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The performance of a deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) model largely relies on the scale and quality of training samples. However, it is time-consuming and expensive to collect sufficient data in practice. Although generative adversarial network (GAN) provides a way for SAR target image generation, existing GAN-based methods cannot confirm what features the generator learns, thus they struggle in generating precise SAR target images. In this article, we propose an angle transformation GAN (ATGAN) that can generate azimuth-controllable SAR target images while preserving the target details. The key idea of our ATGAN is to reframe the generation task from the perspective of image-to-image translation. To this end, ATGAN consists of two modules, a coarse-to-fine generator that aims to learn the angle transformation in the deep feature space, and then, apply it to manipulate the representation of an input SAR target image to generate a new one, while a spectral-normalized patch discriminator that tries to estimate the probability that an input SAR target image is real rather than fake using a patch-averaged strategy. Combining with spatial transformer and adversarial training paradigm, ATGAN can generate precise SAR target images for ATR. Extensive experiments verify the effectiveness of the proposed ATGAN, and our method outperforms the state-of-the-art method qualitatively and quantitatively.

As an important supplement to traditional airborne synthetic aperture radar (SAR), multirotor unmanned aerial vehicle (UAV) SAR has the advantages of low cost, high flexibility, and strong survival ability. However, due to the complex motion and flight characteristics of the multirotor UAV platform, multirotor UAV SAR faces challenges, including spatially variant low-frequency (LF) errors and severe high-frequency (HF) errors. To deal with these problems, an improved motion compensation approach is proposed for multirotor UAV SAR imaging, which is implemented through two processing steps. 1) The LF errors are eliminated by an improved two-step MoCo approach, which takes into account the spatial variations of both envelope and phase. 2) The HF errors are estimated and corrected by an extended phase gradient autofocus scheme. Different from conventional solutions, our approach can effectively remove the complex motion errors of multirotor UAV SAR step-by-step with high robustness even in high-resolution scenarios. Computer simulation and experimental results verify the effectiveness of our approach.

Super-resolution Direction of Arrival (DOA) estimation is a critical problem related to vehicle-borne Millimeter-wave radars that needs to be solved to realize accurate target positioning and tracking. Based on the common conditions of limited array aperture, low snapshot, low signal-to-noise ratio, and coherent sources with respect to vehicle-borne scenarios, a super-resolution DOA estimation method for a moving target with an MMW radar based on Range-Doppler Atom Norm Minimize (RD-ANM) is proposed herein. First, an array for receiving signals in the range-Doppler domain is constructed based on the radar echo of the moving target. Then, the compensation vector for the Doppler coupling phase of the moving target is designed to reduce the influence of target motion on DOA estimation. Finally, a multitarget super-resolution DOA estimation method based on the atomic norm framework is proposed herein. Compared to the existing DOA estimation algorithm, the proposed algorithm can achieve higher angular resolution and estimation accuracy owing to low signal-to-noise ratio and single snapshot processing conditions, as well as robust performance in processing coherent sources without sacrificing array aperture. The effectiveness of the proposed algorithm is proven via theoretical analyses, numerical simulations, and experiments.

The unknown relative motions between synthetic aperture radar (SAR) and a ground moving target will lead to serious range cell migration (RCM) and Doppler frequency spread (DFS). The energy of the moving target will defocus, given the effect of the RCM and DFS. The moving target will easily produce Doppler ambiguity, due to the low pulse repetition frequency of radar, and the Doppler ambiguity complicates the corrections of the RCM and DFS. In order to address these issues, an efficient ground moving target focusing method for SAR based on scaled Fourier transform and scaled inverse Fourier transform is presented. Firstly, the operations based on the scaled Fourier transform and scaled inverse Fourier transforms are presented to focus the moving targets in consideration of Doppler ambiguity. Subsequently, in accordance with the detailed analysis of multiple target focusing, the spurious peak related to the cross term is removed. The proposed method can accurately eliminate the DFS and RCM, and the well-focused result of the moving target can be achieved under the complex Doppler ambiguity. Then, the blind speed sidelobe can be further avoided. The presented method has high computational efficiency without the step of parameter search. The simulated and measured SAR data are provided to demonstrate the effectiveness of the developed method.

