The reconstruction of the three-dimensional structure of synthetic aperture radar (SAR) targets is a hot and difficult issue in the field of SAR. Conventional 3-D reconstruction methods based on 2-D SAR images do not consider the inherent characteristics of SAR imaging such as geometric deformation, overlap, and occlusion, and can only reconstruct simple and regular targets. To address this, we propose a CNN-based SAR 3-D reconstruction method called Multi-aspect SAR Stereo Network (MASS-Net). Our network is an end-to-end deep learning architecture that can automatically complete dense matching among multi-aspect SAR images and calculate the height to obtain height maps by learning prior knowledge. In the network, a feature extractor based on CNN is constructed to extract features from SAR images, which can extract high-dimensional features of 2-D SAR images, and help to capture neighborhood information and overcome the influence of geometric deformation and occlusion. Then a differentiable SAR projection relationship is established to construct a cost volume that includes the differences in multi-aspect image features. This projection relationship ensures ensures the overall differentiability of our pipeline. Meanwhile, the encoding and decoding architecture based on 3-D CNN is utilized to achieve regularization and regression to generate height maps. Finally, we use multi-aspect height maps to construct a dense 3D point cloud of the target. These make MASS-Net efficient and effective. Compared with traditional methods, our method can address issues such as distortion and occlusion, and efficiently reconstruct dense and accurate 3-D point cloud of complex targets. Simulation experiments and actual measurement experiments have been conducted to verify our proposed method.

With the increasing number of electromagnetic devices, radio frequency interference (RFI) suppression has gradually become an essential problem in synthetic aperture radar (SAR) imaging. Faced with complex RFI environments such as time-varying and multitype mixing that may occur, traditional approaches often result in inadequate suppression and loss of valuable echoes. Moreover, the representation ability of manually extracted features is limited, struggling to maintain consistent performance in complex electromagnetic environments. To tackle these challenges, this article proposes a feature-enhanced low-rank and sparse decomposition network (FELS-Net), which separates RFI and useful echoes in the time-frequency domain (TFD). We introduce two learnable invertible nonlinear transforms to enhance the representation of RFI and SAR echoes, and unfold the RFI suppression scheme based on low-rank and sparse decomposition into a parameter-learnable network structure. The strong interpretability of model-driven architecture offers a potential stability guarantee for RFI suppression performance, while deep learning (DL) contributes to more effective feature characterization and more efficient and robust parameter schemes. Experimental results demonstrate that the proposed method outperforms comparative approaches in both time-frequency (TF) and image domains, exhibiting robust performance across diverse experimental conditions.

Bistatic Synthetic Aperture Radar (BiSAR) enables highly flexible configuration, offering broad application prospects. However, existing BiSAR imaging algorithms neglect the complex scattering characteristics of the target, resulting in the loss of target structural information in the imaging results. To enhance the target structural information in imaging results and improve the interpretability of BiSAR images, we first establish the BiSAR echo model based on the target scattering model and analyze the target’s imaging characteristics by incorporating the imaging mechanisms. Subsequently, based on the imaging characteristics, we propose a structure-driven multi-stage BiSAR trajectory planning method (SMTP). This method solves a multi-stage multi-objective optimization problem driven by target scattering characteristics, thereby fully presenting all discernible structural features in the imaging results. Numerical simulation experiments validate the proposed method, demonstrating its ability to recover target structural information. This approach addresses the gap where BiSAR mission planning has largely overlooked target-specific characteristics.

Range sidelobe is a common and widely concerned issue in synthetic aperture radar (SAR) imaging. The sidelobe of strong scatters could cover weak targets and reduce the quality of SAR images, increasing the difficulty in interpretation. Due to the principle of energy conservation, reducing the sidelobe will reduce the range resolution of SAR for the single waveform design. By taking full advantage of the degrees of freedom of the transmitter, transmitting multiple waveforms, and exploiting the information discrepancy between pulses, we can overcome this limitation. Complementary sequences are a typical example since the range sidelobe of the sequences can be theoretically summed up to zero. Due to the transmission of different waveforms between pulses, an amplitude-phase modulation will be introduced in the azimuth dimension of SAR echo, which, in turn, leads to the Doppler spectrum aliasing and unexpected energy spikes outside the main energy region of SAR point spread function (PSF). Seeking to suppress the range sidelobe and meanwhile mitigate the spikes caused by waveform agility in SAR, we first establish the expression of the echo under the agile transmitting mode. Then, we propose an SAR PSF shaping (SAR-PSFS) method to suppress the integrated sidelobe level (ISL) of the whole range sidelobe plane in the SAR image via complementary waveform optimization. The inexact alternating direction penalty method (IADPM) framework is adopted to solve the resulting nonconvex optimization problem. Simulation results show that the proposed method outperforms the nonlinear frequency-modulation (NLFM) signal with a 5.46-dB lower integrated sidelobe ratio (ISLR) under the same mainlobe width. Besides, the range sidelobe can be effectively canceled with a 3.26-dB lower peak sidelobe ratio (PSLR) and a 4.99-dB lower ISLR compared with the Hanning window while maintaining the range resolution with the spikes caused by waveform agility effectively mitigated.

Multistatic three-dimensional synthetic aperture radar (3D SAR) has shown significant potential in rapid 3D imaging. Compared to traditional multi-pass or array 3D imaging systems, it achieves high-resolution imaging in a single pass. However, due to the introduction of multiple radar systems, decoherence factors such as multi-channel and synchronization cause serious degradation in data quality, posing challenges for accurate reconstruction. To address this issue, this paper proposes a data recovery algorithm based on matrix completion (MC) for 3D imaging of structured targets. The structural characteristics of architectural targets introduce a low-rank property into the data, stemming from the inherent correlation among adjacent pixels. Utilizing the principle of matrix completion, combined with the sparsity of scattering points in elevation, a low-rank and sparse joint completion model is established. Furthermore, the Truncated Schatten-p Norm and Sparse Regularizer-Alternating Direction Method of Multipliers (TSPN-ADMM) algorithm is adopted for solving. Additionally, considering that this recovery method reconstructs the 2D complex image, a Filter-MC processing framework is proposed to further enhance the performance. Finally, both simulation and real data verify the effectiveness of the proposed recovery method and framework.

With multiple channels in azimuth receiving echoes, multichannel radar has the potential of forward-looking imaging, and various schemes can be formulated. However, due to the different resources utilized by different imaging schemes, the angular resolution will be different. How to analyze the angular resolution and then design appropriate parameters is a key issue in multichannel radar forward-looking imaging. In this paper, based on the echo model of forward-looking imaging, the imaging schemes of synthetic aperture and real aperture are illustrated firstly. Then, based on the ambiguity function theory, the angular ambiguity functions, and the analytical expressions of the angular resolution for different imaging schemes are derived and analyzed. Finally, the simulation results of point targets and extended targets are presented to verify the effectiveness of the theoretical analysis, which would lay a significant foundation for the design of forward-looking imaging schemes of multichannel radar.

Distributed synthetic aperture radar (SAR) tomography has recently aroused extensive attention due to its 3-D imaging capability and flexibility. However, atmospheric turbulence brings in motion errors, and collaborative work of multiple platforms increases the degree of freedom (DoF) for motion errors and introduces time-frequency synchronization errors. These undesired errors result in severe 2-D image defocusing, making SAR tomography 3-D imaging unattainable. To address these issues, a novel 3-D focusing scheme that can simultaneously compensate for high DoF motion errors and time-frequency synchronization errors is proposed. Firstly, the RCM offset induced by time synchronization errors is corrected through preprocessing. Then, image-quality-based 2-D autofocus is utilized to achieve 2-D focusing. Finally, the effects of linear phase and constant phase introduced by 2-D autofocus are eliminated by image registration and elevation autofocus based on maximizing image sharpness, respectively. Simulation results demonstrate the effectiveness of the proposed 3-D focusing scheme.

Microwave Photonic Synthetic Aperture Radar (MWP-SAR) holds significant promise for applications in Earth remote sensing owing to its exceptional imaging resolution. This radar technology emits ultra-wideband signals surpassing those of conventional radar systems. Hence, MWP-SAR exhibits the potential to generate pseudo-color images by exploiting scattering differences, thereby augmenting the information acquisition capabilities of MWP-SAR. This paper introduces a methodology for synthesizing pseudo-color images while preserving the high resolution of MWP-SAR. The proposed algorithm employs an optimization technique to identify subband echo channels exhibiting the most significant differences in scattering characteristics. Simultaneously, to safeguard the resolution of MWP-SAR, a fusion model is devised to integrate the full-resolution Synthetic Aperture Radar (SAR) image with the multi-subband image. Finally, a full-resolution pseudo-color image was successfully synthesized from the measured airborne MWP-SAR data.

Sea clutter suppression plays a crucial role in maritime moving target indication. However, in the bistatic SAR (BiSAR) system, traditional space-time adaptive processing (STAP) method can’t satisfy the expected performance due to severe range cell migration (RCM), Doppler frequency migration (DFM), nonstationary clutter, and spatio-temporal spectrum expansion caused by the internal motion of sea clutter. To issue these problems, a matrix sparse model based spatio-temporal spectrum recovery method is proposed. The proposed method mainly consists of three steps. Firstly, Generalized Keystone transform in preprocessing stage is used for RCM correction and DFM compensation. Then, multiple spatio-temporal samples acquisition strategy for cell under test is designed, to enhance the solution robustness. Next, a matrix sparse model for BiSAR spatio-temporal spectrum recovery is constructed and solved. Finally, the sea clutter suppression performance is verified with numerical simulations.

Synthetic aperture radar (SAR) usually uses linear frequency modulation (LFM) signal to image the target and clutter signal, which often causes the target to be overwhelmed by strong clutter. To solve this problem, we propose a waveform design method which can reduce the clutter and guarantee the SAR imaging performance. Firstly, the spectrum mask constraints of signal-to-clutter ratio (SCR) are obtained according to the scene prior information, and then the unimodular waveform of minimizing weighted integral sidelobe level (WISL) is designed. The final result is the SAR transmit waveform with spectral constraints. Simulation results show that the design can effectively achieve target enhancement and clutter suppression while maintaining SAR imaging performance.

High-resolution ship Inverse Synthetic Aperture Radar (ISAR) imaging is vital for identifying ship targets by revealing their contours and detailed features. However, the spatial-variant Doppler frequency caused by unknown motion parameters often leads to defocused ISAR images. Traditional Fourier-Transform-based methods struggle to handle the three-dimensional spatial-variant Doppler frequency (3-D SVDF). CLEAN-based methods, capable of estimating 3-D SVDF, often sacrifice target details. To overcome these hurdles, this paper proposes a refined ship ISAR imaging method based on Fractional Fourier Transform (FRFT). Employing FRFT, the method estimates multiple Doppler parameters and segregates scattering points into spatial-invariant sub-images based on their Doppler similarity. Phase compensation is then applied to each sub-image, enabling compensation for ship motion and extracting detailed ship target information in high-resolution ISAR images. Simulation results affirm the algorithm's efficiency in achieving image focusing and revealing intricate ship target details.

Space-borne passive radar (PR) exploits non-cooperative signals of satellite constellations, specifically Low-Earth-Orbit (LEO) communication satellites. It offers advantages such as low intercept probability and reduced costs. However, utilizing LEO signals presents challenges as multiple similar signals from different satellites can interfere without identification. This paper proposes a method for LEO communication signal separation and target detection leveraging synchronization signal blocks (SSB) for discrimination and identification, generating a reference signal for subsequent processing to transfer target information to the range-Doppler domain. The final integration result enables the detection and localization of the target. The effectiveness of the proposed method is validated through simulated experimental results.

Synthetic Aperture Radar (SAR), with its all-weather and all-day operation, is an effective tool for earth observation. However, with the continuous intensification of global changes, current earth observation methods face challenges in meeting the demands for global coverage and timeliness. Moon-based SAR (MBSAR) has the advantage of long observation time and wide coverage. However, the imaging capability of MBSAR is limited by the orbit characteristics of the moon. Using the moon as the illumination source and the high-earth orbit (HEO) satellite as the receiving station (MH-BISAR) not only allows for a wide imaging area and flexible viewing angle, but also can effectively reduce the signal transmission power. This paper first establishes the motion model of MH-BISAR in a unified coordinate system, then calculates the basic conditions such as the required transmit power for MH-BISAR and compares it with MBSAR. Next, the imaging capabilities of MH-BISAR and imaging time of global areas within one month are analyzed. Finally, it is concluded that MH-BISAR has the advantages of high resolution, long observable time, large imaging coverage, and low system requirements for earth observation. It can serve as a powerful means for earth observation.

