movable Antenna Array (MAA) in aiding radar beampattern synthesis under dense jamming scenarios. Using the extra spatial degrees of freedom provided by MAA, this work jointly optimizes both weighting vectors and antenna element positions to achieve superior beampattern without adding array element number. The integrated sidelobe level is chosen as the optimization objective with practical constraints, which leads to a highly non-convex optimization. To address this, we propose an Alternating Optimization-based Sequential Approximation (AOSA) algorithm. In each iteration, the weighting vector subproblem is solved through convex approximations of the primary nonconvex constraints, while the antenna position vector subproblem is tackled indirectly with a specifically derived proposition. Simulation results verify that the joint optimization framework effectively improves target separability and jamming rejection in complex electromagnetic environments, demonstrating its promising potential for advancing radar detection capabilities.

For high-resolution squinted airborne synthetic aperture radar (SAR) imaging, both linear range walk correction (LRWC) and motion error introduce significant azimuth spatial-variant (ASV) characteristics in the radar echo, rendering the classical assumption of "azimuth translational invariance" no longer valid. Existing sub-aperture methods attempt to overcome the ASV characteristics of the signal by performing segmentation processing in the data domain or the image domain. However, grating lobes or image stitching problems inevitably occur in the focused images. Existing full-aperture methods, on the other hand, utilize azimuth resampling or nonlinear chirp scaling (NCS) to address the ASV problem. Nevertheless, the above-mentioned methods basically handle the ASV characteristics introduced by LRWC and motion errors separately, without considering the coupling characteristics between the two. Therefore, this paper proposes a high phase-preservation squint airborne SAR autofocus imaging method by modifying the traditional azimuth resampling processing, so that only a single azimuth resampling factor is required to simultaneously solve the ASV problems brought about by LRWC and motion errors. The imaging processing results of airborne squint SAR real-data verify its good focusing effect. Meanwhile, the interferometric processing results of multi-pass cross-track SAR real-data also indicate that the proposed algorithm exhibits a high phase-preservation capacity. The images processed by the proposed algorithm and the comparison algorithms, as well as the multi-pass cross-track SAR complex images after registration, can be downloaded from https://pan.baidu.com/s/1okgAkp18ynK7qzKXe2lceQ?pwd=nquf.

As a novel configuration of along-track Multistatic SAR (Multi-SAR), the high frame rate Along-Track Swarm SAR (ATS-SAR) has garnered significant attention in recent years due to its exceptional efficiency in reducing data acquisition time. Motivated by its potential for high-resolution imaging of moving targets, this paper investigates the application of ATS-SAR in moving target imaging. However, high frame rate ATS-SAR-based moving target imaging confronts two critical challenges: time-space coupling and partial data loss in moving target echoes. To address these challenges, we first conduct a comprehensive analysis and theoretical derivation of the moving target echo model under the high frame rate ATS-SAR configuration. Subsequently, we propose an innovative motion parameter estimation algorithm that exploits unique echo characteristics to achieve high-performance imaging. Furthermore, we introduce the highresolution, high frame rate ATS-SAR Sub-Aperture Collaborative Imaging algorithm for Moving Targets (MT-SACIm-ATS). Extensive simulations and a real measured experiment validate the effectiveness of the MT-SACIm-ATS algorithm, demonstrating imaging performance that closely approximates reference imaging results. Comparative analysis with several state-of-the-art algorithms further highlights the superiority of the proposed approach in terms of resolution and robustness.

Due to its rapid data acquisition capabilities, the Along-Track Swarm SAR (ATS-SAR) system offers a distinct advantage in simplifying the motion states of moving target. This paper addresses challenges encountered in ATS-SAR moving target imaging, specifically focusing on issues related to partial aperture data loss and the spatiotemporal non-equivalence of the ATS-SAR moving target echo. A novel ATS-SAR imaging method for moving target is introduced. Initially, the ATS-SAR moving target echo model is established. Subsequently, the proposed algorithm designs a phase compensation function that contributes to the accurate reconstruction of the complete echo for ATS-SAR moving target, resulting in outstanding imaging performance.

