Sparse Bayesian learning (SBL) is an advanced statistical framework that dominantly enhances the sparse features of targets of interest in radar imagery. A widely adopted strategy for posterior approximation in SBL is the variational Bayesian (VB) inference, which circumvents the intractable high-dimensional integrals involved in direct posterior computation and yields closed-form expressions for the posterior moments. However, conventional VB inference suffers from computationally expensive matrix inversions, particularly in high-resolution radar imaging scenarios. To this end, a matrix-inversion-free SBL (MIF-SBL) method via improved VB inference is proposed. Within this framework, the sparsity-inducing hierarchical scheme based on the scaled Gaussian mixture model is retained, while explicit matrix inversion is replaced by solving a series of linear problems, which are efficiently handled via matrix–matrix multiplications. The proposed method reduces the computational cost by eliminating the need for direct matrix inversion, especially for the reconstruction of high-resolution radar imagery. Extensive experiments on both simulated and real radar datasets demonstrate that the proposed approach achieves improved computational efficiency while maintaining reconstruction accuracy comparable to conventional SBL.

Conventional statistical-driven synthetic aperture radar (SAR) imaging algorithms can only encode a single and/or static prior, leading to that limited features can be accessed quantitatively. To this end, a novel multi-task learning framework is proposed by devising a divide-and-conquer variational Bayesian (DC-VB) inference, so that elaborated features of interests can be exploited for a high-resolution SAR imagery. Specifically, a flexible generalized Gaussian distribution (GGD) and a customized hybrid probability distribution are introduced and employed for the priors of features of interests. To resolve the resultant complicated Bayesian inference, splitted random variables are incorporated, so that the joint posterior problem can be decomposed into multiple local problems that are easy to be solved. Simultaneously, dual variables are established for residual errors of the decomposition. To guarantee a global solution for the image of the target of interests, the data augmentation is employed to coordinate multiple local solutions. Therefore, the intended Bayesian inference works in a divide-and-conquer manner, which is superior in quantization of multiple features of high-resolution SAR imagery. It is capable of incorporating multiple priors in a fully-statistical probability and guaranteeing closed-form solutions of posterior distributions. Unavoidable propagation errors can be minimized in the DC-VB process. Raw SAR data is applied to validate the effectiveness of the proposed algorithm. Comparisons with conventions show the superiority in terms of qualitative and quantitative aspects.

By equipping the synthetic aperture radar (SAR) sensors on multiple unmanned aerial vehicles (UAVs) to form a UAV swarm (UAVS) and operate collaboratively, UAVS-SAR presents the significant advantages of rapid echoes acquisition, high imaging frame rate as well as high system survivability for advanced SAR applications. However, due to the flexible trajectories as well as the distributed configuration, the problem of the spectrum blurring in the UAVS-SAR is more complicated than that of the conventional monostatic/bistatic SAR configurations, which makes the current fast time domain algorithms (FTDAs) difficult to achieve high imaging performance. In this paper, a novel fast time domain algorithm (FTDA) is developed for UAVS-SAR imaging with both high efficiency and promising accuracy. By developing the hierarchical framework based on the designed spectrum alignment function, the imaging procedures can be realized recursively where back projection (BP) operations are reduced dramatically, and then, the total computational burden are decreased consequently. Moreover, the trajectory difference of the UAVS formation is particularly considered for practical applications, which will inevitably degrade the coherence among the sub-images from different UAV platforms. To address this problem, a correction procedure is designed according to the distributed geometrical configuration. As the coherence between the sub-images is adequately maintained, the data-driven motion compensation (MOCO) is readily developed to remove the residual phase errors to achieve desirable imaging performance. Simulations and raw data experiments are presented to validate the advantages of the proposed algorithm.