Compared with the traditional tomographic synthetic aperture radar (TomoSAR) system, multistatic TomoSAR can overcome the physical size limitation, realizing high-resolution 3-D imaging via single pass. However, due to the minimum safety distance limitation between flight platforms, the maximum unambiguous imaging range of multistatic SAR is relatively small, which is difficult to meet the mapping needs of urban high-rise. To address this problem, this paper proposes a multistatic TomoSAR ambiguity suppression method based on multiple subbands. This method can effectively achieve ambiguity suppression and 3-D reconstruction of the target without grating lobes. First, a multistatic TomoSAR imaging model is established. Second, we analyze the mechanism of multiple subbands ambiguity suppression. Finally, we utilize the adaptivity of the sparsity Bayesian recovery via iterative minimum (SBRIM) algorithm to the number of targets to realize multistatic TomoSAR 3-D imaging. The effectiveness of the proposed method is validated by simulations.

In complex synthetic aperture radar (SAR) imaging configurations, such as bistatic SAR, time-domain algorithms are less constrained and more accurate than frequency-domain algorithms. But they have not be applied to real-time imaging well because of large computation. The back-projection algorithm based on wavenumber-domain spectral splicing (WFBP) that emerged recently can be used to resolve this contradiction. In this paper, an efficient implementation architecture of this algorithm is designed based on FPGA. Parallel structures are used for sub-aperture BP imaging and images fusion. The imaging results and speed of the system is verified by simulations and experiments. It runs WFBP over a image of size 1024×512 in 0.131s, significantly accelerating imaging while maintaining accuracy of time-domain algorithms.

In high sea states, ship targets often exhibit intense and complex rotational movements due to sea waves, leading to defocused and time-varying imaging results for bistatic synthetic aperture radar (SAR). This paper introduces a novel strategy for selecting optimal imaging times of ship targets by analyzing image projection plane (IPP). Utilizing short-time Fourier transform (STFT) on target echoes, rotation parameters of the ship are estimated via differential evolution (DE) method, to determine IPP throughout the entire observation period, so that the optimal imaging moments can be selected. By comparison of image contrasts and entropies, the optimal imaging time lengths are also refined. Overall, this paper offers an optimal time section strategy ensuring clear views of ship targets, and simulation results verify its effectiveness.

With the increasing widespread use of synthetic aperture radar(SAR) systems in various environments, jamming have become a serious problem that they face. In many situations, these jamming affect SAR systems’ ability to gather information. Many anti-jamming methods are based on the classification of jamming types. In order to provide information about jamming types, it is necessary to design a classification method which can classify multiple types of jamming. Considering the complexity of jamming features, in this paper, multi-domain jamming feature analysis and a residual network with convolutional block attention module (CBAM) are proposed to classify SAR jamming. To train this jamming classification network and validate its effectiveness, a database containing different jamming simulations is generated. The simulation results show that this method has reliable classification performance for various types of jamming.

Airborne multistatic synthetic aperture radar (SAR) shows various advantages such as high flexibility and target observation from different perspectives regarding localization. However, due to the separation of transmitters and receivers, the time-frequency synchronization error is induced and the traditional Range-Doppler (R-D) localization method is unreliable because of challenges in Doppler centroid estimation. Furthermore, there is a position error in the position of the unmanned aerial vehicle (UAV) as a high-maneuvering platform. This paper proposes a novel localization method for airborne multistatic SAR based on time difference of arrival (TDOA). This method only uses range information extracted from received signals without Doppler centroid estimation for TDOA localization equation construction. Then the localization equation is solved by the Taylor algorithm for the target location, which is of good robustness to the time-frequency synchronization error and the UAV position error. Numerical simulations validate the effectiveness of this method.

Bistatic synthetic aperture radar (BiSAR) can provide high-resolution images and effectively observe from various visual angles, enabling automatic target recognition. Estimation of target scattering parameters is one of the important key techniques in target recognition and super-resolution imaging. The compressed sensing(CS)-based methods have been widely used in SAR imaging. However, the hyperparameters of these methods are often difficult to be set to optimal, which leads to inaccurate estimation results. To solve this problem, this paper proposes a BiSAR target scattering parameter estimation method. First, the echo model is established based on the point scattering model and three typical scattering primitives to characterize the different scattering characteristics of different targets. Second the scattering parameter estimation problem is transformed into the optimization problem, and the alternating direction method of multipliers(ADMM) is introduced to estimate target parameters. There are four regularization parameters in the optimization problem, which are automatically set to the optimum by combining with the differential evolution(DE) algorithm. Then the estimated result is obtained by optimization with the optimal regularization parameters. Numerical simulation experiments demonstrate the effectiveness of the proposed method.

Multipass synthetic aperture radar (SAR) tomography is a promising method for 3-D imaging. However, atmospheric turbulence-induced motion errors lead to severe 2-D defocusing and hinder 3-D imaging. The spatial-variant characteristic of motion errors further complicates motion compensation. To address this issue, an improved autofocus method is proposed, which achieves 2-D focusing by reconstructing trajectory. Firstly, a spatial-variant phase error compensation model is established to elucidate the correlation between motion errors and 2-D image quality. Subsequently, to maximize image sharpness, motion errors estimation is performed using a combination of the block coordinate descent (BCD) algorithm and the gradient descent (GD) algorithm. Finally, image registration is applied to correct 2-D image offset induced by an image-quality-based optimization strategy. Simulation results confirm the effectiveness and superiority of the proposed method.

Video synthetic aperture radar (SAR) has attracted increasing attention in recent years due to its ability to provide continuous images for the scenes of interest. However, practical applications of video SAR are limited by the large amount of data and computational costs involved in imaging. In this paper, we propose a deep unfolding network for reconstructing SAR videos from undersampled echo data. Firstly, we introduce a low-rank representation operator to perform low-rank representation on the video SAR tensor. Then the problem of reconstructing video SAR is modeled as a regularization problem based on low-rank representation and solved by the alternating direction method of multipliers (ADMM) algorithm iteratively. Finally, we unfold the iterative solution into a deep neural network to learn the network parameters and low-rank representation operator from the data. Simulation experiments validate the effectiveness of the proposed method.

Sea clutter suppression plays a crucial role in maritime moving target indication. However, in the bistatic SAR (BiSAR) system, traditional space-time adaptive processing (STAP) method can’t satisfy the expected performance due to severe range cell migration (RCM), Doppler frequency migration (DFM), nonstationary clutter, and spatio-temporal spectrum expansion caused by the internal motion of sea clutter. STAP based on sparse recovery (SR-STAP) is an effective method for clutter suppression, but two major problems still remain. (1) The multiple samples for solution need to satisfy the same spatio-temporal distribution characteristics. Nevertheless, such consistency is not applicable when considering violent internal motion of sea clutter. (2) The computational complexity is exceedingly high. To issue these problems, an efficient matrix sparse recovery STAP (MSR-STAP) method based on Kronecker transform is proposed. The proposed method mainly consists of three steps: (1) Generalized Keystone transform in preprocessing stage is used for RCM correction and DFM compensation. (2) Multiple spatio-temporal samples acquisition strategy for CUT is designed, to enhance the solution robustness. (3) An efficient MSR-STAP model is established and solved. Subsequently, the space-time filter is designed without clutter covariance matrix estimation, to facilitate effective sea clutter suppression. Compared with existing SR-STAP methods, computational complexity of the proposed method decreases by orders of magnitude, and the spatio-temporal spectrum expansion effect is greatly reduced. The sea clutter suppression performance is verified with numerical simulations.

Localization and tracking are important components for ground-moving target indication (GMTI). The traditional range-Doppler (RD) localization model is always applied to locate stationary targets in bistatic synthetic aperture radar (BiSAR). However, for moving targets, this localization model is no longer applicable due to the strong coupling of position and velocity with Doppler frequency. To address the severe challenge, this article proposes a joint moving target localization and tracking method for BiSAR-GMTI via transmitter–receiver trajectories extraction and inversion. The key innovation is the derivation of closed-form localization results for moving targets, which is beneficial for the quantitative analysis of localization error. The proposed method is structured into three main parts. First, to accurately capture the range history trajectory information, a segmented fitting extraction and inversion framework is designed. Second, to estimate the moving target’s initial state and demonstrate localization observability, joint range localization equations are established where the closed-form localization result of the moving target is derived. Finally, by defining the appropriate state transition equation and observation equation, and incorporating particle filtering (PF), the position measurement errors of bistatic platforms are weakened, and accurate tracking of moving targets is realized. The proposed method only utilizes range information, mitigating localization errors caused by Doppler estimation inaccuracies effectively. Simulation experiments and real data both demonstrate the localization and tracking accuracy of moving targets.

Radar forward-looking imaging has important applications in autonomous landing, autonomous navigation, reconnaissance guidance and other fields. However, conventional single channel synthetic aperture radar (SAR) or Doppler beam sharpening (DBS) technology has a blind area for forward-looking imaging due to left/right ambiguity and small angle variation. Multichannel radar can utilize the differences of echoes from multiple channels in azimuth to resolve left/right ambiguity, and has the potential for forward-looking imaging. However, there is still a problem of low azimuth resolution due to the restriction of array size. In this article, a deep learning based multichannel radar forward-looking super-resolution imaging framework is proposed. In this framework, synthetic aperture processing is conducted on the echo data of each channel to obtain the image with left/right ambiguity, and the preliminary forward-looking imaging is achieved first by resolving left/right ambiguity with multichannel data. Then, the generative adversarial network (GAN)-like network with mixed attention mechanism is designed to learn the mapping relationship between the original scene and the preliminary imaging result. At last, based on the learned mapping relationship, the echo data of multichannel radar is processed with the proposed framework to achieve forward-looking superresolution imaging. Experimental results were provided to verify the effectiveness of this imaging framework.

Swarm unmanned aerial vehicle synthetic aperture radar (UAV SAR) system leverages multiple UAVs to form a formation, overcoming the limitations of a single platform and enabling the execution of advanced SAR missions. By forming a uniform linear array formation, the swarm UAV SAR system is able to coherently enhance resolution in one direction. Extend to 2-D cases, a uniform planar array needs to be formed for resolution enhancement. However, the requirement for a large number of UAVs to form the planar array can lead to significant costs. In addition, the performance of resolution enhancement is intricately tied to the chosen system formation. Therefore, there is a pressing need to conduct research on methods to obtain the optimal formation. In this article, a hybrid resolution enhancement (HRE) framework has been proposed for the swarm UAV SAR system to optimize resolution enhancement performance while mitigating costs. The proposed framework is mainly divided into two stages: cost-effective formation strategy and optimal HRE. The cost-effective formation strategy, which lays down the foundation for resolution enhancement, is comprised of three steps. First, to achieve HRE with a reduced number of UAVs, a cross-shape formation structure is established. Second, to effectively optimize the position and velocity of the central node of the UAV swarm for optimal resolution performance, a constrained differential evolution (DE)-nondominated sorting (CDE-NS) algorithm is proposed. Third, baseline design is conducted to determine the attached nodes’ positions for optimal coherent resolution enhancement (CRE). After the ideal formation is obtained, optimal HRE can be accomplished. Specifically, the principle of CRE is explained. The inspiration, motivation, and novelty of the proposed noncoherent resolution enhancement method named minimum combination (MC) are elucidated. Simulation results have demonstrated the validation of the proposed framework.

Bistatic synthetic aperture radar (BiSAR) parametric imaging can reconstruct the structural information of the target, which is one of the research hotspot for BiSAR imaging. However, compared with monostatic SAR, there are several challenging problems to be faced in terms of BiSAR parametric imaging, such as complex echo models, compute and storage burden, azimuth-dependent phase (ADP). To this end, we first propose the BiSAR parametric echo model, followed by analysis of the characteristics of BiSAR parametric imaging. Based on the abovementioned anaysis, we propose a deep adaptive BiSAR parametric imaging network (DAPI-net), which consists of three parts, namely adaptive dimensionality reduction module, parameter estimation module and image reconstruction module. The main idea of DAPI-net is to employ coarse imaging to determine the approximate range of target parameters and construct a local observation matrix to reduce computational complexity. On this basis, a variant observation matrix learned solver and ADP compensation unit are used to achieve high-precision BiSAR target parameter estimation. Among them, the ADP compensation unit mainly mitigates echo model mismatch caused by ADP and improves the parameter estimation performance. Finally, BiSAR parametric image output is achieved through image dilation. The efficacy of DAPI-net is validated through simulation experiments and microwave anechoic chamber data. The proposed algorithm not only addresses the identified challenges but also advances the state-of-the-art in BiSAR parametric imaging.