The motion error of airborne microwave photonic synthetic aperture radar (SAR) has 2-D spatial variation characteristics, and the range spatial variant motion error (RVE) and azimuth spatial variant motion error (AVE) significantly interplay during the motion error estimation. For the RVE estimation, the standard weighted least square (WLS) algorithms are susceptible to the AVE and the moving targets. In addition, the azimuth subimage-based WLS algorithms face the problem of dominant points decreasing dramatically. This letter proposes a WLS estimation kernel based on nonlinear chirp scaling (NCS) to address the issues above. The AVE is first significantly corrected by the NCS processing, and RVE is subsequently estimated using the standard WLS kernel. In addition to eliminating the adverse effects of AVE and moving targets, the proposed method can retain sufficient dominant points to ensure the accuracy of phase gradient autofocus (PGA). The measured data processing results verify the effectiveness of the proposed method.

This letter studies the issue of ground accelerating target imaging with airborne circular stripmap synthetic aperture radar (CSSAR). Most of the existing ground moving target imaging algorithms are based on the Taylor approximated range model. However, for CSSAR and accelerating targets, the high-order terms of the Taylor series should be kept to meet the requirement of high-quality imaging, which would complicate the imaging process. In this letter, a more accurate approximation, i.e., the Chebyshev polynomial approximation, is made to the target’s range equation, which reduces the number of parameters to be searched or estimated during imaging. Moreover, to deal with the Doppler ambiguity and range migration caused by the target’s unknown motion, an azimuth-time and range-frequency domain-matched filtering-based imaging method is proposed. Furthermore, the differential evolution (DE) algorithm is adopted to achieve an efficient search of the parameters of the Chebyshev polynomial approximated range model. Finally, numerical experiments are conducted to validate the proposed method.

Neural networks for synthetic aperture radar (SAR) automatic target recognition often encounter overfitting challenges owing to limited training samples. Moreover, the azimuth angle of SAR, a vital parameter for improving network generalization, is frequently disregarded in most models. In response, we propose MIGA-Net, a classification neural network that effectively perceives azimuthal information using multi-view images to improve classification performance. Specifically, we quantize low-dimensional azimuthal values for sample-limited scenarios. Then, we utilize encoded image sequences as training data because they encompass spatial context information compared to individual images. After extracting features of the sequence samples through convolutional layers, we design a two-layer output module. One layer converts these sequence features into graph data. Then the dense graph attention network (GAT) extracts contextual features from the graph data for angle estimation. Simultaneously, another layer combines these features for target classification. During the network training, the GAT module can extract image azimuth features with powerful information aggregation capabilities. It supervises the convolutional layers to learn azimuth features, which are fused with class features from another layer to obtain a more structured feature domain. This feature domain significantly enhances the classification performance of the network. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have proven the superior performance of the proposed method, achieving at least 1% higher accuracy compared to other state-of-the-art algorithms.

The microwave photonic synthetic aperture radar (SAR) is capable of realizing large scene remote sensing observation with centimeter or even millimeter resolution, which greatly enhances the ability to acquire target information. A key issue in airborne microwave photonic SAR imaging is how to accurately correct the two-dimensional (2-D) spatial variation characteristic of the motion error. The typical two-step motion compensation (MoCo) method cannot correct the azimuth spatial variant characteristic of motion error, and the traditional subaperture methods may introduce the problems of grating lobes and image stitching. In addition, the efficiency of existing parametric full-aperture autofocus methods is usually low. To solve the above problems, a nonparametric full-aperture autofocus method based on a two-stage processing framework is proposed in this article. The first stage is to introduce a nonparametric low-order nonlinear chirp scaling (NCS) or resampling (RS) model to compensate for the low-order spatial variant motion error that accounts for the dominant component before the range cell migration correction (RCMC), which ensures that there is no significant residual RCM after the RCMC. The second stage introduces a nonparametric high-order NCS/RS model after the RCMC to compensate for the remaining high-order azimuth spatial variant phase error to achieve accurate azimuth focusing. Based on the full-aperture processing strategy, the algorithm proposed in this article avoids the problems existing in the subaperture methods. In addition, the nonparametric modeling is used throughout the autofocus processing (e.g., motion error estimation and parameter reversion of NCS/RS model), which greatly improves the efficiency of autofocus processing. The results of processing simulated and measured data verify the effectiveness of the proposed algorithm.