The imaging of high-speed high-squint synthetic aperture radar (HSHS-SAR), which is mounted on maneuvering platforms with curved trajectory, is a challenging task due to the existence of 3-D acceleration and the azimuth spatial variability of range migration and Doppler parameters. Although existing imaging algorithms based on linear range walk correction (LRWC) and nonlinear chirp scaling (NCS) can reduce the range–azimuth coupling of the frequency spectrum (FS) and the spatial variability of the Doppler parameter to some extent, they become invalid as the squint angle, speed, and resolution increase. Additionally, most of them ignore the effect of acceleration phase calibration (APC) on NCS, which should not be neglected as resolution increases. For these issues, a modified frequency nonlinear chirp scaling (MFNCS) algorithm is proposed in this paper. The proposed MFNCS algorithm mainly includes the following aspects. First, a more accurate approximation of range model (MAARM) is established to improve the accuracy of the instantaneous slant range history. Second, a preprocessing of the proposed algorithm based on the first range compression, LRWC, and a spatial-invariant APC (SIVAPC) is implemented to eliminate most of the effects of high-squint angle and 3-D acceleration on the FS. Third, a spatial-variant APC (SVAPC) is performed to remove azimuth spatial variability introduced by 3-D acceleration, and the range focusing is accomplished by the bulk range cell migration correction (BRCMC) and extended secondary range compression (ESRC). Fourth, the azimuth-dependent characteristics evaluation based on LRWC, SIVAPC, and SVAPC is completed to derive the MFNCS algorithm with fifth-order chirp scaling function for azimuth compression. Consequently, the final image is focused on the range time and azimuth frequency domain. The experimental simulation results verify the effectiveness of the proposed algorithm. With a curved trajectory, HSHS-SAR imaging is carried out at a 50° geometric squint angle and 500 m × 500 m imaging width. The integrated sidelobe ratio and peak sidelobe ratio of the point targets at the scenario edges approach the theoretical values, and the range-azimuth resolution is 1.5 m × 3.0 m.

A novel theoretical framework and method, which is based on a novel space-time-coding metasurface (STCM) with nonlinearly periodic phases, is proposed to generate frequency-modulated continuous waves (FMCWs) and dynamically control their spatial propagation behaviors simultaneously.

Due to the silent operation of the receiver station in a multistatic radar system, it is difficult for the jammer to generate the cooperative active deception target for the multistatic radar system. Making use of the spatial diversity property, a data level fusion method is proposed to counter the active deception jamming in this paper. According to the spatial correlation difference in physical target and active false target motion states, the deception velocity of the physical target, which is obtained by the radial velocity of each receiver, obeys the Gaussian distribution with zero mean, and the one of the active false target obeys the Gaussian distribution with the mean being its true deception velocity. Based on this fact, the active false target and physical target are discriminated by the deception velocity testing. The proposed deception velocity-based (DVB) method can keep a constant misjudgment probability for physical targets and discriminate active false targets effectively, especially in large deception velocity cases. The simulation verifies the feasibility and validity of the proposed discrimination method. Moreover, the proposed method can be combined with the location information association method to enhance the ability to discriminate the range–velocity joint deception of false targets.

Abstract Synthetic aperture radar (SAR) image similarity metric is at the core of SAR image interpretation techniques, however, it is still a challenging task due to complex nonlinear intensity, scale, and rotation differences between SAR images and other remote sensing images. This letter addresses this problem by proposing a novel similarity metric method for SAR images using structure and shape properties. The magnitude and orientation representation of the phase congruency model is first built based on the local phase of images. Then a new scale and rotation‐invariant local binary pattern (SRI‐LBP) descriptor is proposed using local structure and shape information. Finally, a similarity metric is defined using the symmetry Kullback Leibler divergence (SKLD) of the SRI‐LBP descriptors. Numerical experiment results verify its robustness in terms of nonlinear intensity, scale, and rotation differences.