Geosynchronous synthetic aperture radar (GEO-SAR) can provide long-duration and wide beam coverage over the interested target scene, which is an ideal illuminator for bistatic SAR acquisitions. As a particular system configuration, the GEO bistatic SAR with maneuvering platform as the receiver (GEO-MP-BiSAR) can achieve continuous observation of the interested target during the flight. The target recognition and tracking information can be generated from the updating images for enhanced guidance performance. However, the bistatic SAR imaging performance is dependent on the observation geometry, which in turn is determined by the trajectory of the receiver. Therefore, in this paper, the trajectory optimization for GEO-MP-BiSAR is firstly investigated. The goal of the method is to generate a set of feasible trajectories to guide the maneuvering platform towards the target, and meanwhile obtaining the optimized imaging performance during the whole flight. The trajectory optimization is then modeled as a multi-objective optimization problem with multiple constraints, where the trajectory control and SAR imaging performance are comprehensively considered. Then, a knee-guided multiobjective evolutionary algorithm is put forward to effectively solve the problem, where the knee solutions are utilized to guide the search process and improves convergence and diversity of the method. The proposed algorithm can generate the prescribed number of solutions with significant trade-offs between the performance metrics. The mission designer can then choose a solution from only a few optimized candidates for implementation, which greatly improves the efficiency of decision making. Experimental studies demonstrate the effectiveness of the proposed method.

A conventional synthetic aperture radar (SAR) transmits the linear frequency modulation (LFM) signal for imaging and processes the target and clutter without distinction, making target enhancement difficult to realize. Waveform design is considered a common approach to improve the signal-to-clutter ratio (SCR). However, due to the undulating frequency response of radar clutter, the waveform designed for SCR improvement tends to have a high sidelobe autocorrelation function (ACF), which can lead to the degradation of SAR imaging performance. To solve this problem, this article proposes a complementary SAR waveform set design method for the improvement of the SCR while suppressing the sidelobe. Different from the traditional single waveform design method, in this article, the transmitted waveform set is jointly optimized. The designed waveform with a complementary sidelobe varies between pulses. Utilizing the multipulse azimuth compression of SAR imaging, the aforementioned high sidelobe can be eliminated after azimuth focusing. To this end, we use the inexact alternating direction penalty method (IADPM) and develop the spectrally constrained complementary sequences (SCCSs) algorithm to solve the resulting nonconvex optimization problem. Simulation and experimental data verification highlight the effectiveness of the proposed design for SCR improvement while maintaining the SAR imaging performances.

Synthetic Aperture Radar (SAR) is widely used in various fields due to its all-weather and all-day working characteristics. With the increasing use of SAR on small platforms, SAR is facing a series of problems due to the large volume of echo data. Imaging methods based on compressed sensing (CS) use the sparsity prior of the scene to reconstruct images from undersampled echoes. However, the CS-based method requires the imaging scene or its transformation domain to be sparse, which is not the case for most practical applications. This paper proposes a deep unrolling network named NSR-NET, which is based on SAR image representation learning and is applicable for undersampled imaging in non-sparse scenes. In modeling, the learned image representation is adopted as the regularization term. Then, the proximal gradient descent (PGD) algorithm was used to derive the iterative solution of the model. In network design, the iterative process is unrolled into a deep neural network with learnable parameters. Specifically, image representation is obtained through 2D convolutional layers in the network, and a learnable piecewise linear layer is used to fit the regularization function, which ultimately achieves the mapping from undersampled echoes to SAR images. Comparative experiment using different imaging methods shows that the imaging performance of the proposed network exceeds that of the state-of-the-art methods in non-sparse scenes. Moreover, we also designed transferability validation experiments with different radar parameters and imaging scenes, whose experimental results suggest that the proposed network has good generalization ability.

Different sea states make maritime ship targets move three-dimensionally, causing imaging results severely defocused. Therefore, selection of proper imaging time has attracted global attention in the field of ISAR imaging. This paper proposes an imaging time and plane selection algorithm based on short-time fractional Fourier transform (STFrFT). By STFrFT, time-frequency curves of the target echo for different fractional orders are obtained, and probability density functions of frequency distributions are offered to estimate when Doppler frequency of the target changes most stably. Then, different imaging planes of different STFrFT orders are analyzed to select the optimal imaging time moment. Finally, by comparing image contrasts of various imaging lengths, the optimal imaging time is selected. In general, this method can select the optimal imaging moment, imaging length and imaging plane, and simulation verifies the effectiveness of this imaging method.

The actual detection scenes faced by bistatic synthetic aperture radar (SAR) often have elevation information, which will cause shadow and overlay effects in imaging results. In view of the above problems, this paper proposes an echo generation method of bistatic SAR based on hidden point removal(HPR) operator. In this paper, according to the bistatic configuration, the shadow area is deduced first by using blanking algorithm(HPR operator). On this basis, the visibility of the target in the scene can be determined to obtain the echo. Finally, a simulation using a digital elevation model is conducted to verify the accuracy of the proposed method.

In UWB (ultra-wideband) radar imaging, there are differences in the signal scattering characteristics of targets for different frequencies. The estimation of target scattering characteristics is an important research field in target recognition and structured imaging. To solve this problem, this paper combines the target scattering characteristic estimation and super-resolution imaging into a low-rank and sparse signal reconstruction problem. It proposes an ADMM (the alternating direction algorithm of multiplier) scattering characteristic estimation algorithm based on low-rank and sparse signal. First, the echo model of UWB signal is established according to GTD model. Then build the corresponding complete dictionary. Finally, the ADMM-LS algorithm is used to solve the multi-objective joint optimization problem. Numerical simulation experiments verify the algorithm to prove the effectiveness of the proposed method.

In the application of vehicular MIMO (Multiple Input Multiple Output) radar, it is particularly important to measure and associate the position and velocity of targets. However, traditional methods like 3D-FFT have low angular resolution, and velocity ambiguity resolution is required. Combined with Super-resolution DOA (Direction Of Arrival) algorithm, we propose a multi-dimensional information association method based on target tracking, which uses the labeled GM-PHD (Gaussian Mixture Probability Hypothesis Density) algorithm. This method simultaneously completes the association task about position and velocity and the tracking task, and does not require an additional step on velocity ambiguity resolution. Finally, we use experimental data to verify the effectiveness of the algorithm.

In the past, the parameters of SAR system were relatively fixed, which lead to the poor flexibility in the use of imaging resources. In this paper, the concept of cognitive radar is introduced into SAR, which can make the system adaptively optimize the resources required. We design a three-steps process for large-scale imaging, which consists of two reconnaissance process and one cognitive processing link. Finally, we design the parameters and resources of two reconnaissance process manually or by using genetic algorithm. Experimental results show that the parameters designed meet the demand of large-scale search and the genetic algorithm used can significantly reduce the resource cost in further reconnaissance and improve the flexibility of system.

Moving target indication (MTI) plays an important role in both military and civilian applications. However, the performance of MTI has deteriorated dramatically due to clutter interference. In bistatic synthetic aperture radar (BiSAR), MTI mainly faces three challenges: First, BiSAR has large range cell migration (RCM); Second, the Doppler spectrum is severely extended; Third, the clutter presents nonhomogeneity and nonstationarity. In order to solve these problems, a novel MTI method for single channel BiSAR is proposed. At first, the first-order keystone transform (KT) is performed to remove linear range walk (RWK) regardless of unknown motion parameters. Then, the high-order RCMC is realized by reference compensation function. It is extremely difficult to achieve MTI because moving target is submerged in clutter whether in azimuth time domain or Doppler domain. So, to achieve separation of moving target from stationary clutter effectively, a novel procedure named increased dimension rotation (IDR) processing is introduced. Thereafter, the distribution characteristic of moving target and stationary clutter after rotation processing is analyzed exhaustively. Naturally, the optimal filter is constructed for clutter suppression in the light of the distribution characteristic. Finally, the energy of moving target can be accumulated after inverse increased dimension rotation and Fractional Fourier Transform (FrFT). And the MTI in BiSAR can be easily achieved.

The information obtained by single sensor is limited, so the fusion of multi-source images becomes the research focus. Monostatic SAR and bistatic SAR can obtain complementary scattering information from the same target, but the SAR image lacks color information resulting in limited visual effects. The optical image contains color information but it is difficult to achieve high resolution at the decimeter level due to the limitations of present optical cameras. Therefore, this paper proposes an algorithm to fuse monostatic and bistatic SAR image with optical image. First, the three images are registered, then the monostatic and bistatic images are stitched into a dual-channel image. Finally, the dual-channel SAR image is fused with optical image using a blind model-based fusion method. The fusion result combines the color information of optical image with the high-resolution and texture information of SAR image, which enhances the visual effect and readability, providing great convenience for subsequent applications. The fusion results of the experimental data show that the algorithm can effectively make the image information more comprehensive and accurate.

Multi-core digital signal processor (DSP) is widely used in SAR real-time imaging system for its high-speed operation capacity and parallel working ability. The matching of the algorithm and the parallel architecture has great impact on imaging speed of SAR processing. This paper proposed an efficient imaging architecture based on multi-core DSP TMS320C6678. The system implements bistatic polar format algorithm (PFA), using two-dimensional Chirp-Z Transform and one-dimensional interpolation to realize two-dimensional resampling. The experimental results show that the proposed architecture can complete 1024×1024 points echo processing and output a 1024×1024 pixels image within 1.7 seconds.

The joint FPGA and multi-DSP imaging architecture has found widespread use in airborne SAR and satellite-based SAR. However, traditional back-projection (BP) algorithms are not efficient enough for real-time imaging. This paper proposes a back-projection algorithm based on wavenumber-domain spectral splicing (WFBP) for designing an efficient SAR imaging system. A unified polar coordinate system is employed in this work to project the sub-aperture imaging results, which eliminates the need for a large amount of interpolation and multiple projection transformations. Furthermore, spectral shifting is applied to eliminate the effects caused by wavenumber-domain spectral overlap. Experimental results demonstrate that the WFBP algorithm reduces the imaging time by 64% compared to the traditional BP algorithm.

Multistatic SAR based on UAV swarm can reduce synthetic aperture time and increase the imaging frame rate while maintaining high image resolusion, thus it is of great promising prospect. Due to the lack of efficient and high precision imaging methods, the performance of UAV swarm SAR is not fully developed. Therefore the imaging method suitable for UAV swarm SAR is studied in this paper. Compared with frequency domain imaging techniques, time domain imaging method is more appropriate for UAV swarm SAR with complex trajectories. But the heavy computational burden is a problem. Thus this paper proposes a fast imaging method based on spectrum fusion for high frame rate UAV swarm SAR. In the method, each bistatic pair in UAV swarm SAR is split into several sub-pairs to generate coarse angular resolution images and the fusion of images is realized through spectrum fusion, which obtains the imaging result of UAV swarm SAR. Experiment is carried out to verify the effectiveness of the proposed method.

Multistatic synthetic aperture radar (SAR) can obtain abundant information from different angles for terrain classification and tomography. However, multistatic SAR systems, particularly the multistatic global navigation satellite systems (GNSS) face the problem of insufficient resolution. To address this issue, a novel noncoherent combination method termed as minimum combination (MC) is proposed. Inspired by dual apodization, MC is specifically designed to improve the resolution of the multistatic SAR system. Relative to the conventional noncoherent addition (NA) method, MC yields an image with higher resolution and reduced sidelobe level. Simulation results are presented to illustrate the effectiveness and superiority of the proposed approach.

In recent years, many countries are actively deploying Low-Earth-Orbit (LEO) communication satellite constellations, which have the advantages of both high power flux density (PFD) on the surface of the earth and large signal bandwidth. From the perspective of radar application, these new LEO constellations are very suitable as opportunity of illuminator for target detection in passive radar systems. In this paper, the echo signal using LEO communication satellite is analyzed, and a moving target detection method is proposed.