Air moving target indication (AMTI) is an important task of spaceborne radar. Medium-Earth-orbit (MEO) synthetic aperture radar (SAR) has the characteristics of large coverage, short revisit time, and high orbital altitude, and thus is an attractive tool for AMTI missions. However, due to air moving target’s high-speed maneuvering and MEO SAR’s long synthetic aperture time and significant curvature of trajectory, the signal of air moving target after imaging processing is very complex, which makes the design of AMTI method be challenging. In this article, a study on image-domain signal modeling and refocusing of air moving target for MEO multichannel SAR is presented. The range equation of an air moving target with 3-D velocities and accelerations is established. In addition, the target’s signal models after range migration correction and azimuth compression with the stationary scene parameters are derived. Then, via dividing targets into three types based on their residual range migration and Doppler ambiguity, the image-domain signal models of air moving targets are developed. Furthermore, a refocusing method that can cope with large range migration and Doppler ambiguity is proposed. Finally, numerical experiments are conducted to validate the developed signal models and proposed refocusing method.

At present, the resolution of the most advanced airborne microwave photonic synthetic aperture radar (SAR) can reach the order of centimeters or even millimeters, so the two-dimensional spatial variation and two-dimensional coupling characteristics of motion error will become more serious. In this paper, based on the advantages of nonlinear chirp scaling (NCS) and resampling (RS) processing, a microwave photonic SAR full-aperture autofocus algorithm based on a cascaded NCS-RS is proposed. Firstly, the proposed algorithm combines the typical two-step MoCo and chirp-Z transform (CZT) to correct the range spatial variant characteristics of motion error. Then, a cascaded NCS-RS processing is used to correct the azimuth spatial variant characteristics of motion error, in which NCS processing is introduced before range cell migration correction (RCMC) and RS processing is introduced after RCMC. Finally, the RS in cascaded NCS-RS processing is modified to change with range to correct the range-azimuth coupling characteristic of motion error. The three steps of the algorithm belong to the full-aperture processing, which avoids the problems of grating lobes and image stitching caused by the sub-aperture algorithm. The estimation of the parameters in NCS-RS processing is modeled as a high-dimensional optimization problem. Before solving this optimization problem, it is converted to multiple one-dimensional optimization problems. The results of processing simulated and measured data verify the effectiveness of the proposed algorithm.

For airborne synthetic aperture radar (SAR) imaging with high resolution and wide swath, the atmospheric turbulence may produce serious range-dependent (RD) motion error. To estimate the RD motion error, traditional methods usually first divide the range full-aperture data into multiple range blocks, and then use phase gradient autofocus (PGA) to estimate the phase error of all range blocks one by one, which is inefficient. In addition, the robustness of PGA is also affected by the number of strong scattering points. To solve these two problems, a new motion compensation (MoCo) algorithm is proposed to improve the efficiency and robustness of airborne SAR MoCo. The real data-processing results are given to verify the effectiveness of the algorithm.

Synthetic aperture radar (SAR) imaging usually needs to be converted between the time domain and the frequency domain, in which the solution of stationary phase point determines the accuracy of time–frequency conversion and the final image quality. Theoretically, the stationary phase point can be accurately solved by the method of series reversion (MSR) in an arbitrary configuration (e.g., bistatic and/or nonlinear trajectory). However, the existing methods based on MSR are proposed based on the assumption of specific signal form. In other words, it is necessary to reuse MSR to derive the time–frequency conversion for different signal forms, which brings great inconvenience to engineering applications. In this article, we aim to propose a general method based on MSR. Based on this method, the accurate time–frequency conversion can be derived by simply arranging any signal to the standard form specified in this article first and then simply replacing the variables.