Due to the fast scanning speed of the current phased-array radar and the moving characteristics of the target, the moving target usually spans multiple beams during coherent integration time, which results in severe performance loss for target focusing and parameter estimation because of the unknown entry/departure beam time within the coherent period. To solve this issue, a novel focusing and detection method based on the multi-beam phase compensation function (MBPCF), multi-scale sliding windowed phase difference (MSWPD), and spatial projection are proposed in this paper. The proposed method mainly includes the following three steps. First, the geometric and signal models of multiple beam integration with observed moving targets are accurately established where the range migration (RM), Doppler frequency migration (DFM), and beam migration (BM) are analyzed. Based on that, the BM is eliminated by the MBPCF, the second-order keystone transform (SOKT) is utilized to mitigate the RM, and then, a new MSWPD operation is developed to estimate the target’s entry/departure beam time, which realizes well-focusing output within the beam. After that, by dividing the radar detection area, the spatial projection (SP) method is adopted to obtain multiple-beams joint integration, and thus, improved detection performance can be obtained. Numerical experiments are carried out to evaluate the performance of the proposed method. The results show that the proposed method could achieve superior focusing and detection performances.

When synthetic aperture radar (SAR) is conducting remote sensing or terrain mapping, its radar beam is inevitably occluded by the variations in the under-test topography. Although back-projection algorithm (BPA) can theoretically directly solve the imaging problems of topography variations that most current SAR imaging algorithms cannot handle, these BPAs only solve the phase focusing of SAR echo signal, and do not consider the mismatch of SAR imaging results caused by topography occlusion. To solve the mis-imaging issue of the occluded area generated by BPA under the case of topography variation, a topography-based BPA (Topo-BPA) is proposed in this paper. Firstly, a new beam occlusion judgment algorithm based on spherical wave assumption is proposed, and its core is depression angle interpolation and depression angle updating. Then, the proposed Topo-BPA embeds the proposed beam occlusion judgment algorithm before the classical BPA, which not only did not reduce the focus depth of BPA, but improved the imaging accuracy of classical BPA. Finally, numerical experiments have demonstrated the superiority of the Topo-BPA’s performance in comparison with classical BPA.

Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection technology in this paper. The direction of arrival (DOA) of incident signals is estimated firstly by the space smoothing-based MUSIC method. The composite steering vector of multipath coherent signals is then obtained utilizing the oblique projection matrix constructed with the estimated angles. The weight vector is thereby derived with the minimum variance distortionless response criteria. The proposed oblique projection-based beamformer can receive the multipath coherent signals effectively. Moreover, the proposed beamformer is more robust and converges to optimal beamformer rapidly without aperture loss. The theoretical analysis and simulation verify the validity and superiority of the proposed coherent signal beamformer.

Thanks to the excellent feature representation capabilities of neural networks, target detection methods based on deep learning are now widely applied in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation, small targets with complex background such as islands, sea clutter, and inland facilities in SAR images increase the difficulty for SAR ship detection. To increase the detection performance, in this paper, a novel deep learning network for SAR ship detection, termed as attention-guided balanced feature pyramid network (A-BFPN), is proposed to better exploit semantic and multilevel complementary features, which consists of the following two main steps. First, in order to reduce interferences from complex backgrounds, the enhanced refinement module (ERM) is developed to enable BFPN to learn the dependency features from the channel and space dimensions, respectively, which enhances the representation of ship objects. Second, the channel attention-guided fusion network (CAFN) model is designed to obtain optimized multi-scale features and reduce serious aliasing effects in hybrid feature maps. Finally, we illustrate the effectiveness of the proposed method, adopting the existing SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0). Experimental results show that the proposed method is superior to the existing algorithms, especially for multi-scale small ship targets under complex background.

The orbital angular momentum (OAM) of a vortex electromagnetic wave (VEW) has gained attention as a newly explored information carrier. OAM modes provide vortex azimuth resolution, which is a new degree of freedom (DOF) in radar application. Due to the special characteristics of the vortex azimuth domain, VEW shares compound Doppler information of two-dimensional (2D) speed. This paper proposes a 2D target velocity estimation method for VEW radar. The Doppler effect of VEW is first analyzed. Based on the relativity of tangential speed and OAM mode, a pulse-by-pulse OAM mode-changing strategy is designed. Then, a modified Radon–Fourier transformation (RFT) is proposed to estimate the compound Doppler frequency while range migration is compensated. In addition, decoupling and ambiguity-solving procedures are applied to the compound Doppler frequency estimation to obtain tangential and radial speed estimations separately. According to the simulation analyses, the effectiveness of the proposed method is verified.