Shadow simulation is a key part of SAR raw data and image simulation for undulated terrain, which will directly determine the accuracy of SAR simulation and will also affect the accuracy of subsequent shadow-based SAR image processing applications. In this paper, a shadow simulation scheme for undulated terrain SAR images based on facet cell fitting and elevation angle comparison is proposed. In the scheme, the height information of each grid point on the same line of sight within the beam irradiation range is obtained firstly with the facet cell fitting, and the elevation angle of each grid point can be computed and compared to determine whether the occlusion happens. Then, all grid points in the illumination range are traversed to obtain the shadow judgment result at a certain azimuth moment, and the SAR shadows are obtained by superimposing the judgment results at all azimuth moments. At last, simulated and measured data experiments are given to verify the effectiveness of the proposed scheme, and the results show that this scheme can accurately simulate the SAR shadow of undulating terrain.

Retrieval of building height from synthetic aperture radar (SAR) image is of great significance for damage assessment after natural disasters, urban development and planning, and dynamic time series monitoring in urban areas. Based on the prior information of the building and the radar platform, the building height can be estimated by iteratively simulating the SAR image and matching it with the measured SAR image. However, the estimation accuracy would be affected inevitably when there is error of prior information introduced by instable platform or inaccuracy of measuring instruments. In this letter, the beam incident angle and building height are both used as search variables for iterative simulation and matching (ISM), and normalized mutual information (NMI) is used to measure the similarity between the simulated image and the measured image to achieve an accurate estimation of building height. At last, measured data experiments are provided to verify the effectiveness of the proposed scheme.

Compared to conventional microwave regime radars, microwave photonic (MWP) radar is capable of transmitting extremely large bandwidth signals, wherein the frequencies of such signals distribute across multiple bands. In practical applications, the large bandwidth of MWP radar may be split into multiple discrete sub-bands due to various considerations such as anti-jamming, resource-saving, communication band avoidance, etc. Nonetheless, it leads to the fact that MWP radar suffers from the challenging problems of side-lobes elevation and main-lobes broadening. These problems will affect the image quality seriously. In order to address this issue, a spectrum recovery algorithm based on an improved Truncated Schatten-pNorm and Sparse Regularizer-Alternating Direction Method of Multipliers (TSPN-ADMM) network is proposed in this paper. This algorithm can efficiently recover the lost spectrum in MWP radar applications and further improve the imaging quality of the MWP radar. In the lost spectrum recovery problem, the parameters of the recovery algorithm directly determine the recovery performance. The different forms of lost spectrum possessed by MWP radar make the selection of parameters for the spectrum recovery algorithm extremely difficult. As a consequence, in this paper, the spectrum recovery problem for MWP radar can be reformulated into a matrix completion problem by exploiting its joint sparsity and low-rankness. Based on the traditional TSPN-ADMM algorithm, an improved TSPN-ADMM-Net approach is proposed by utilizing the algorithm unrolling technique, wherein the hyperparameters in TSPN-ADMM algorithm are optimized in an end-to-end training manner. Consequently, the algorithm proposed in this paper can achieve excellent recovery results when dealing with the multiple spectrum missing situations existing in MWP radar. The effectiveness of the algorithm is verified by a combination of numerical simulations and actual MWP radar data.

Synthetic aperture radar (SAR) plays an important role in remote sensing by providing electromagnetic images of the observation scene. The prior knowledge-based SAR image reconstruction method can reduce the requirement of data sampling ratio and improve image quality. The existing prior knowledge-based method usually uses the magnitude information of SAR images while ignoring the phase information. However, since the echo and backscattering coefficients are complex values, the phase information of SAR images will help improve the reconstruction accuracy in the image reconstruction process. To improve the reconstruction performance, this paper proposes a SAR image reconstruction and autofocus method using complex-valued feature prior. In the proposed method, the complex-valued feature prior is learned from data by a complex-valued feature projection operator (CFPO), which can characterize and extract scene features in Range-Doppler domain and 2D frequency domain. The proposed CFPO enables more efficient use of echo data and helps to improve image reconstruction and autofocus performance. In addition, the proposed method is implemented by an unfolded deep network, which enables data-driven feature learning and efficient computation. The proposed method is verified by simulated and measured data.

Moving target indication (MTI) in bistatic synthetic aperture radar (BiSAR) is a promising task in both civilian and military fields. However, it suffers severe challenges under the influence of clutter. MTI in BiSAR mainly faces three challenges: 1) the range cell migration (RCM) of BiSAR is larger than that of monostatic synthetic aperture radar; 2) the clutter range-Doppler spectrum is severely extended; and 3) the clutter characteristic is closely related to geometrical configuration, with nonhomogeneity and nonstationarity. To solve these problems, based on single-channel BiSAR, a joint clutter suppression and MTI method is proposed. First, to ensure that the energy of an arbitrary target is concentrated in one range cell, RCM correction (RCMC) is completed by the first-order keystone transform (KT) and high-order RCMC. Then, an important step named increased dimension rotation (IDR) is applied, which mainly consists of two stages. One is increased dimension processing, which introduces a 2-D azimuth rotated time domain (ARTD). The other is rotation processing, which makes signals in one range cell rotated to 2-D ARTD. After that, the distribution characteristic between the moving target and stationary clutter in 2-D ARTD is analyzed. Next, an optimal filter for clutter suppression is designed according to the characteristics of signal distribution in 2-D ARTD. Furthermore, the corresponding inverse IDR processing and fractional Fourier transform (FrFT) are performed, and finally, MTI can be realized accurately. Generally, the proposed method has good robustness and low system complexity, and its effectiveness is proven by numerical simulations.

Bistatic synthetic aperture radar (BiSAR) has been attracting worldwide attention because of its forward-looking imaging and high anti-interference. In harsh environment, it is vital for BiSAR to conduct extensive surveillance, imaging, and recognition of maritime ship targets. However, under the disturbance of sea waves, the ship target has an unknown and massive three-dimensional (3-D) rotation, so that its imaging projection plane (IPP) is also undetermined. Thus, high-dimensional random distortion appears in imaging results, making it difficult to recognize the target through two-dimensional distorted images effectively. To solve these problems, bistatic SAR maritime ship target 3-D image reconstruction method without distortion in local Cartesian coordinate (LCC) is proposed. In this paper, according to positions of scatterers and rotation parameters, significant differences of different scatterers of maritime ship targets have been found in bistatic range, Doppler centroid (DC), and Doppler frequency rate (DFR), which lays a solid foundation for the scatterer separation of ship targets. On this basis, a 3-D R-DC-DFR domain is constructed, and 2-D echoes of the maritime ship target are projected into R-DC-DFR domain to separate scatterers. Then, by remapping data of transmitter and receiver in R-DC-DFR domain to LCC, as well as evaluating their similarity metric, the optimal rotation parameters of the ship target can be obtained via the maximal similarity. Therefore, the image distortion caused by the unknown IPP has been removed, and 3-D image reconstruction of ship targets can be realized without distortion in the LCC. Furthermore, to evaluate performances of 3-D image reconstruction for different rotation parameters and bistatic configurations, 3-D reconstruction index is proposed and analyzed. Both point-targets and maritime ship targets are simulated to emphasize the effectiveness of the proposed method.

GEO spaceborne-airborne bistatic SAR (GEO SA-BiSAR) has flexible configuration and the ability of multi-looking imaging, so it has a good application prospect. The transmitting propagation delay is about 0.1s due to the 36500Km high altitude transmitter, so the motion of ground target and receiver in the propagation delay can't be ignored. This paper presents a range model under the “non-stop-and-go” assumption, which analyzes the transmitting and receiving process respectively, and we consider the movement of the target and receiving platform under long transmitting delay, and obtains a succinct and accurate range model. For the delay of each process, we establish the accurate numerical solution, the approximate solution and expanded of the time delay under the “non-stop-and-go” assumption separately. Simulation results show that our method can estimate the real propagation delay accurately for both stationary and moving targets.

Multistatic synthetic aperture radar (SAR) can realize 3D imaging of observation scenes by single navigation using distributed aperture. Meanwhile, due to the flexible baseline configuration of unmanned aerial vehicle (UAV), and has broad application prospects in remote sensing, surveying and mapping fields. However, the 3D reconstruction performance of multistatic SAR is closely related to its multistatic baseline. In this paper, a multistatic baseline design method is proposed to achieve optimal 3D reconstruction performance. Firstly, the quantitative relationship model between multistatic baseline and 3D imaging measurement matrix is established, and the cross-correlation value of measurement matrix is introduced as the evaluation index of reconstruction performance. Then, the multistatic baseline design problem is modeled as an optimization problem with an optimal crossrelation number. Finally, the differential evolution algorithm is used to obtain the multistatic baseline of the optimal design. Simulation results show that compared with the unoptimized multistatic baseline, the optimized multistatic baseline can obtain better reconstruction performance when the typical sparse recovery method is used for 3D imaging.

Beam steering is widely applied in the airborne and space-borne SAR system to achieve a balance between large imaging area and high azimuth resolution. However, it leads to a linear variation of Doppler centroid in monostatic SAR, which is more complicated in bistatic SAR due to the contribution built by the transmitter and the receiver and results in Doppler spectrum aliasing. The spatial variance of the Doppler centroid of the beam steering bistatic SAR is analysed. And a modified preprocessing method for azimuth-variant bistatic SAR with beam steering mode is proposed to remove the Doppler spectrum aliasing by effectively increasing the sampling rate of azimuth time. The movement of the platforms is considered with cured trajectory.

Multistatic SAR based on unmanned aerial vehicles (UAV) swarm can simultaneously obtain the echo of the target from different perspectives. Thus it can synthesize a large aperture in a short time, realizing high resolution and high frame rate imaging of interested areas. But there is few imaging method for high frame rate UAV swarm SAR and the problem of over-lapping apertures affecting imaging quality remains to be re-solved. Therefore, this paper proposes a time-domain imaging method to solve the problems. Firstly, echo model of UAV swarm SAR is established and multistatic equivalent aperture distribution is analyzed. Then the echo is equalized to im-prove image quality. Next, coarse angular resolution images of each bistatic SAR pair in UAV swarm SAR is accurately formed. Finally, all bistatic images are recursively fused to get the final result. Simulation results verify the effectiveness and accuracy of the proposed method.

Airborne ultra-wideband synthetic aperture radar (UWB-SAR) is easily affected by motion error, leading to a decline in image quality. In this paper, a motion error compensation method based on coherent correlation function (CCF) is proposed, which can estimate the high-order components of motion error and reconstruct platform trajectory. Taking the peak sidelobe ratio (PSLR) of CCF as the evaluation function, the particle swarm optimization (PSO) algorithm is used to estimate the high-order components of the trajectory error. Then the trajectory is modified to compensate for the image blur caused by motion error. The effectiveness of the method is verified by numerical simulation.

Synthetic Aperture Radar (SAR) can perform all-time and all-weather observations and is wildly used in earth remote sensing. The sparsity-driven SAR imaging methods can reconstruct sparse scenes under down-sampling conditions, but they are unsuitable for non-sparse scenes. To reconstruct non-sparse scenes from under-sampled data and further improve the utilization efficiency of sampled data, this paper proposes a feature learning and SAR imaging method and implements it through a deep network. The imaging model is constructed firstly, where a feature-based sparsity regularization term is incorporated. Then, by unfolding the iterative solution derived via the Alternating Direction Multiplier Method (ADMM) algorithm, a CNN-based deep network is proposed to solve this imaging model. In the proposed network, convolution layers are used to represent and learn the scene feature prior knowledge. Simulation experiments verify the effectiveness of the proposed method.

Various imaging methods based on compressed sensing (CS) of synthetic aperture radar (SAR) have been proposed to reduce the sample size of echoes required for the imaging process. The unrolling technique further solves the inefficiency of conventional CS-based methods by mapping them into deep neural networks. However, most of these methods are based on sparsity prior of the scene or its transformation domain, which could be invalid for non-sparse scenes. To address this, we proposed a network utilizing the feature priors of the images instead of sparsity for non-sparse scene reconstruction of SAR, namely NSR-Net. We adopt learnable regularization terms in the CS model. Then the iterative solving process of the model is derived and unrolled into the proposed deep neural network to learn the best regularization terms from data. Simulation experiments verified the effectiveness of NSR-Net in the reconstruction of non-sparse scenes with down-sampled SAR echoes.