Airborne synthetic aperture radar (SAR) imagery is susceptible to non-systematic motion errors, which will definitely defocus the target image and degrade the recognition accordingly. Representations learned from conventional convolutional neural networks (CNNs) do not emphasize much about the intrinsic features of the data distribution, which collapses the purposefulness of feature extraction. To this end, a deep Merged Auto-Focusing Network (MAFNet) is proposed for adaptively removing the defocusing effect from the input SAR data used for target recognition. Specifically, we propose an end-to-end architecture consisting of one focusing module and one recognition module. The focusing module contains a U-shaped sub-network based on the cross convolution to ensure the phase history coherence of the extracted features, and a deep unfolding method is devised to improve the mathematical generalization of focusing and sparse features so as to enhance the purposefulness of feature extraction. In particular, a compact surrogate function is designed for the non-convex feature quantization problem in the focusing module, which leads to closed-form solutions. The recognition module simply consists of a classifier. By building a multivariate loss function consisting of a cross-entropy loss function and a novel focusing loss function, MAFNet achieves accurate target recognition even when the input data is contaminated by non-systematic phase errors and additive noises. MSTAR data set is utilized to validate the effectiveness of MAFNet, and comparisons with conventional algorithms are performed to demonstrate the superiority of the proposed network.

This paper proposes a novel lightweight SAR aircraft detection network (Lightweight SAR-Aircraft Network LSAN) based on YOLOv8n. In the backbone and neck part of the network, we design a lightweight feature extraction module with Efficient Multiscale Attention Module (EMA) to reduce the network parameters and computation cost. A multiscale Discrete semantic Feature Enhancement Module (DFEM) is added in the neck to enhance the capability of feature extraction. Experimental results on the China GF-3 SAR datasets demonstrate that the proposed algorithm achieves 98.1% mAP50and 82.1% mAP50-95at a speed of 122 FPS. Compared to the YOLOv8n model, LSAN shows an improvement of 1.4% in mAP50and 4.8% in mAP50-95. In particular, there is a reduction of 68.6% in the parameters and 30.8% of FLOPs. Also, experiments on other public SAR datasets demonstrate the generalization and effectiveness of our method.

Conventional sparse Bayesian learning (SBL) for synthetic aperture radar (SAR) imagery predominantly focuses on the signal model which is affected by the zero mean white Gaussian noise. Therefore, it is difficult to solve the problem of non-zero mean additive interference. In this letter, a new and non-stationary SBL (NS-SBL) algorithm is proposed, which is capable of handling the additive interference with non-zero mean and in general distributions. Specifically, to accommodate the strong direct-current power of the interference, the Gaussian mixture model (GMM) is introduced to fit the expectation, which is flexible enough to capture the complicated variation of additive interference. However, the intended additive perturbation will result in complicated likelihood distribution, which is difficult to get a fully Bayesian inference. To this end, hierarchical Bayesian inference is employed to solve the problem that the likelihood and prior are not conjugated. Further, the variational Bayesian (VB) method is adopted to overcome the difficulties in accessing closed-form solutions of the intended posteriors. Experimental results of synthetic and practical data indicate that the proposed algorithm has superior performance of anti-interference and super-resolution over other reported ones, especially under limited data and complicated environments.

Due to the sparse feature enhancement only concentrating on strong scatterers of target of interest, the conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the loss of elaborated-structure features, where weak scatterers would be overlapped by the sidelobes of strong scatterers. In this article, an elaborated-structure awareness SAR (ESA-SAR) imaging algorithm is proposed based on Hessian-enhanced total variation (HETV) regularization and cooperation. By encoding the Hessian operator onto the prior of the interested target, the high-order information connected with elaborated-structure features of interests can be captured. Different from the conventional high-order formulation that is projected onto Euclidean norm balls, the proposed algorithm uses the Schatten norm balls as the projection space, where the high-order structure tensor is established, and the elaborated-structure feature can be extracted under the intended convex regularizer. More specifically, the HETV regularizer is analytically solved under the proximal algorithm considering its nondifferentiability. An eigen-soft-thresholding (E-ST) operator is derived, so that a closed-form solution for the elaborated-structure feature can be obtained. Moreover, a synergistic multitask learning framework embedded with the sparse feature enhancement is introduced, in which the elaborated-structure feature can be solved in a cooperative manner. The cooperative learning is guaranteed in terms of both theoretical and practical aspects. Finally, both simulated and raw SAR data are processed to validate the effectiveness of the ESA-SAR algorithm. Comparisons with conventional algorithms examine the superiority of the proposed algorithm.