In this paper, a diverse-region hyperspectral image classification (DRHy) method is proposed by considering both irregularly local pixels and globally contextual connections between pixels. Specifically, the proposed method is operated on non-Euclidean graphs, which are constructed by superpixel segmentation methods for diverse regions to cluster irregularly local-region pixels. In addition, the dimensionality reduction method is employed to alleviate the curse of dimensionality problem with a lower computational burden, generating more representative data with the input graph features. In this context, it then constructs a superpixelwise Chebyshev polynomial graph convolution network (ChebyNet) to aggregate global-region superpixels. Benefiting from different superpixel numbers of segmentations, we construct different graph structures, and multiple classification results are obtained, which brings more opportunities to represent the hyperspectral data correctly. Then, all the diverse-region results are further fused by a majority voting technique to improve the final performance. Finally, numerical experiments on two benchmark datasets are provided to demonstrate the superiority of the proposed DRHy-ChebyNet method to the other state-of-the-art methods.

The research into curvilinear flight synthetic aperture radar (CF-SAR) is the inevitable result of the comprehensive practicality of SAR. The flight path of the SAR platform in real applications, which is highly nonlinear or curvy due to three-dimensional velocity and acceleration, cannot be described by the traditional uniform linear motion model. New mathematical models, signal characteristics, imaging algorithms, and system design criteria must be proposed and investigated for CF-SAR. This paper provides a comprehensive overview of CF-SAR. Firstly, the basic concept, unified model, and general signal characteristics of CF-SAR are defined, derived, and analyzed, respectively. Additionally, the advantages and drawbacks of current methodologies are reviewed. Discussions on the CF-SAR’s applications are presented from the perspective of typical platforms, new configurations, and advanced technologies, which are suitable means to fulfill the increasing user requirements. Finally, the challenges faced by CF-SAR are summarized, and some future trends for the study of CF-SAR are explored. Hopefully, this paper will serve as a reference for SAR researchers/engineers and stimulate the future development and actual application of CF-SAR.

The synthetic aperture radar (SAR) images of a moving target may be out of focus, given the motions of a non-cooperative target. Doppler ambiguities, including the Doppler center blur and spectrum ambiguity, will easily appear due to the limitations of pulse repetition frequency, which causes difficulty in moving-target imaging. Therefore, a robust fast Doppler ambiguity approach for SAR imaging of a ground-based moving target using range azimuth joint processing (RAJP) is presented. Firstly, the use of RAJP, based on a two-dimensional cross-correlation function and linear range cell migration (LRCM) compensation function, is proposed to simultaneously obtain the first- and second-order phase parameters in the fast-time and azimuth-frequency domains. Then, a corresponding azimuth reference function is constructed to image the moving target. Additionally, a principal component analysis-based operation is introduced to solve the mismatch with the LRCM compensation function. The couplings between the range and azimuth and between the first- and second-order parameters can be simultaneously decoupled by the proposed RAJP operation, which simplifies the processing steps. The developed approach can simultaneously obtain the first- and second-order parameters in the fast-time and azimuth-frequency domains, which avoids the propagation error of parameter estimation caused by the stepwise processing operation. The proposed method is relatively fast, given the need for fewer processing steps. The presented approach is robust in terms of Doppler ambiguity and handles the blind speed sidelobe well. In this study, simulated and real data are processed to verify the proposed approach.