Synthetic aperture radar (SAR) can obtain two-dimensional images of the illuminated area, which is an important means for earth remote sensing and monitoring. However, due to the loss of azimuth data and system errors during the processing of data sampling, it is necessary to study the method for high-quality SAR image reconstruction from down-sampled data in the condition of measurement inaccuracy. Considering these factors, this paper proposes a sparsity-driven SAR imaging method based on general regularization and the sparse total least-squares (S-TLS) model and implements the method by an unfolded deep network. In the proposed method, general regularization can solve the problem of sparse sampling, and the S-TLS model is adopted to deal with measurement inaccuracy. Moreover, through the deep network implementation, the proposed is more time-efficient and can exploit more effective scene prior knowledge, making the proposed method suitable in practical applications. Experiments verify the effectiveness of the proposed method.

Localization plays an important part in the application field of bistatic SAR (BiSAR). The accuracy of traditional localization method for BiSAR depends on the measurement precision of platform. The lower measurement precision, the higher localization error of targets. However, due to the size, cost, and technical limitations of existing attitude measurement devices, it is difficult to satisfy the requirement of high-precision target positioning in BiSAR. This paper proposes a blind locali-zation method based on monostatic equivalent model for BiSAR, which has high tolerance and low sensitivity for measurement error. First, the BiSAR is equivalent to monostatic SAR system based on the principle of Equivalent Phase Center (EPC). Next, the Range-Doppler localization model is applied to fix position for EPC and the relative position of multi-targets are obtained subsequently. At last, establishing localization equation with bistatic range of multi-targets, and the Newton iteration algorithm is devoted to solving localization model constructed above. The effectiveness of the localization method proposed in this paper has been demonstrated and illustrated by numerical simulations.

In recent years, many imaging algorithms for highly squint synthetic aperture radar (SAR) have been proposed. An algorithm based on keystone transform (KT) and azimuth Extended Nonlinear Chirp Scaling (ENLCS) is widely used in highly squint SAR imaging processing. It's effective in solving spatial-variant linear range cell migration (LRCM) and azimuth-variant Doppler parameters. However, due to the highly squint configuration, the beam center crossing time of many illuminated targets are not included in the track. We need extend the azimuth data length to ensure the targets a corresponding position in the data, which leads to an increase in computational complexity. And the imaging result also has geometric distortion. This paper proposes an improved ENLCS method with lower complexity combined with fast KT. First, we use the low complexity KT without interpo-lation in the RCM correction (RCMC) process. Then, we find a solution to reduce the extended data length by adding a time shift factor in the ENLCS process, saving data storage space and operation cost. Finally, geometric correction is performed by the grid mapping. The effectiveness of the proposed method is verified by numerical simulation and real data processing.

Distributed MIMO Array Radars normally utilize sparse arrays with large equivalent array element spacing, which cannot avoid the generation of grating lobes that cause interference to the received signal. The grating lobes can be suppressed by Phase Coherence Factor (PCF) weighting factor. Regretfully, PCF has problems such as the possibility of suppressing the main lobe as well and the unclear phase resolution of the grating lobes. In this paper, we propose a new weighting factor, namely Embedded Classification Coherence Factor (ECCF), by improving the PCF weighting factor, and propose an embedded superposition method to filter and superimpose the phases of all channels in the BP imaging process. ECCF shows a lower peak side lobe ratio than PCF, and enjoys a superior effect on grating lobe suppression. Simulations are given to verify the performance.

Sparse linear array synthetic aperture radar (SAR) is widely used in near-field 3-D imaging, which can reduce the volume of data and the cost of acquisition. However, the sparsity of array elements means the missing rows or columns of data, leading to the high-level sidelobes and the aliasing of targets. Therefore, in this paper, we develop a near-field 3-D imaging method for non-uniform sparse linear array SAR based on matrix completion (MC), where a rotated expansion algorithm is proposed to make the non-uniform sparse data af-ter range compression satisfies the requirements of the MC. The MC technique is applied to the signal slice of each range cell to reconstruct the echo signal, and the back-projection algorithm (BP) is used to achieve 3-D high-resolution imaging of targets. Moreover, simulations and near-field experi-ments validate the feasibility and superiority of the proposed method.

Clutter suppression is the premise of moving target detection and imaging. We propose a cancellation method for GEO Spaceborne–Airborne Bistatic SAR (GEO SA-BiSAR). Firstly, according to the range history under the “non-stop-and-go” assumption, we establish a multi-channel echo signal model. Then, according to the range from the high orbit transmitting station to the target, we compensate the phase of the echo signal. Next, by analyzing the multi-channel phase relationship, we propose an improved Displaced Phase Center Antenna (DPCA) method suitable for GEO SA-BiSAR, and then we analyze the result and performance of DPCA. Finally, the effectiveness of this clutter suppression method is verified by numerical simulation.

High sidelobe of strong scattering points usually submerges weak targets nearby and affects the quality of SAR image. Therefore, SAR image usually requires sidelobe control. Common window functions have limited improvement on PSLR performance when the image resolution is required to be guaranteed. Combining Min-Max weighted ISL technique, this paper proposes an azimuth low sidelobe window function design method for SAR. Simulation results show that PSLR of the designed window is nearly −10dB lower than hanning window with a −45dB ISL level, and main lobe width is almost equal to hanning window.

With the continuous development of imaging radar technology, the bandwidth of transmission signals continues to increase. The geometrical theory diffraction (GTD) model can describe target scattering characteristics under wideband electromagnetic wave irradiation, which is significant for target detection and recognition. Existing target scattering center parameter estimation methods based on compressed sensing (CS) theory require sparse imaging scenes, which is difficult to meet in actual radar data. To solve this problem, this paper proposes a method for estimating high-value scattering centers parameters based on energy trajectory extraction. The estimation result of this method is accurate with fewer operations than CS-based method, and this method reduces the requirement for target sparsity. And use numerical simulation to prove the effectiveness of the method.

Synthetic Aperture Radar (SAR), an all-weather microwave remote sensing technology, is widely used in environmental monitoring, earth resource surveys and other fields, especially in the military field to achieve monitoring purposes. In the monitoring process, it is crucial to achieve real-time imaging of the target scene. Therefore, in this paper, an efficient SAR real-time imaging system is designed to achieve timely parallel processing of the scene when the radar platform scans the target scene, so as to reduce the consecutive frame cycles of real-time imaging. In which, the signal processing board structure consists of two FPGAs and six DSPs is used to quickly implement high precision SAR real-time imaging for airborne radar, so that the radar system can display the target image in time with the platform moving and check whether the current echo is valid. In addition, the architecture proposed in this paper can be extended in case the continuous frame period for real-time imaging is short.

Benefiting from the capability of forward-looking imaging, ability of receiver radio silence and resistance of jamming, bistatic SAR has extensive potential applications. However, due to its independent dual platform movement, motion error of bistatic SAR is usually more complicated than monostatic SAR and could easily exceed range resolution cell, which will result in both azimuth and range defocusing if not properly compensated. In this paper, an autofocus scheme for bistatic SAR considering cross-cell residual range migration is proposed. In this scheme, cross-cell residual range migration is firstly compensated by estimating bistatic range error through time-frequency analysis. Meanwhile, azimuth phase error is coarsely compensated by using bistatic range error estimation result. Secondly, maximum image sharpness autofocus method is cascaded to further compensate for azimuth residual phase error and enhance the quality of bistatic SAR image. The effectiveness of proposed scheme is verified by simulation and experiment results.

Synthetic aperture radar (SAR) imaging with sub-Nyquist sampled echo is a challenging task. Compressed sensing (CS) has been widely applied in this case to reconstruct the unambiguous image. The CS-based methods need to set the iterative parameters manually, but the appropriate parameters are usually difficult to obtain. Besides, such methods require a large number of iterations to obtain satisfactory results, which seriously restricts their practical applications. Moreover, the observation scene of SAR is not sparse in some cases. In this paper, we aim at proposing an efficient and effective imaging method for non-sparse observation scenes with reduced data. Firstly, considering the characteristics of non-sparse observation scenes in SAR imaging, we model the SAR imaging problem as a joint low-rank and sparse matrices recovery problem. After that, the iterative alternating direction method of multipliers (ADMM) to solve the above problem is unrolled into a layer-fixed deep neural network with trainable parameters, in which the learnable parameters are layer-varied. The threshold parameters, as well as the weight parameter between the sparse part and low-rank part of each layer, are learned adaptively instead of manually tuned. Experiments prove that the proposed LRSR-ADMM-Net is capable of reconstructing the non-sparse observed scene with high efficiency and precision. Particularly, the proposed LRSR-ADMM-Net yields better reconstruction performance while maintaining high computational efficiency compared with the state-of-the-art iterative recovery methods and the trainable sparse-based network methods.

Video synthetic aperture radar (SAR) receives more and more attention in recent years because it can provide continuous images of the observed scene. However, the enormous data of video SAR to obtain the multiframe images bring big challenges to its transmission, storage, and processing, especially for small unmanned aerial vehicle (UAV) platform. In this article, we aim at proposing an efficient video formation method for video SAR systems with reduced data. First, the characteristics of video SAR observed scene are analyzed. It is found that the observed scene with multiple frames can be modeled as the sum of a low-rank tensor and a sparse tensor efficiently. After that, the video formation problem for video SAR is modeled as a joint low-rank and sparse tensors recovery problem. Finally, an efficient tensor alternating direction method of multiplier is proposed to obtain the final SAR video. Compared with the traditional frequency- or time-domain imaging methods, the amount of data samples can be greatly reduced. On the other hand, the proposed method outperforms the state-of-the-art SAR imaging methods with reduced samples, including the joint low-rank and sparse matrices recovery method and the low-rank tensor recovery method. Numerical simulations validate the effectiveness of the proposed method.

Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with conventional SAR imaging algorithms, two major problems should be considered: 1) The scattering characteristics of target might vary from the frequency of signal and the angle of incidence, which seriously affects the coherence of received echo. 2) The imaging of MWP-SAR system is more sensitive to motion errors and therefore requires higher accuracy of motion compensation processing. To solve the above issues, a high-precision imaging method for MWP-SAR is proposed. First, based on the attribute scattering center(ASC) model, this paper analyzes the target-scattering characteristics with different frequencies and incident angles. Then, according to the influence of scattering phase on MWP-SAR imaging, an optimal sub-aperture division algorithm is proposed to guarantee the coherence of each sub-aperture data and better imaging results. Furthermore, to compensate for the effects of high-order motion errors, this paper proposes a motion error estimation algorithm based on sub-image registration, where the full aperture high-order motion errors can be divided into multiple linear components, and the flight trajectory of platform can be accurately reconstructed. Finally, a full-aperture time-domain high-precision imaging method is presented, and the effectiveness of the proposed formation is verified by both simulation and actual airborne MWP-SAR data processing.

The global navigation satellite system (GNSS)-based passive radar shows potential in permanent maritime surveillance. In this paper, the GNSS signals are exploited for vessel target imaging. From the obtained radar image, meaningful information about the vessel, such as its shape, position, length, and orientation can be extracted. In addition, the vessel is observed from different angles by spatially diverse GNSS satellites, and the multistatic geometry enables to enhance the imagery quality. The main drawback of GNSS-based passive radar stays in its limited power budget. And the inaccessible motion makes the noncooperative vessel smeared using conventional radar imaging methods. To address the problems, at first, each bistatic echo over a long observation time is integrated in range and Doppler (RD) domain after removing the two-dimensional migrations. The signal-to-noise ratio can be increased after the step. Then, with respect to a particular target velocity, the local Cartesian plane is constructed, and the multiple RD maps are projected and combined in the plane to obtain the multistatic image. In view of the inaccessibility of target kinematic parameters, such imaging processing is modeled as an optimization problem, where vessel’s velocity is set as decision variable and the aim is to minimize the image entropy. Finally, particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which a well-focused vessel image can be obtained. In May 2021, we have successfully carried out the world’s first BeiDou-based passive radar maritime experiment, and effectiveness of the proposed method is verified against the experimental data.