Due to the requirements of portability and omni-directivity, the planar bow-tie antenna is widely used in impulse through-wall radar (ITWR). When the planar bow-tie antenna is used to radiate the ultrawide bandwidth (UWB) signal, the ringing phenomenon of the transmitted signal would be serious, which can damage the quality of radar imaging. The previous solutions for this problem are the usage of various hardware loadings; however, those loadings could cause signal energy loss and reduce the signal gain. Alternatively, this letter studies a deconvolution-technique-based signal processing approach to suppress the signal ringing. Because the proposed approach does not require any hardware loadings on the antenna, it can help improve the signal-to-noise ratio (SNR) and significantly reduce the energy loss of signal. The effectivity of this signal processing approach is verified by the radar detecting experiments.

Squint ultrahigh-resolution (UHR) synthetic aperture radar (SAR) generally uses the extended Omega-K algorithm (EOK) for range cell migration correction (RCMC). However, the extra range migration and defocusing (ERMD) will be introduced by the EOK when there are motion errors. This problem will be severe as the squint angle increases, which may sharply reduce the signal-to-noise ratio (SNR) and lead to the failure of the existing autofocus methods. In this letter, a UHR autofocus algorithm based on cascaded map-drift (MD)-PGA is proposed. The MD is used for the rough estimation to improve the SNR and the phase gradient algorithm (PGA) is used to accurately estimate the residual error based on a relatively high SNR. The new algorithm combines the advantages of MD and PGA, which can solve the problem of severe defocusing. The processing of airborne real data validates the effectiveness of the proposed algorithm.

Spaceborne synthetic aperture radar (SAR) real-time imaging is especially important for disaster emergencies and real-time monitoring applications with highly desired real-time requirements. Therefore, the continuous improvement of real-time imaging efficiency is an important development trend. At present, traditional real-time imaging algorithms based on constant pulse repetition frequency (PRF) have low accuracy when processing spaceborne SAR data with nonlinear trajectory. For this problem, the existing methods usually introduce some complex signal processing steps, such as scaling or interpolation processing, to improve the accuracy of the real-time imaging, but this will reduce its efficiency. Therefore, this article proposes a new real-time imaging algorithm based on variable PRF for nonlinear trajectory spaceborne SAR. By introducing the variable PRF, the proposed algorithm is equivalent to complete the complex signal processing steps in the radar signal transmission stage, which can greatly improve the efficiency of real-time imaging. Simulation experiments verify the effectiveness of the algorithm.

For ultra-high resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultra-high resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation for ultra-high resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm.

In the existing time-domain autofocus algorithms, the azimuth deramping operation will change the azimuth-independent phase into the azimuth-dependent phase, which may greatly reduce the accuracy of autofocus processing in squint spotlight synthetic aperture radar (SAR). In contrast, the frequency-domain autofocus algorithms can avoid this problem because it does not involve the azimuth deramping operation. However, the existing frequency-domain autofocus algorithms are proposed based on the assumption of broadside mode, which cannot be directly applied to the squint mode. Therefore, this article extends the existing frequency-domain autofocus algorithm to the squint mode combined with the extended Omega-K (EOK) algorithm. Furthermore, a space division (SD) algorithm is embedded into the proposed algorithm as preprocessing, which can effectively compensate for the azimuth-dependent motion error. The simulation and real data are processed to verify the effectiveness of the algorithm.

There has been an increasing interest in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse sampling data. Traditional compressed sensing (CS) and matrix completion (MC) methods are based on sparse and low-rank constraints, respectively, which do not make full use of the structure of ISAR data. In this article, a sparse ISAR imaging algorithm using a structured low-rank approach is proposed for enhanced imaging performance. Based on the observation that the structured Hankel matrix has better low-rank property, the proposed algorithm can outperform the group of conventional MC methods in terms of accuracy to data quality and quantity. Rather than using the traditional singular value decomposition (SVD) solution of nuclear norm minimization, the proposed algorithm restates the nuclear norm via an equivalent reformulation that the structured Hankel matrix can be decomposed into two disjointed parts to avoid the dimensional expansion of the Hankel matrix. Meanwhile, the alternative direction method of multipliers (ADMMs) is applied to effectively reduce the computational complexity. Finally, the effectiveness of the proposed algorithm is further validated using the experiments on simulated and measured data.