Fast factorized back-projection (FFBP) is a classic fast time-domain algorithm (FTDA), which is not limited by the assumption of azimuth-invariant of echo signal and is suitable for the bistatic synthetic aperture radar (BiSAR) process of arbitrary geometric configuration. However, when the conventional FFBP processing is employed for continuous imaging of multiple full-apertures, the processing efficiency will be decreased significantly, and difficulty will be introduced in motion compensation (MOCO) development. The main contributions in this article include the following two aspects: 1) a new FTDA framework based on FFBP implementation is developed for continuous imaging where echo data are divided into several full-aperture data blocks and then processed separately by FFBP implementation to reduce redundant BP operations for achieving high efficiency and 2) an efficient and effective data-driven MOCO methodology is developed based on the new FTDA framework for high focusing quality. In MOCO, because the phase error functions of subimages are estimated in the phase history domain from different local polar coordinate systems, these phase error functions are actually discontinuous in the spatial domain, which will bring significant discontinuity and defocusing into the final image. To address this problem, the correspondence of error functions between the spatial domain and the wavenumber domain is revealed based on which the phase error functions are reconstructed to remove the discontinuity for high focusing quality. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.

Conventional sparsity-driven synthetic aperture radar (SAR) imagery proceeds via ℓ1regularization, or named by least absolute shrinkage and selection operator (LASSO). However, followed by the enhanced sparse feature, structures of the scenes or targets of interests in weak scattering are easily lost. Therefore, it becomes difficult to make use of the high-resolution SAR data, even high costs have been paid for the resolution. In this paper, a novel structure-guaranteed SAR (SG-SAR) imaging algorithm is proposed by utilizing the morphology metric for the cluster feature of the scatterers of the scenes/targets of interests. By introducing structural prior in terms of morphology norm, the intended structure features can be highlighted via convex regularization. More specifically, to accommodate to complicated scenarios or targets, the structure element in the morphology regularizer is designed to be spatially variant under structure tensor representation. Different from conventional convex optimizations, the proposed SG-SAR algorithm is solved under alternating direction method of multipliers (ADMM), which is flexible to incorporate with the super-resolution imagery. In such cases, both sparse and structural features can be simultaneously enhanced, even with limited measurements and in low signal-to-noise ratio (SNR). Superior convergence and robustness can be guaranteed. Moreover, a grouping mask scheme is used to accommodate to the complex-valued SAR data. Finally, both simulated and measured SAR data are applied for the validation. Comparisons with the conventions are performed in terms of phase transition analysis, so as to verify the superiority of the proposed algorithm both qualitatively and quantitatively.

Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the problem of loss of structural features in weak scattering. Although there are algorithms that focus on structure enhancement, no proper balance between accuracy and efficiency can be achieved. In this article, a novel feature enhancement algorithm, named structure-awareness SAR (SA-SAR), is proposed by exploiting an emerging regularizer of structure tensor total variation (STV). By imposing the STV norm onto the prior of the scenes or targets to be imaged, the intended structure feature can be analytically solved under the proximal algorithm. More specifically, the regularization method is developed within the alternating direction method of multipliers (ADMM) framework, where closed-form proximity operators can be derived. Due to the ADMM framework, it facilitates to incorporate with more features to be enhanced in a fully synergistic way. Therefore, the$\ell _{1}$and entropy norms are involved so that the sparse and focusing features can be enhanced accordingly. Considering the coherence of the SAR data, a linear proximal operator is developed within the multitask learning framework. The unavoidable error propagations can be alleviated in a great extent. In such cases, the proposed algorithm is superior in terms of convergence and efficiency. To facilitate the implementation and computation, the STV proximal mapping is optimized under the Vieta theorem. Finally, both simulated and raw SAR data are applied to verify the effectiveness of the proposed algorithm. Comparisons with conventional algorithms are carried out to show the superiority of the proposed algorithm.

Conventional sparsity-driven Synthetic Aperture Radar (SAR) imagery often encounters the difficulty of extraction for complicated structural features. To this end, this paper proposes a Complex Compatible-Structure Tensor Total Variation (CC-STV) optimization algorithm, which can describes and enhances the structural feature of SAR imagery. By the introduction of multi-channel structure tensor, the CC-STV optimization algorithm can utilize detailedly local neighborhood information to describe and enhance structural features of SAR imagery when sparse features enhancement suppresses detailed structural feature. Considering the enhancement of structural features in sparsity-driven SAR images, Alternating Direction Multi-Multiplier (ADMM) algorithm is introduced to realize cooperative multi-feature enhancement. In the experiments, efficiency and superiority are validated by SAR simulated and raw data. Compared with conventions, the superiority of the proposed algorithm in terms of quantitative aspect is proven by the analysis experiment of phase transition.

Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the sensitivity of nonsystematic errors and highly computational load. In this article, a cooperative multitask learning algorithm is proposed based on an autocalibrated alternating direction method of multipliers (AutoCal-ADMM) framework, by which the sparse feature of the scenes/targets-of-interests can be enhanced, and simultaneously the nonmodeled motion errors of either airborne platform or moving target can be autocalibrated in a synergistic manner. By leveraging the entropy and sparsity regularizers in the AutoCal-ADMM framework, the proposed algorithm is particularly tailored to obtain focused SAR images with enhanced sparsity. A reasonable surrogate function is designed for a convex objective function, so that an analytical proximal mapping of the entropy regularizer can be derived. Both nonsystematic range cell migration (NsRCM) and azimuthal phase errors (APEs) are concerned and coherently compensated. A linear and complex soft-thresholding operator is introduced for the sparse solution. The proposed algorithm is capable of greatly alleviating “error propagation” between multiple tasks, where an optima balance between the sparse and focusing features can be achieved. Superior performance in terms of convergence and efficiency can be guaranteed. Both raw SAR and canonical ground moving target imaging (GMTIm) data sets are processed and comparisons with conventions are performed, where the effectiveness and superiority of the proposed AutoCal-ADMM algorithm are validated.

We propose a deep learning method for non-cross term and high-resolution time-frequency analysis. There is a tradeoff between cross term suppression and time-frequency resolution in time-frequency analysis (TFA). By exploiting the end-to-end learning property of convolutional neural network (CNN), we propose a new TFA method, which uses low-resolution short-time Fourier transform (STFT) as input, and outputs high-resolution time-frequency distribution (TFD). By stacking high-resolution instantaneous Doppler lines from different range cells, we will obtain high-resolution inverse synthetic aperture radar (ISAR) image of maneuvering target. Simulation result demonstrates the effectiveness of the proposed method.

High-resolution radar imaging will give us detailed information of target, which becomes basic function of radar systems. Improvement of image resolution of the existing radar system is also important. Based on deep learning, a new method for super-resolution through-the-radar imaging is proposed. A network, called cascade U-net (CU-net), is proposed in this paper. The results of simulation and real data experiments demonstrate the effectiveness of our methods.

Time-domain algorithms have special advantages for bistatic forward-looking synthetic aperture radar (BFSAR) applications with complex geometric configuration. In this paper, a improved fast time-domain algorithm based on orthogonal elliptical polar (OEP) coordinate is proposed for BFSAR imaging, which has prominently reduced burden in computation. In addition, the non-systematic range cell migration (NsRCM) is also analyzed and corrected in the motion compensation (MOCO) process. Simulation experiments are presented and analyzed to validate the superiority of the proposed algorithm.

For a synthetic aperture radar (SAR) system with curved trajectory, which is different from the conventional SAR, the downward velocity and the acceleration result in highly complicated range history, making it hard to achieve a focused target response. The traditional SAR imaging algorithms are not accurate enough to compensate the phase errors introduced from the highly complicated range history. In this letter, considering the impact of complex range history, an improved hyperbolic range equation is proposed to access the 2-D spectrum for curved trajectory SAR imaging. Based on the derived spectrum, frequency-domain imaging algorithm can be performed to focus targets. By analyzing the phase error and comparing with other current range models, the proposed range model is proved to be precise enough to deal with the complex motion model happened in curved trajectory SAR imaging. Simulation experiments are implemented to evaluate the imaging performance of the proposed approach.

In this letter, a new spectrum-oriented fast factorized backprojection (FFBP) algorithm is proposed for synthetic aperture radar (SAR) imaging on a maneuvering platform. Specifically, an analytical SAR image spectrum is derived in a novel quasi-polar coordinate system based on the FFBP, which makes it easy to incorporate with an autocalibration process for both systematic and nonsystematic errors. Different from the conventional FFBP algorithms developed in polar grid, the proposed algorithm devised in quasi-polar gird conducts the motion-induced phase error as a space-invariant component, which will definitely facilitate the phase autofocusing process during the FFBP recursions. Subsequently, a phase autofocusing process is incorporated in the resultant SAR image formation algorithm. Simulations and discussions are presented to show the focusing quality improvement made by the proposed algorithm.