This article puts forward a BeiDou-based passive multistatic radar (PMR) maritime moving target (MMT) detection technique via space–time hybrid integration (STHI) processing. Compared with passive bistatic radar (PBR), the utilization of multiple satellites provides an improvement in MMT detection performance, together with the capabilities of localization and velocity estimation. However, the multiple satellite transmitters cause the differences principally in bistatic range and Doppler centroid (DC) of the MMT. To integrate the PMR echoes, the biggest challenge is the two differences that need to be handled. In the proposed technique, first, the centroid-compensated keystone transform (CCKT) is proposed and applied to each PBR echo. It not only corrects range cell migration (RCM) but also equalizes the DC to the same. Then, the long-time integration is performed on each PBR echo, after which it is integrated into the range-Doppler frequency rate (DFR) domain. Finally, in order to settle the difference in bistatic range, an MMT position and velocity domain (i.e., the$X$–$Y$–$V$domain) is constituted. The obtained multiple range-DFR maps are projected to the$X$–$Y$–$V$domain, and then, the effective integration of multistatic echoes can be implemented. The final STHI result allows detecting the MMT reliably. Meanwhile, according to the 3-D position where the MMT is located in the$X$–$Y$–$V$domain, the MMT can be localized, and its velocity can be estimated simultaneously. In May 2021, we have successfully carried out the world’s first BeiDou-based PMR MMT detection experiment, and the experimental results are given to prove the effectiveness of this technique.

Synthetic aperture radar (SAR) is an important means for target surveillance through reconstructing the microwave image of the observation area. However, under the condition of low signal-to-clutter ratio (SCR), such as a strong sea clutter situation, it is difficult to surveil targets from SAR images acquired by the traditional matched filter-based imaging methods. To improve the target surveillance performance of SAR, this article proposes a target-oriented SAR imaging method, which can enhance the desired target and improve the SCR in the reconstructed SAR images. By separating the target area from the clutter area, we first establish a target-oriented SAR imaging model, where the generalized regularization is used to characterize the features of the target, contributing to the improvement of SCR in the reconstructed image. Then, the imaging model is solved through a deep network, MF-ADMM-Net, which is obtained by unfolding an alternating direction method of multipliers (ADMM)-based iterative solution. In addition, the training strategy is formulated with the consideration of complex values. Experiments are conducted to verify the performance of image reconstruction and SCR improvement of the proposed method, and comparisons show the superiority of MF-ADMM-Net in effect and efficiency.

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering images of the illuminated area, playing a significant role in various applications. However, achieving focused SAR images is challenging under sparse sampling and phase error conditions. By exploiting the sparsity or compressibility priors, the state-of-the-art sparsity-driven SAR imaging methods can reconstruct images under the condition of sparse sampling. However, the handcrafted priors used in these methods limit the imaging performance, and the iterative solution schemes reduce the computational efficiency. Besides, the measurement inaccuracy introduced by the phase error also degrades the reconstruction performance of the sparsity-driven imaging methods. To address these issues, a deep network for SAR autofocus imaging is proposed, which alternately performs image reconstruction and phase error estimation. When performing image reconstruction, the sparsity-cognizant total least-square (S-TLS) model is introduced to handle the problem of measurement inaccuracy, contributing to robust reconstruction performance under the condition of phase error. During the implementation of the deep network, a feature transform operator is used to realize data-driven prior knowledge learning and overcome the limitations of handcrafted priors. Moreover, the deep network approach can significantly improve computational efficiency. Experiments on simulated and real data verify the effectiveness and efficiency of the proposed method.

Clutter suppression is a challenging task in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, sufficient secondary samples are not easily acquired due to the nonstationary and nonhomogeneous characteristics of bistatic SAR (BiSAR) clutter, resulting in worse clutter suppression results. Recently, space–time adaptive processing based on sparse recovery (SR-STAP) has been developed since its better clutter suppression performance with less samples. However, since the off-grid problem in space–time domain caused by BiSAR’s separate configuration, existing SR-STAP would suffer from severe performance degradation. To address this problem, a clutter-ridge matched STAP (CRM-STAP) method for BiSAR nonstationary clutter suppression is proposed. First, clutter distribution modeling with arbitrary BiSAR configuration is applied to accurately obtain the clutter ridge in space–time domain. Then, keystone transform and time-division processing are applied to correct range cell migration and eliminate Doppler frequency migration, respectively. Next, to solve the off-grid problem, the CRM dictionary is reconstructed via adaptive gradient method, which is established along the direction of clutter ridge and its orthogonal direction. Then, with the constructed CRM dictionary, the clutter covariance matrix (CCM) estimation process is transformed to a multimeasured vector optimization problem, and it can be directly solved by the sparse Bayesian learning algorithm. Finally, based on the estimated CCM, the CRM-STAP filter is built to suppress the nonstationary clutter effectively. Compared with the existing STAP and SR-STAP methods, this method can avoid the performance degradation in clutter suppression caused by the off-grid problem and overcomes the strong nonstationary problem of BiSAR clutter in heterogeneous environments. In October 2020, we have successfully carried out the world’s first airborne BiSAR-GMTI experiment, and the experimental results are given to verify the effectiveness of this method.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of the receiver. Current literature and reports about BFSAR mainly concentrate on the stationary scene and ground-moving target imaging. Unlike stationary and ground-moving targets, the translational and rotational movements of ship targets usually lead to complicated range cell migration (RCM) and Doppler frequency migration (DFM). Moreover, the characteristics of RCM and DFM for different scattering points of the ship target are significantly different, i.e., the characteristics of the RCM and DFM are 2-D spatial variation, ultimately leading to severe defocusing of ship target in the SAR image. To solve these problems, a kind of hybrid SAR-ISAR imaging formation is proposed for BFSAR ship target imaging. First, to solve the problem of the Doppler ambiguity caused by the forward-looking mode of the receiver, an efficient ambiguity estimation method based on the minimum entropy criterion is presented. Then, keystone transform and range alignment processing can be applied to correct the spatial variant range walk and higher order RCM, respectively. Moreover, in order to obtain a high-resolution and well-focused image after translational compensation, a new method based on the fractional Fourier transform (FrFT) and the Wigner–Ville distribution (WVD) is proposed, where FrFT is applied to separate the multiple main scattering points in each range cell, and WVD is applied to obtain the high-resolution time–frequency distribution of each scattering point. Compared with the conventional ISAR range-Doppler (RD) algorithm and time–frequency estimation-based imaging methods, this method not only has no cross terms but also has high processing accuracy and better antinoise performance.

Clutter suppression in synthetic aperture radar (SAR) is one of the urgent and attractive problems in ground moving target indication (GMTI) application. With separate transmitter and receiver, ground clutter is nonstationary in bistatic forward-looking SAR (BFSAR), which directly leads to the inaccurate clutter covariance matrix (CCM) estimation. As a consequence, the traditional space-time adaptive processing (STAP) will suffer from a serious performance deterioration. In this article, an optimally matched space-time filtering (MSTF) technique is proposed to suppress nonstationary clutter for BFSAR systems. The main idea of the proposed method is to directly design and generate a suppression filter in space-time domain, whose space-time frequency response is matched with clutter spectrum. To construct the matched space-time filter, clutter modeling with arbitrary BFSAR configuration is first proposed to acquire space-time information of clutter spectrum. And then, the suppression filter can be designed and the design process is transferred into a constrained optimization problem (COP), according to the obtained clutter space-time information. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the COP and obtain the optimal solution, i.e., the desired matched space-time filter weight, for BFSAR nonstationary clutter suppression. Since the generation of the designed filter circumvents CCM estimation, the proposed method will not be affected by the nonstationary characteristic of BFSAR clutter. In October 2020, the first airborne BFSAR-GMTI experiment in the world has been successfully conducted by us, and the experimental results are given to validate the effectiveness of the proposed method.

The unmanned aerial vehicle (UAV) is a low-cost and high-efficiency lightweight synthetic aperture radar (SAR)-mounted platform that can be used for a variety of military and civilian missions. Using multiple UAVs to form a swarm can break through the limitations of a single platform and has broad application prospects. In this article, swarm UAV SAR that contains tens or hundreds of UAV platforms is proposed for the first time. The concept and advantages of swarm UAV SAR are investigated, and the mission outlook is given. Afterward, the swarm UAV 3-D linear array SAR (LASAR) is illustrated, which enables high-resolution 3-D imaging in a single flight. Since the antenna array of the swarm UAV 3-D LASAR is sparse, the compressed sensing (CS) algorithm is applied, whose reconstruction performance is closely related to the correlation coefficient of the sensing matrix. Hence, the signal model of swarm UAV 3-D LASAR is derived, and the expression of the sensing matrix is deduced. The sensing matrix design in this article aims at obtaining satisfactory reconstruction performance by optimizing the distribution of the antenna elements, which directly influences the correlation coefficient of the sensing matrix. Considering the limitation of the practical conditions, the sensing matrix design problem is modeled as a constrained integer programming problem. Finally, a sensing matrix design method based on discrete constrained differential evolution (DCDE) algorithm is proposed to solve the optimization problem. Experimental results demonstrate the effectiveness and superiority of the proposed method.

Due to the division of the transceiver, a bistatic synthetic aperture radar (SAR) gains many advantages, such as forward-looking imaging and powerful anti-interference capabilities. In the meantime, information sharing (e.g., positions and status) between the transmitter and the receiver is required for SAR imaging. This article addresses the co- use waveform design for SAR-dual-functional radar communication (SAR-DFRC). To this end, we embed information into a time-frequency spectrum of the phase coded signal, which can be a feasible solution to SAR-DFRC, bringing the possibility of realizing a light-weighted, miniaturized, low-costed, spectrum reusable, and more confidential system. A novel time-frequency spectrum shaping (TFSS) SAR-DFRC architecture based on short-time Fourier transform (STFT) is proposed for the first time. The weighted peak sidelobe level (WPSL) is considered a figure of merit for SAR imaging performance. Information is embedded by nulling the time-frequency spectrum of the waveform. Here, we develop the majorization-minimization PSL-TFSS (MMPSL-TFSS) algorithm to solve the resulting nonconvex NP-hard optimization problem. The designed waveform can ensure imaging performance and obtain high communication capacity in the meantime. Experimental and numerical results highlight the effectiveness of the proposed SAR-DFRC framework for imaging performance and the secure transition to communication information.

Bistatic synthetic aperture radar (BiSAR) has received more and more attentions because of its forward-looking imaging capability and configuration flexibility. For BiSAR moving target imaging, its non-cooperative motion leads to unknown range cell migration (RCM) and additional phase modulation. Consequently, moving target imaging in BiSAR face two main challenges: 1) The unknown RCM correction and Doppler parameter estimation are tightly coupled. 2) The Doppler parameters of the extended moving target’s different scattering points are different, i.e., the Doppler parameters are spatially variant. To cope with these problems, an optimal polar format refocusing method for bistatic SAR moving target imaging is proposed. First, the main part of tight coupling and spatial variation effects caused by the BiSAR platforms are eliminated, while the moving target is two-dimensional (2-D) defocused and shifted. Then, we analyze the characteristics of two-dimensional defocused and shifted of the moving target in BiSAR, and give the analytical expressions. On this basis, a new bistatic polar format transformation is introduced, in which the degree of freedom of defocusing result is reduced from 2-D to only one-dimension. After that, the parameter estimation and refocusing issues are transformed into a constrained optimization problem (COP), and differential evolution (DE) is applied to solve the COP and obtain the refocusing results. Finally, considering the spatial variation of the extended moving target, the compensation processing is performed to relocate each scattering point. Numerical simulations verify the effectiveness of the proposed method.

The echo signal of slow-moving target is usually submerged in clutter signal. As a consequence, ground moving target (GMT) separation is challenging because of the decreasing of detection performance. To solve this problem, this paper proposes a moving target detection method with single-channel for bistatic forward-looking SAR (BFSAR) which mainly contains three steps. First, range cell migration (RCM) is corrected by Keystone transform. Next, Extended Nonlinear Chirp Signal (NLCS) algorithm is applied to equalize the spatial variant Doppler parameters and subpress clutter spectrum broadening. At last, according to the difference of Doppler FM rate between GMT and the stationary clutter, the Short Time Fourier Transform (STFT) is devoted to separate GMT from the stationary clutter. The effectiveness of the detection method proposed in this paper has been demonstrated and illustrated by numerical simulations.

Synthetic Aperture Radar (SAR) image is highly useful in civilian and military fields, such as ship detection, maritime search and rescue. Considering the target detection from SAR image, we propose a SAR image reconstruction and target extraction method via low-rank and sparsity constrains from under-sampled data. Firstly, the low-rank and sparsity constrains are incorporated into the SAR image reconstruction model, and the objective function is established based on Robust Principal Component Analysis (RPCA) theory. Then, the Augment Lagrange Multiplier (ALM) algorithm is used to transform this objective function to a convex optimization problem. Lastly, SAR image reconstruction and target extraction are obtained by Alternating Direction Method of Multipliers (ADMM) algorithm. The simulations are conducted to verify the effectiveness of the proposed method.