Division-of-focal-plane (DoFP) polarimeter provides a way for snapshot acquisition, making it available to simultaneously record polarization measurements at different orientations. This polarization imaging system has gained more attention in the last few years and is promising to be used in the fields of computer vision and remote sensing. However, this system suffers from the degradation of spatial resolution. To reconstruct polarization information at full resolution, polarization image demosaicking is indispensable. To address polarization image demosaicking issue while preserving the essential structure of polarization data, a sparse tensor factorization-based model is proposed. For a target cube, its similar cubes are first grouped together as a tensor. Then, its compact dictionary and sparse core tensor are learned by factorizing the tensor using sparse coding. Moreover, the correlation among different polarization orientations and the nonlocal self-similarity are adopted to boost the performance. Experimental results on synthetic and real-world data demonstrate that our proposed model outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.

A nonlinear trajectory of a radar platform in synthetic aperture radar (SAR) may lead to severe coupling between the range and the azimuth, which may make the ambiguity function (AF) analysis complicated. The numerical algorithm-based AF analysis may be computationally expensive, while the existing analytical algorithm-based AF analysis may cause large errors because it does not consider the coupling between the range and the azimuth. By observing that the singular value decomposition (SVD) is good to deal with the coupling problem, in this article, we propose an effective AF analysis based on SVD. The key idea is to first use a small amount of sampling points for SVD of the coupled term in the AF and then the decoupled vectors are fitted to high-order polynomials for the analytical AF calculation. It converts the double integral into the product of two single integrals in the calculation. From the proposed SVD-based AF analysis, three parameters, namely, 3-dB resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR), are then effectively computed. The simulated results verify the good performance of the proposed SVD-based AF analysis.

The water body detection in high-resolution synthetic aperture radar (SAR) images is a challenging task due to the changing interference caused by multiple imaging conditions and complex land backgrounds. Inspired by the excellent adaptability of deep neural networks (DNNs) and the structured modeling capabilities of probabilistic graphical models, the cascaded fully-convolutional network (CFCN) is proposed to improve the performance of water body detection in high-resolution SAR images. First, for the resolution loss caused by convolutions with large stride in traditional convolutional neural network (CNN), the fully-convolutional upsampling pyramid networks (UPNs) are proposed to suppress this loss and realize pixel-wise water body detection. Then considering blurred water boundary, the fully-convolutional conditional random fields (FC-CRFs) are introduced to UPNs, which reduce computational complexity and lead to the automatic learning of Gaussian kernels in CRFs and the higher boundary accuracy. Furthermore, to eliminate the inefficient training caused by imbalanced categorical distribution in the training data set, a novel variable focal loss (VFL) function is proposed, which replaces the constant weighting factor of focal loss with the frequency-dependent factor. The proposed methods can not only improve the pixel accuracy and boundary accuracy but also perform well in detection robustness and speed. Results of GaoFen-3 SAR images are presented to validate the proposed approaches.

For the airborne SAR imaging, the motion compensation (MOCO) is required because of the phase error caused by the atmospheric turbulence. In the case of ultrahigh-resolution and wide-swath, the phase error may be rangedependent (RD), which may induce a RD residual-RCM after the correction of range cell migration (RCM) by using the range migration algorithm (RMA). Currently, phase gradient autofocus (PGA) is widely used to estimate the phase error from the raw data. However, the RD residual-RCM may degrade the accuracy of the PGA. To overcome such a problem, we present a CZT correction algorithm to correct the RD residual-RCM. Processing of airborne real data validates the effectiveness of the proposed algorithm.

The motion compensation (MOCO) for the airborne SAR with ultrahigh resolution and wide swath is required to consider the range-dependent (RD) phase error. The RD phase error may cause an RD residual-range cell migration (RCM) after the correction of RCM, which can degrade the performance of phase gradient autofocus (PGA) when estimating the phase error. In addition, because the PGA estimation is based on the strong scattering point, it may wrongly estimate the phase error for some observation scenes without strong scattering point. Alternatively, to take into account the above two problems, we study a MOCO algorithm based on two-step accuracy improvement. In the algorithm, the first step is to estimate and correct the RD residual-RCM and thus improves the accuracy of PGA. The second step is to develop a prior-information-based-weighted least square (PI-WLS) to further improve the accuracy of RD phase error estimation. Processing of airborne real data validates the effectiveness of the proposed algorithm.