Ground moving targets will typically be defocused because of the range migration (RM) and Doppler frequency migration (DFM) caused by the unknown relative motions between the platform of synthetic aperture radar (SAR) and the ground moving targets. The received signal of the ground moving target easily exhibits the Doppler ambiguity, and the Doppler ambiguity leads to the refocusing difficulty of ground moving targets. To address these problems, a SAR refocusing method of ground moving targets with Doppler ambiguity based on modified second-order keystone transform (MSOKT) and keystone transform (KT) is presented in this paper. Firstly, the second-order phase is separated by the time reversing process. Secondly, MSOKT is performed to compensate the range curvature migration and DFM, and then the coefficient of the second-order phase is estimated. Finally, a well-refocused result of the moving target is achieved after KT and the estimated Doppler ambiguity number are used to eliminate residual range walk migration. The proposed method can accurately remove RM and DFM and effectively focus the moving targets without residual correction errors. Moreover, the effects of Doppler ambiguity (including Doppler center blur and spectrum split) and blind speed sidelobe are further avoided. On the basis of the analysis of cross-term for the multiple target case, the identification strategy of spurious peak of cross-term is proposed. Additionally, the developed method can be sped up by nonuniform fast Fourier transform without the interpolation operation. The effectiveness of the proposed method is verified by both airborne and spaceborne real data processing results.

Refocusing ground manoeuvring targets with complex motions in synthetic aperture radar (SAR) remains a challenging objective because of the large range of cell migration (RCM) and time-varying Doppler frequency modulation (DFM). By exploiting the geometric information of RCM and two-dimensional (2-D) coherently integrated gain, a fast ground manoeuvring target refocusing method using principal component analysis (PCA) and high-order motion parameter estimation is proposed. First, an efficient phase difference (PD) method and PCA are utilized to correct the RCM, and then, the energy of the ground manoeuvring target is concentrated into the same range bin. Second, by utilizing the coherently integrated cubic phase function (CICPF) that was developed in our previous work, the motion parameters are obtained accurately, and the manoeuvring target is thus well refocused into a sharp peak point based on the estimated motion parameters. The proposed method is of low computational complexity because it avoids time-consuming search and interpolation operations and demonstrates an improved anti-noise performance due to fully exploiting the 2-D coherent accumulation characteristics for estimating motion parameters and enhanced refocused imaging results for manoeuvring targets due to adopting the high-order motion model. Finally, experiments are conducted using simulated and real SAR data to show the performance of the proposed method.

The synthetic aperture radar (SAR) image of moving targets will defocus due to the unknown motion parameters. For fast-maneuvering targets, the range cell migration (RCM), Doppler frequency migration and Doppler ambiguity are complex problems. As a result, focusing of fast-maneuvering targets is difficult. In this work, an efficient SAR refocusing algorithm is proposed for fast-maneuvering targets. The proposed algorithm mainly contains three steps. Firstly, the RCM is corrected using sequence reversing, matrix complex multiplication and an improved second-order RCM correction function. Secondly, a 1D scaled Fourier transform is introduced to estimate the remaining chirp rate. Thirdly, a matched filter based on the estimated chirp rate is proposed to focus the maneuvering target in the range−azimuth time domain. The proposed method is computationally efficient because it can be implemented by the fast Fourier transform (FFT), inverse FFT and non-uniform FFT. A new deramp function is proposed to further address the serious problem of Doppler ambiguity. A spurious peak recognition procedure is proposed on the basis of the cross-term analysis. Simulated and real data processing results demonstrate the validity of the proposed target focusing algorithm and spurious peak recognition procedure.

Near space is the key to integrating “sky„ and “space„ into the future. A synthetic aperture radar (SAR) that works in this area would initiate a technological revolution for remote sensing applications. This study mainly focused on ground moving target imaging (GMTIm) for a near-space hypersonic vehicle-borne SAR (NS-HSV-SAR) with squint angle. The range history, parameter coupling, and Doppler ambiguity of the squint-looking NS-HSV-SAR are more complicated than traditional side-looking airborne or space-borne SARs. Thus, a precise range model is presented on the basis of phase error analyses. Then, all potential distributions of echo’s azimuth spectrum are derived, and a GMTIm method is proposed on the basis of a detailed analysis of the echo characteristics. The proposed method consists of three steps. Firstly, a prior information-based pre-processing function was created to decrease the Doppler ambiguity and range migration effects. Secondly, a blur matched keystone transform was developed to correct the residual range walk migration. Thirdly, a time-saving chirp Fourier transform was investigated for azimuth focusing. Implementation considerations, including the curvilinear trajectory of the NS-HSV-SAR, multiple moving target imaging, and applicability and limitation of the method, are discussed. Finally, simulation results are presented to validate the effectiveness of the proposed method.