Traditional synthetic aperture radar (SAR) imaging methods focus on high-resolution imaging of the observation scene. In the application of specific target imaging and detection, such as threat target search, the priori knowledge of target can be used for better task performance. Therefore, it is highly desirable to improve the image quality of the target. In this paper, we propose a SAR formation method based on the sparse dictionary. Firstly, the sparse dictionary is learned through the SAR images of a specific target via K-SVD method. Then the SAR formation model is established as a sparse reconstruction problem by incorporating the sparse dictionary. Lastly, the problem is solved via Augment Lagrange Multiplier (ALM) method and Alternating Direction Method of Multipliers (ADMM) method. The proposed method is validated by the simulation, and the results show that the proposed method can effectively reconstruct the target.

The survivability and stability of synthetic aperture radar (SAR) in the increasingly severe electromagnetic environment are widely concerned. With the development of interception receiver technology, it is very difficult to prevent the competitor from detecting the radio frequency (RF) energy of radar. Therefore, more complex intra pulse modulation waveform is needed to prevent the competitor from effectively acquiring and analyzing information. In this paper, a novel low probability of intercept (LPI) waveform optimization framework is proposed. Symmetric piecewise linear functions (PWL) is used to define the waveform search space and a constrained multi-objective evolutionary algorithm is employed to solve the waveform optimization problem. Simulation results show that the proposed waveform has good performance of low interception and auto-correlation, which is suitable for SAR imaging.

In order to overcome layover and shadowing effects, three-dimentional(3-D) synthetic aperture radar(SAR) systems have developed rapidly. One of them is linear array SAR(LASAR). Therefore, the image result may defocus due to the antenna phase centers(APCs) motion measurement error. Thus a novel LASAR motion compensation approach is proposed. Based on the analysis that linear array motion error is approximately linear, the method estimates only selected APCs' phase error and derive the whole array's phase error via extrapolation. The compensation scheme is verified with simulations.

Video Synthetic Aperture Radar (SAR) has received extensive attention in recent years due to its continuous imaging capabilities. Video SAR requires continuous image reconstruction, and there are many redundant operations in the reconstruction process. In order to meet the real-time requirements of video SAR and improve data utilization, we propose a subaperture imaging algorithm based on PFA. First, the echo is divided into multiple subapertures, and coarse focusing is achieved respectively. Then the subaperture echo is subjected to wavenumber mapping to obtain high-resolution high-frame image output. All subaperture data has only been coarsely focused once, and interpolation is not required for wavenumber mapping, which improves the efficiency of image output. Finally, we use experimental data to verify the effectiveness of the algorithm.

The clutter background poses a challenge to the detection and recognition of targets from synthetic aperture radar (SAR) images, especially when the target is submerged by the clutter. In this paper, we propose a target-oriented SAR waveform optimization method to deal with this problem. The proposed method constructs an optimization criterion jointing the signal-to-clutter ratio (SCR) and the resolution of transmitted waveform. Based on the prior information of the frequency response of the interested target, the clutter suppression performance and the range resolution performance are jointly optimized. The simulation results show that the proposed method can effectively improve the SCR of SAR image in the condition of low SCR, while the resolution performance is guaranteed.

Miniaturized radar near-field imaging systems have become a hot spot for SAR imaging in recent years. The irregular synthetic aperture caused by arbitrary motion of miniaturized system is an important factor affecting imaging quality. On the other hand, the design of fast and high resolution 3D imaging methods is also a major issue. In this paper, a fast time-frequency joint three-dimensional imaging algorithm based on aperture linearization is proposed. First, irregular synthetic aperture is adjusted through special linearization method, and then the target is quickly and accurately imaged. Through formula derivation and simulation test analysis, the results show that the proposed method is feasible and maneuverable, which can solve the current problems.

Synthetic aperture radar (SAR) imaging has always been an area of concern and exploration by researchers. The restoration of elevation information is a core issue of SAR imaging. Therefore, this paper proposes a method of three dimensional (3D) surface reconstruction with multistatic SAR. The presence of elevation information makes the target shift in the SAR image which is related to the configuration of SAR. This method utilizes the range history of the scattering point, combining the quantitative relationship between the receivers and the transmitters, to realize the reconstruction of the three-dimensional surface. Simulations prove the effectiveness of the proposed algorithm.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) can achieve high-resolution Earth observation with superior system flexibility and efficiency, which offers huge potential for advanced SAR applications. In this article, the echo characteristics of GEO-SA-BiSAR are analyzed in detail, including range history, the Doppler parameters, and spatial variance. The distinct features of GEO-SAR and airborne receiver result in the failure of the traditional bistatic SAR range model and imaging methods. In order to deal with these problems and achieve high-precision data focusing on GEO-SA-BiSAR, this article first proposes a novel range model based on one-stationary equivalence (RMOSE) to accommodate the distinctiveness of the GEO-SA-BiSAR echo, which changes with orbit positions of GEO transmitter. Then, a 2-D frequency-domain imaging algorithm is put forward based on RMOSE, which solves the problem of the 2-D spatial variance of GEO-SA-BiSAR. Finally, simulations are presented to demonstrate the effectiveness of the proposed range model and algorithm.

Bistatic forward-looking spotlight synthetic aperture radar with stationary transmitter and maneuvering receiver (STMR-BFSSAR) is a promising sensor for various applications, such as the automatic navigation and landing of maneuvering vehicles. Because of the bistatic forward-looking configuration and the receiver's maneuvers, conventional image formation algorithms suffer from high computational complexity or small size of a well-focused scene if applied to STMR-BFSSAR. In this article, we propose a wavenumber-domain algorithm for STMR-BFSSAR image formation, which is termed the bistatic-range-Doppler-aperture wavenumber algorithm (BDWA). First, a novel range model in bistatic-range and Doppler-aperture coordinate space instead of conventional Cartesian coordinate space is established by employing the elliptic polar coordinate system and the method of series reversion. The novel range model not only makes the echo's samples to be regular along the direction of the bistatic-range wavenumber axis but also constructs a curved wavefront close to the true wavefront. Second, an operation termed wavenumber-domain gridding is conceived to regularize the echo's samples along the Doppler-aperture wavenumber axis, which can be implemented by 1-D interpolation. The proposed algorithm significantly outperforms the conventional algorithms in terms of computational complexity and scene size limits. Both point and distributed targets are simulated for two STMR-BFSSAR systems with different parameters. The simulation results verify the validity and superiority of the proposed BDWA.

An efficient echo simulation algorithm considering topography in time domain for bistatic SAR is proposed in this paper. By using subaperture processing and equivalent scatter, this method can implement efficient echo simulation. At last, the effectiveness of this method is verified by numerical simulation.

This paper presents a long-time integration method for passive radar detection of marine target with multi-stage motions using global navigation satellite system (GNSS) as illuminator. Owing to the complex motion, the range cell migration (RCM) and the Doppler parameters of the target echo pertaining the multiple motion stages are different. To solve the problems, in this method, first the range symmetry transform (RST) is applied to correct the RCM over the multiple stages, after which a one-dimensional azimuth signal can be directly extracted from the two-dimensional echo. Then, the conjugate integration processing (CIP) is performed to accumulate the target energy during each stage into Doppler centroid (DC) and Doppler frequency rate (DFR) domain. Finally, in order to settle the difference in Doppler parameters between multiple stages, the DC-DFR maps are projected and combined in high-dimensional Doppler parameter domain after Doppler frequency shift compensation, implementing the well integration of the echo over the entire long time. The effectiveness of the proposed integration method is demonstrated by simulations.

For a non-cooperative ground moving target (GMT), its complex motion inevitably induces unknown range cell migration (RCM) and Doppler frequency migration (DFM) in Bistatic SAR (BiSAR) echo. Unfortunately, the existence of RCM and DFM often results in a deteriorative or even unacceptable performance on target detection and parameter estimation. In this paper, a novel coherent integration approach for BiS-AR GMT detection and parameter estimation based on two-dimensional (2-D) deramp processing is proposed. First, the deramp processing along the range frequency is exploited for GMT's unknown RCM correction. Then, the second deramp function along the slow time is constructed to reduce the order of azimuth phase and eliminate the effect of DFM. Finally, GMT's energy can be accumulated into a peak and Doppler parameter can be obtained via azimuth fast Fourier transform (FFT) and range inverse FFT. The proposed approach is computationally efficient, since it can be implemented only by complex multiplications and FFT. Simulations are given to verify the effectiveness of the proposed approach.

In this paper, a weighted dense connected convolutional network(WDCCN) is proposed for SAR image super-resolution. In the network, to enhance feature propagation and the super-resolution performance, each layer will receive the output from all the previous layers in a different weight proportion. At last, the experimental results indicate that weighted dense connected convolutional network can realize SAR image super-resolution well.

Echoes of bistatic forward-looking synthetic aperture radar (BFSAR) disperse to multiple range cells and exist spatial frequency extension as well as Doppler spectrum extension (i.e., namely space-time extension). Furthermore, the characteristics of BFSAR clutter are strongly nonstationary and spatial variants. Because of the abovementioned issues, clutter and moving targets are fully overlapped in the initial 3-D space-time-range raw data domain, and the clutter cannot be suppressed effectively. To solve this problem, a BFSAR multipulse displaced phase center antenna (MP-DPCA) method is proposed in this article. First, keystone transform without Doppler ambiguity is applied to remove the coupling between the space-time and range domains. Hence, the overall complex 3-D processing in the space-time-range domain is reduced to independent 2-D processing in each space-time domain. Subsequently, a spatial-dechirp processing is applied in the space-time domain to eliminate the spatial frequency extension. Meanwhile, Doppler parameters of clutter point scatterers are equalized by nonlinear chirp scaling processing in the frequency and time domains. Accordingly, the Doppler spectrum extension of point scatterers can be eliminated by a uniform azimuth dechirp processing. After aforesaid three steps, the clutter and moving targets are separated in the space-time domain. Finally, based on the linear phase difference of clutter between the channels, a multipulse canceller can be designed to suppress the clutter. Compared with the existing space-time adaptive procession (STAP) and DPCA methods, this method not only overcomes the nonstationary problem in BFSAR but also conquers the strict application conditions of DPCA. Simulation results are given to verify the effectiveness of the proposed method.

This paper briefly introduces the basic scheme of a maritime moving target detection and localization method with multiple BeiDou satellites as transmitters. To detect the target and localize it, a receiver local-coordinates is constituted, whose axes are X, Y, and Doppler frequency rate (DFR), and the received echoes from multiple satellites are integrated into the local-coordinates. In this method, first symmetrical keystone transform (SKT) is applied to correct range cell migration (RCM) and set Doppler centroid (DC) to zero. Then, dechirp-FFT is applied to integrate each echo in fast-time and DFR domain. Finally, each DFR integration result is back projected to local-coordinates according to its bistatic range. From the final integration result, target can be detected and localized simultaneously. The effectiveness of the proposed method is demonstrated by simulations.

Time and frequency synchronization is a crucial problem for Bistatic SAR systems. In this paper, frequency reference error of bistatic SAR system is analyzed firstly. Then the time and frequency synchronization errors are deduced and the intrinsic relationship between them is revealed. At last the effect of the frequency reference error is studied. Simulation and experiment results are given to verify the effectiveness of the analysis.

This paper proposes a clutter suppression method for the bistatic forward-looking SAR(BFSAR). The proposed method relies on a proper processing of the received echoes. First, keystone transform (KT) and the higher-order RCM correction are applied to correct the range cell migration (RCM). Then, azimuth nonlinear chirp scaling (NLCS) and azimuth dechirp are applied to suppress the spatial-variant Doppler spectrum extension. After the aforesaid two steps, the moving targets and clutter can be separated in the space-time domain, and the clutter signal with different pulses contains only one constant phase difference. Thus, the two-pulse clutter canceller can be designed to suppress the clutter. Finally, it is suggested that this proposed method can improve the signal to clutter ratio (SCR) and are beneficial for the subsequent signal processing, e.g. target detection. Simulation results are provided to validate the effectiveness of the clutter cancellation method.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In BFSAR, the compensation of the spatially variant motion errors is of great significance to get a well-focused image. In this paper, a motion compensation method based on keystone transform and modified autofocus back-projection is presented to deal with this problem. Keystone transform is applied to remove the spatial variation of range cell migration (RCM) and the first-order term of RCM errors simultaneously, prepares for the following modified autofocus back-projection, which can eliminate the high-order term of azimuth phase errors. Simulation results verify the validity and efficiency of the presented method.