The curved trajectory can lead to severely 2-D spatial-variance in spaceborne synthetic aperture radar (SAR). The azimuth-variance makes the traditional frequency domain imaging algorithms for the straight trajectory based on the assumption of azimuth translational invariance invalid. To correct the severely 2-D spatial-variance in curved trajectory spaceborne SAR, this paper studies a frequency imaging algorithm based on an optimal linear range walk correction (LRWC) preprocessing and 2-D singular value decomposition (SVD). Before the correction of the 2-D spatial-variance, an optimal LRWC preprocessing is introduced to minimize the azimuth-variance. Subsequently, a range block-SVD is proposed to correct the range-variance and, thus, achieves the accurate range cell migration correction. Finally, the azimuth tandem-SVD method is used to correct the azimuth-variance and, thus, accomplishes the azimuth compression for the whole azimuth scene. Processing of the simulated data validates the effectiveness of the proposed algorithm.

Due to the very high orbit, the apparent features of geosynchronous synthetic aperture radar (GEOSAR) are the curved trajectory and long integration time, which can lead to severe coupling between the azimuth and the range directions and, therefore, complicates the resolution evaluation. The traditional analytical approach based on the 2-D division may produce large resolution error, and the numerical approach may suffer from huge computation burden. Therefore, an analytical resolution evaluation approach for GEOSAR based on the local feature of the ambiguity function is studied in this paper. The proposed approach is validated with simulation data to be of high efficiency and accuracy. In addition, the proposed approach is also demonstrated to be capable of evaluating the resolution for other complex platforms, and of evaluating the 3-D resolution of a SAR system.

The available focusing algorithms for medium-Earth-orbit (MEO) SAR are all based on the complex nonhyperbolic range equation, which may make it more difficult in imaging processing. In this paper, we model the range equation as the standard hyperbolic form based on the motion compensation (MOCO) principle. However, the conventional two-step MOCO may introduce azimuth spectrum expansion due to the potential large motion error, which can lead to severe azimuth ambiguity. To resolve this problem, we develop an omega-K algorithm based on a modified two-step MOCO and an adaptively straight equivalent (ASE)-velocity model. The algorithm is implemented through three-step processing: 1) the modified two-step MOCO does not compensate for the quadratic motion error (the main factor for the spectrum expansion); 2) an ASE-velocity model is introduced to compensate for the quadratic motion error; and 3) an extended Stolt mapping is proposed to perform the accurate range cell migration correction, and the tandem singular value decomposition-nonlinear chirp scaling algorithm is to correct the azimuth-variant phase error and to perform the azimuth compression. Processing of simulated data and airborne SAR real data validates the effectiveness of the proposed algorithm.

Image formation of large scenes is still challenging in medium-earth-orbit (MEO) synthetic aperture radar (SAR) due to the existence of severe 2-D space variance. In this paper, the properties of space variance are analyzed in detail, and then a variable-coefficient fourth-order range model is adopted to model the space-variant range history of every target in a large scene accurately. A method integrating a modified chirp scaling algorithm with joint time-Doppler resampling is proposed to address the range-variant range cell migration, as well as the azimuth-variant frequency-modulation rate and higher order Doppler parameters. The computational burden and alternative implementation approaches are also discussed. Finally, processing of simulated data for MEO SAR with 2-m resolution is presented to validate the proposed algorithm.