This paper deals with the issue of ground moving target imaging and motion parameter estimation with an airborne dual-channel circular stripmap synthetic aperture radar (CSSAR) system. Although several methods of ground moving target motion parameter estimation have been proposed for the conventional airborne linear stripmap SAR, they cannot be applied to airborne CSSAR because the range history of a ground moving target for airborne CSSAR is different than that for airborne linear stripmap SAR. In this paper, the moving target's range history for airborne dual-channel CSSAR and the target signal model after the displaced phase center antenna processing are derived, and a new ground moving target imaging and motion parameter estimation algorithm is developed. In this algorithm, the estimation of baseband Doppler centroid and its compensation are first performed. Then focusing is implemented in the 2-D frequency domain via phase multiplication, and the target is focused in the SAR image without azimuth displacement due to the compensation of the Doppler shift caused by its motion. Finally, the target's motion parameters are estimated with its Doppler parameters and its position in the SAR image. Numerical simulations are conducted to validate the derived range history and the performance of the proposed algorithm.

Because of flexible geometric configuration and trajectory designation, time-domain algorithms become popular for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) applications. In this paper, a new quasi-polar-coordinate-based fast factorized back-projection (FFBP) algorithm combined with data-driven motion compensation is proposed for miniature UAV-SAR imaging. By utilizing wavenumber decomposition, the analytical spectrum of a quasi-polar grid image is obtained, where the phase errors arising from the trajectory deviations can be conveniently investigated and the phase autofocusing can be compatibly incorporated. Different from the conventional FFBP based on a polar coordinate system, the proposed algorithm operates in a quasi-polar coordinate system, where the phase errors become spacial invariant and can be accurately estimated and easily compensated. Moreover, the relationship between phase errors and nonsystematic range cell migration (NsRCM) is revealed according to the analytical image spectrum, based on which the NsRCM correction is developed to further improve the image focusing quality for high-resolution SAR applications. Promising experimental results from the raw data experiments of miniature UAV-SAR test bed are presented and analyzed to validate the advantages of the proposed algorithm.

Recently, compressive sensing theory has been successfully applied in inverse synthetic aperture radar (ISAR) imaging. However, the issue of maneuvering target imaging from compressive sampling data has not been sufficiently addressed because it is difficult to jointly deal with both sparse imaging and motion compensation under compressive sampling. In this paper, we develop a novel algorithm of high-resolution ISAR imaging for maneuvering targets from compressive sampling data. In this algorithm, a non-uniform scaled Fourier dictionary is constructed to represent the maneuverability. A hierarchical statistical model is utilized to encode the sparsity of ISAR image. Then, ISAR imaging joint with motion estimation is solved by using a parametric sparse Bayesian leaning (P-SBL) method, including sparse imaging and dictionary learning. Finally, experiments are performed to confirm the effectiveness of the proposed method by using the simulated and measured data.

In this paper, the analytic expression of synthetic aperture radar (SAR) image spectrum in the polar grid system is derived based on the wavenumber analysis. By revealing the relationship between wavenumber variable and image spectrum in the polar system, we can better understand the mechanism of fast factorized BP (FFBP) processing. Moreover, the form of phase error in spectral domain can be possibly revealed which will facilitate motion compensation and autofocusing in FFBP processing. Simulation results are presented and analyzed to demonstrate the validity of the derived spectrum.

Forward velocity extraction is a very important process for obtaining a high-quality unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) image. Because of the constraints of low flying altitude and small platform size, the flight path of the UAV is easily disturbed by the atmospheric turbulence. The complex motion error of the UAV's flight path makes the forward velocity difficult to be extracted from raw SAR data. To address this problem, an adaptive notch filtering (ANF)-based approach for forward velocity extraction is proposed. Based on the kinetic characteristics of the UAV, the variation of Doppler centroid frequency is analyzed and exploited to remove most components of the cross-track acceleration in the low-frequency range. Then, by regarding the forward velocity component as a narrow-band component, ANF processing is employed to extract it from the estimated Doppler rate. Comparing with the methods reported in the literature, the ANF method can achieve higher accuracy and efficiency due to its excellent notching performance and strong suppression for narrow-band signals. Promising results from raw data experiments are presented to demonstrate the validity and superiority of the proposed method.