For the ultrahigh-resolution airborne stepped-frequency synthetic aperture radar, very large synthetic bandwidth and very long integration time may lead to a 2-D space-variant (SV) motion error when the aircraft flies off the ideally straight trajectory due to the atmospheric turbulence. This new type of error complicates the motion estimation and motion compensation (MOCO). For the motion estimation, we present a jointly 2-D SV motion error estimation method to simultaneously consider the range-variant motion error and the azimuth-variant motion error. For the MOCO, we propose a 2-D SV-MOCO method. The method is implemented through three processing steps: 1) two-step MOCO for the space-invariant motion error and the range-variant phase error; 2) range block-based chirp-z transform (CZT) for the range-variant envelope error; and 3) range block division for the range-dependent azimuth-variant phase error based on the azimuth subaperture method. Finally, processing of simulated data and real data validates the proposed methods.

The ultralong synthetic aperture time and a very large scene cause severe 2-D spatial variation in geosynchronous synthetic aperture radar. The range variation was corrected using the range cell migration equalization and the modified chirp scaling function. The azimuth variation correction with the singular value decomposition and the azimuth nonlinear scaling was studied. The validity of the proposed imaging algorithm has been assessed. Satisfactory results were obtained in the removal of the azimuth variation, and the focusing of point targets from a synthetic aperture up to 1000 sand a scene of 150 km (azimuth) × 130 km (range).

Signal bandwidth and integration time are two significant parameters of a geosynchronous synthetic aperture radar (SAR) sensor. They directly determine the resolution characteristic of SAR imagery. Because their contributions to the ground impulse response width (IRW) curve (consists of −3-dB resolutions in every direction) are severely coupled, an analytical extraction method of the ground IRW curve is studied to analyze the coupling characteristic. Due to the coupling, the IRW curve is generally spatially variant and, thus, can degrade the quality of SAR imagery. To minimize the variation, the two parameters are optimized. However, the optimized signal bandwidth is found to be satellite position varied, which complicates the system design. To solve this problem, a parameter optimization model is built to obtain one optimal signal bandwidth as well as to decrease the variation.

The imagery of highly squinted synthetic aperture radar mounted on maneuvering platforms with nonlinear trajectory is a challenging task due to the existence of acceleration and the cross-range-dependent range migration and Doppler parameters. In order to accommodate these issues, a frequency-domain imaging algorithm based on tandem two-step nonlinear chirp scaling (TNCS) with small aperture is proposed. For the cross-range-dependent range cell migration (RCM) caused by the linear range walk correction and acceleration, the first-step NCS is introduced to suppress this dependence and realize the unified RCM correction. Based on the differences between full-aperture and small-aperture data in the cross-range processing, the second-step NCS is introduced in frequency domain to equalize the cross-range-dependent Doppler parameters, for cross-range processing is more sensitive to the cross-range dependence than range processing. Furthermore, a novel geometric correction method based on inverse projection is utilized to eliminate the negative effects caused by the imaging processing. Simulation results and real data processing are presented to validate the proposed approach.

Due to the effects of the earth's rotation and the satellite's elliptical orbit, the Doppler centroid varies along the orbit in geosynchronous earth orbit synthetic aperture radar (GEO SAR). With an ultrahigh orbit height, the beam may illuminate outside the earth surface with a rotation angle large than 9 degrees. Therefore, the usual attitude steering methods to minimize the Doppler centroid in low earth orbit SAR (LEO SAR) may not be suitable for GEO SAR. Considering the above two effects of GEO SAR and the beam illuminating restriction, a beam determination method to minimize Doppler centroid in GEO SAR is proposed in this paper. Guaranteeing that the beam not illuminate outside the earth surface, the proposed method can drastically decrease the Doppler centroid and the equivalent squint angle.

Airborne SAR imaging processing needs to estimate motion error to compensate non-ideal trajectory. In this paper, a subaperture imaging scheme for wide azimuth beam airborne SAR systems is proposed. First, the motion error is estimated from the subaperture data and the modified range migration algorithm is applied to obtain the coarse focused image, whose azimuth resolution corresponds to the subaperture Doppler bandwidth. The subaperture image is then projected into a fine grid image, whose coordinates is defined by the imaging geometry. As the subaperture data stream is coming, the azimuth resolution of the grid image will become higher and higher. Finally, the fine image with the azimuth resolution corresponding to the full aperture data can be obtained. Since the motion error estimation is based on the sub-aperture data, the imaging processing is suitable for real-time SAR imaging.