In this paper, a novel synthetic aperture radar (SAR) ground moving target imaging (GMTIm) algorithm is presented within a parametric and dynamic sparse Bayesian learning (SBL) framework. A new time-frequency representation, which is known as Lv's distribution (LVD), is employed on the moving targets to determine the parametric dictionary used in the SBL framework. To combat the inherent accuracy limitations of the LVD and extrinsic perturbation errors, a dynamical refinement process is further developed and incorporated into the SBL framework to obtain highly focused SAR image of multiple moving targets. An emerging inference technique, which is known as variational Bayesian expectation-maximization, is applied to achieve an efficient Bayesian inference for the focused SAR moving target image. A remarkable advantage of the proposed algorithm is to provide a fully posterior distribution (Bayesian inference) for the SAR moving target image, rather than a poor point estimate used in conventional methods. Because of utilizing high-order statistical information, the error propagation problem is desirably ameliorated in an iterative manner. The perturbations, known as the multiplicative phase error and additive clutter and noise, are both well adjusted for further improving the image quality. Experimental results by using simulated spotlight-SAR data and real Gotcha data have demonstrated the superiority of the proposed algorithm over other reported ones.

This paper presents a new SAR ground moving target imaging (GMTIm) algorithm based on an unified framework of Keystone transformation (KT). To combat the inherent range-azimuth coupling, an tandem two-step strategy is designed, where the range decoupling is implemented by polar format algorithm (PFA) and the azimuth decoupling is finished by an novel time-frequency representation method that is Lv's distribution (LVD). We show, mathematically, that the azimuth resampling of PFA has inherently the same mechanism as the KT, and also, the LVD achieves the optimal performance when it is performed in accordance with the KT principle. Therefore, multiple moving targets can be imaged simultaneously. Focused targets' responses can be obtained in both range and azimuth dimensions. Isotropic point target simulation is designed, and experiments are carried out to validate our proposed SAR-GMTIm algorithm.

This letter presents a new method of cross-range scaling in inverse synthetic aperture radar (ISAR) imaging. The effective rotational velocity (ERV), being the crucial factor for scaling, is generally unknown for noncooperative objects. By considering the degradation from target rotation, the proposed scheme estimates ERV based on image sharpness maximization. A range deviator induced by the center shift is also embedded in the estimation process. The cross-range scaling factor with an enhanced ISAR image can be obtained by an efficient Gauss-Newton method. The results acquired from both the simulations and real data experiments validate the effectiveness and robustness of the proposed method.

This paper presents a new ground moving target imaging (GMTIm) algorithm for airborne synthetic aperture radar (SAR) based on a novel time-frequency representation (TFR), Lv's distribution (LVD). We first analyze generic moving target signatures for a multichannel SAR and then derive the analytical spectrum of a point target moving at a constant velocity by a polar format algorithm for SAR image formation. SAR motion deviation from a predetermined flight track is considered to facilitate airborne SAR applications. LVD, as a recently developed TFR for the analysis of multicomponent linear-frequency-modulated signal, is adopted to represent the target kinematic spectrum in the Doppler centroid frequency and chirp rate domain. As a result, the proposed SAR-GMTIm algorithm is capable of imaging multiple moving targets even when they are located at the same range resolution cell. Some practical issues such as imaging maneuvering targets and small/weak targets are discussed to enhance the applicability of the proposed algorithm. Simulation results with isotropic point moving targets are presented to validate the effectiveness and superiority of the proposed algorithm. Raw data collected by an airborne multichannel SAR are also used to verify the performance improvement made by the proposed algorithm.

This paper presents a novel inverse synthetic aperture radar (ISAR) imaging method by exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced target images within a Bayesian framework. A simplified radar system is utilized by transmitting the sparse probing frequency signal, where the ISAR imaging problem can be converted to deal with underdetermined linear inverse scattering. Following the Bayesian compressive sensing (BCS) theory, a hierarchical Bayesian prior is employed to model the scatterers in the range-Doppler plane. In contrast to the independent prior on each scatterer in the conventional BCS, a correlated prior is proposed to statistically encourage the continuity structure of the scatterers in the target region. To overcome the intractability of the posterior distribution, the Gibbs sampling strategy is used for Bayesian inference. The parameters of the signal model are inferred efficiently from samples obtained by the Gibbs sampler. Because the proposed method is a data-driven learning process, the tedious parameter tuning process required by the convex optimization-based approaches can be avoided. Both the synthetic and the experimental results demonstrate that the proposed algorithm can achieve substantial improvements in the scenarios of limited measurements and low signal-to-noise ratio compared with other reported algorithms for ISAR imaging problems.

For inverse synthetic aperture radar imagery, the inherent sparsity of the scatterers in the range-Doppler domain has been exploited to achieve a high-resolution range profile or Doppler spectrum. Prior to applying the sparse recovery technique, preprocessing procedures are performed for the minimization of the translational-motion-induced Doppler effects. Due to the imperfection of coarse motion compensation, the autofocus technique is further required to eliminate the residual phase errors. This paper considers the phase error correction problem in the context of the sparse signal recovery technique. In order to encode sparsity, a multitask Bayesian model is utilized to probabilistically formulate this problem in a hierarchical manner. In this novel method, a focused high-resolution radar image is obtained by estimating the sparse scattering coefficients and phase errors in individual and global stages, respectively, to statistically make use of the sparsity. The superiority of this algorithm is that the uncertainty information of the estimation can be properly incorporated to obtain enhanced estimation accuracy. Moreover, the proposed algorithm achieves guaranteed convergence and avoids a tedious parameter-tuning procedure. Experimental results based on synthetic and practical data have demonstrated that our method has a desirable denoising capability and can produce a relatively well-focused image of the target, particularly in low signal-to-noise ratio and high undersampling ratio scenarios, compared with other recently reported methods.

When the range migration caused by motion error exceeds the range cell resolution, the performance of a conventional phase autofocus approach degrades. In this paper, a new adaptive motion compensation (MoCo) algorithm with the removal of the migration that is nonsystematic has been developed for airborne spotlight synthetic aperture radar (SAR) imagery with high resolution. In the algorithm, the relationship between nonsystematic range cell migration (NsRCM) and phase error was first explicitly revealed after the polar format algorithm resampling. The NsRCM could be readily calculated by coarse but reliable phase error estimation. Subsequently, the NsRCM and the bulk of the azimuth phase error were corrected. After the removal of the NsRCM, degradation of the conventional phase autofocus resulting from sidelobe increase as well as mainlobe broadening was avoided. Finally, a fine MoCo procedure was performed to remove the residual azimuth phase error satisfactorily. Through the analysis of the airborne spotlight SAR raw data with high-resolution and wide-swath illumination, a well-focused imagery was obtained. Quantitative assessment of the image quality was satisfactory. The MoCo algorithm was validated.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, a novel databased motion compensation (MOCO) approach is proposed for the UAV SAR imagery. The approach is implemented by a three-step process: 1) The range-invariant motion error is estimated by the weighted phase gradient autofocus (WPGA), and the nonsystematic range cell migration function is calculated from the estimate for each subaperture SAR data; 2) the retrieval of the range-dependent phase error is executed by a local maximum-likelihood WPGA algorithm; and 3) the subaperture phase errors are coherently combined to perform the MOCO for the full-aperture data. Both simulated and real-data experiments show that the proposed approach is appropriate for highly precise imaging for UAV SAR equipped with only low-accuracy inertial navigation system.

In this letter, a two-channel airborne experimental interferometric synthetic aperture radar (InSAR) designed for terrain height estimation is exploited to acquire the ability of ground moving target indication (GMTI). Due to the hybrid baseline of this system, the interferometric phase changes with the target motion as well as the terrain height. The fluctuation of the interferometric phase worsens the performance of the clutter suppression and the radial velocity estimation. In order to resolve this problem, a GMTI method with three steps is proposed. After two steps are used to eliminate the local flat-Earth phase and the cross-track interferometric phase of the scene, respectively, an adaptive filtering method is used to suppress the stationary clutter with the benefit of calibrating the sensor responses. A conventional constant false alarm rate detector is then used to indicate the moving targets. The validity of the proposed method is demonstrated with real data collected using an experimental airborne InSAR system.