Geosynchronous (GEO) spaceborne-airborne bistatic synthetic aperture radar (GEO-BiSAR), where the high-altitude transmitter provides continuous illumination for the receiver, is capable of providing benefits to remote sensing applications. However, obtaining a focused image with high efficiency is a challenging work. The severe 2-D space-variant range cell migration and Doppler modulation introduced by the two moving platforms make the echo hard to be focused. Moreover, the azimuth ambiguity due to the low pulse repetition frequency adopted by the GEO illuminator seriously deteriorates the quality of the final image. In order to simultaneously suppress the azimuth ambiguity and obtain well-focused images, a weighted fast factorized backprojection (FFBP) method is proposed for multichannel GEO BiSAR in this letter. First, the Doppler ambiguity of GEO BiSAR is analyzed and the azimuth multichannel receiving technique is introduced. Then, the multichannel transfer function for GEO BiSAR is derived. Based on the multichannel transfer function, a weighted FFBP method is proposed to achieve accurate focusing and ambiguity suppression. Finally, the simulation results validate the effectiveness of the proposed method.

An echo model plays a pivotal role in the processing of synthetic aperture radar (SAR) data. It has been shown that the widely adopted echo models based on stop-and-go approximation (SAG) are unsuitable for use in many emerging radar systems with high resolution or high-speed platforms. More accurate echo models without SAG have been proposed for some special monostatic scenarios, such as low-Earth-orbit SAR and geosynchronous SAR. However, due to their inherent assumptions, such as linear and constant-speed/orbit-based motion, these conventional echo models cannot be applied to general cases, which involve bistatic configurations and maneuvers. Maneuvers or nonlinear/inconstant-speed motion are very common in cases, such as circular SAR and maneuvering-platform-borne SAR. In this letter, we propose a generalized echo model for bistatic SAR, which accounts for maneuvers of the platforms during pulse propagation. Moreover, we obtain a high-precision closed-form expression of the model by approximating high-order terms of the accurate range history of the receiver. Compared with the conventional models, the proposed echo model can be applied to a much greater number of cases, ranging from slow and constant-speed platforms to fast and maneuvering platforms. Theoretical analysis and backprojection-based image formation simulations confirm the validity of the proposed echo model.

Back-projection (BP) is a reliable synthetic aperture radar (SAR) imaging algorithm because of its high-resolution and strong adaptability. However, it is hard to implement because of its high computational complexity. Fast factorized BP (FFBP) is a new way to fix this problem. Like traditional BP, FFBP is compatible with arbitrary flight paths if the track deviations are measured within fractions of a wavelength. However, when the motion information is not accurate enough, autofocus become an important way to get well-focused images. In this paper, we present an effective autofocus method for FFBP to solve the imaging problem caused by platform's motion errors. First, an image quality evaluation function with unknown phase error based on image sharpness for FFBP is established. Then, the phase error computation for autofocus is modeled as an optimization problem. Second, the coordinate descent (CD) and secant processing are introduced to the maximum image sharpness problem. The proposed method keeps the rapid imaging performance of FFBP and solves well the motion error compensation problem. In the end, simulated data and real data were used to verify the effectiveness of the proposed algorithm.

Being capable of providing weather-independent, day-and-night, forward-looking, and high-resolution images, bistatic forward-looking synthetic aperture radar (BFSAR) is a promising sensing technique in applications such as the scene-matching-aided navigation for recently emerging high-speed maneuvering platforms (HMPs). Because of the high speed and the great maneuverability of HMPs and the bistatic forward-looking configuration, conventional image formation algorithms, such as polar format algorithm (PFA), are no longer suitable for HMP-borne BFSAR (HMP-BFSAR). Hence, in this paper, we propose a novel PFA for HMP-BFSAR image formation. In the proposed PFA, a range model, termed as quasi-continuous -move range model, is established by taking the maneuvers of the receiver during pulse propagation into account instead of adopting stop-and-go approximation. Moreover, to take advantage of the collected k -set efficiently, an affine mapping, termed as k -set affine mapping, is conceived to transform the parallelogram-shaped k -set support region to a horizontal and quasi-rectangular one. Furthermore, to compensate for the defocus effect induced by wavefront curvature, a closed-form refocus filter based on the implicit function theorem is derived. Both point target simulation and distributed target simulation are presented in this paper. The simulation results show that the proposed PFA significantly outperforms the conventional PFA in terms of focusing quality and computational efficiency when applied to HMP-BFSAR image formation.

Moving targets are usually defocused in synthetic aperture radar (SAR) images due to across range unit (ARU) range migration and Doppler migration caused by unknown motion parameters. To eliminate the influence of these issues on moving target imaging, a novel Doppler parameters estimation method for moving targets is proposed. First, second-order keystone transform (SOKT) is applied to remove range curvature. Then, a deramp function is proposed to reduce the order of coupling and remove the residual first-order coupling. In the following, Doppler parameters can be obtained through range IFFT and azimuth FFT. The proposed method is capable of accumulating the energy of ARU target's trajectory into a peak and obtaining Doppler parameters in range-Doppler domain with low computational complexity. Furthermore, it has better antinoise performance comparing with the Hough transform and Radon transform. The effectiveness of the proposed method is validated by simulated data for ground moving targets.

In synthetic aperture radar (SAR) imagery, images of ground moving targets (GMTs) are smeared, distorted, and shifted. Current GMT imaging methods are mostly based on range-Doppler algorithms, which have two main drawbacks: 1) coupling between range cell migration correction (RCMC) and Doppler parameter estimation and 2) cross terms degrade the performance of the nonlinear estimation methods. In this paper, an optimal 2-D spectrum matching method for SAR GMT imaging is proposed. The main innovation or advantage of this method is that the GMT imaging problem is transformed into a constrained optimization problem, and differential evolution is applied to guarantee a high-processing efficiency. As they are associated with the Doppler centroid variation compensation and range shift compensation processing, all GMT point scatterers can be well focused and well located. Compared with the current methods, the improvements exhibited by this method include three main benefits: 1) RCMC and Doppler parameter estimation can be simultaneously accomplished; 2) both along- and cross-track GMT velocities can be simultaneously estimated; and 3) this method can be applied to both monostatic and bistatic SARs. Numerical simulations and experimental data processing have verified the effectiveness and robustness of the proposed method.

This article presents an adaptive large-size moving-target imaging technique for bistatic forward-looking SAR (BFL-SAR). The main problems of this issue are that not only the echo characteristics (including range cell migration and Doppler parameters) are unknown, but also the spatial-variances of these characteristics are nonlinear for different point-scatterers. The proposed technique relies on a proper processing of the data aiming at, first, to correct the range walk by applying keystone transform over the whole received echo, and then, the relationships between the unknown high-order RCM, the nonlinear spatial-variances of the Doppler parameters, and the speed of the mover, are established. After that, using an adaptive NLCS technique, not only the unknown high-order RCM can be accurately corrected, but also the nonlinear spatial-variances of the Doppler parameters can be balanced. Numerical simulations show the effectiveness of this adaptive large-size moving-target imaging technique to be employed in BFL-SAR frameworks.

In this paper, SAR MT imaging and velocity estimation method is proposed. The validity of this method is verified by numerical simulations. The main idea behind this method is to transform the PE problem to be a SOP problem. The advantages of this method include two main aspects: (i) This method can handle the MT imaging problem for different SAR modes, such as mono-static SAR, bistatic SAR, etc.; (ii) Both the along-track and cross-track velocities of theMT can be simultaneously estimated. In addition, since the focusing processing is conducted in 2D spectrum domain and the optimal criterion is the local minimum entropy, this method don't need to find a dominated point scatterer during the process.

Bistatic forward-looking synthetic aperture radar (BFL-SAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of an aircraft. Until now, BFL-SAR imaging theories and methods have been researched for stationary targets. Unlike the stationary target, the motion of a ground-moving target (GMT) induces unknown range cell migration and additional modulation of the azimuth signal. Thus, to finely image the GMT, one must obtain its velocity parameters accurately, but they are usually unknown. In this paper, a novel GMT imaging and velocity estimation method, which is based on mismatched compression, is proposed for BFL-SAR without a priori knowledge of the GMT's velocity parameters. The main idea behind mismatched compression is to use a presumed azimuth reference function for performing correlated operation with the azimuth signal of the GMT. In general, the Doppler parameters of the presumed azimuth reference function are different from those of the GMT's azimuth signal because the velocity parameters of the GMT are unknown. Therefore, the correlation operation referred to earlier is actually mismatched compression, and the resulting image is shifted and defocused. The shifted and defocused image is utilized to get the real Doppler and velocity parameters of the GMT. The advantage of this method is that not only the GMT can be well focused but also the GMT's velocity can be simultaneously obtained. In addition, this method needs only monochannel antenna. The proposed BFL-SAR GMT imaging and velocity estimation method is validated by numerical simulations.

Geosynchronous synthetic aperture radar (GEO-SAR) offers new opportunities for continuous Earth observation missions with large coverage and short revisit cycle. The unique features of GEO-SAR present huge potentials for bistatic observation applications. In this paper, the concept and advantages of GEO bistatic SAR (GEO-BiSAR) are first investigated. The system consists of a GEO illuminator and an airborne receiver, such as an airplane or a near-space vehicle. Compared with a monostatic GEO-SAR system, the bistatic configuration can provide finer spatial resolution and higher signal-to-noise ration (SNR) with less system complexity. The spatial resolution characteristics are then analyzed based on generalized ambiguity function, where the time-varying GEO velocity, Earth rotation, and ellipsoid Earth surface are taken into consideration. Meanwhile, the bistatic SNR is analyzed using the integration equation model. In this paper, the mission design for GEO-BiSAR aims at identifying a set of receiver flight parameters and bistatic configurations to obtain the desired spatial resolution and SNR. Based on the desired imaging performance of a specific application background, the mission design process is modeled as a nonlinear equation system (NES). Finally, a mission design method based on fast nondominated sorting genetic algorithm is proposed to solve the NES and obtain multiple optimal solutions to guide the receiver flight missions. Examples of the mission design process are given to validate the effectiveness of the proposed method. The results of the mission design can be conveniently used to guide the receiver flight mission for the desired imaging performance, which is highly desirable in practical applications.

The ground moving target (GMT) model has been widely employed in modern coherent radar systems, such as the synthetic aperture radar (SAR) and the bistatic SAR (BiSAR). For the coherent radar systems, GMT imaging necessitates the compensation of the additional azimuth modulation without a priori knowledge of the GMT's motion parameters. That is to say, it is necessary to estimate the Doppler parameters of the GMT before the azimuth compression processing. For conventional estimation methods, such as the map drift (MD) method and the phase gradient auto-focus (PGA) method, a searching procedure is necessary and leads to an expensive computational cost. In this paper, a Doppler parameter estimation method based on mismatched compression is proposed. One advantage of this method is that it doesn't need the searching procedure. In addition, another advantage of this method is that both the Doppler centroid and the Doppler frequency rate of the GMT can be simultaneously estimated according to the relationships among the Doppler parameters, the positional offset and the boarding width of the mismatched imaging result. The theoretical analysis and numerical simulations validate that the proposed method works well with different signal to noise ratio.

Highly squint synthetic aperture radar (SAR) data focusing is a more challenging and difficult task than the side-looking SAR due to the strong 2-D coupling of echo signal induced by the imaging mode. Although several algorithms have been proposed, they fail to take into consideration the spatial variance of linear range cell migration (RCM). Moreover, the RCM correction (RCMC) process in range frequency and azimuth time domain by phase multiplication brings the problem of azimuth variation of the Doppler centroid, which has a great influence on the azimuth focusing. In this letter, an algorithm based on keystone transform (KT) and azimuth extended nonlinear chirp scaling (ENLCS) is derived to deal with these problems. A new method for higher order RCMC is derived to remove the residual RCM after KT. Then, azimuth ENLCS is performed to equalize the azimuth-variant Doppler centroid and FM rate. The simulation results verify the effectiveness of this algorithm.

The combination of frequency-modulated continuous-wave (FMCW) technology and bistatic synthetic aperture radar (SAR) promises a small, light-weight, cost-effective and high-resolution remote sensing system. In this paper, an Omega-k imaging algorithm based on linearization theory to focus the raw data of translational invariant (TI) bistatic FM-CW SAR is proposed. This method utilizes a bistatic point target reference spectrum (BPTRS) of generalized Loffeld's bistatic formula (GLBF), where the motion during the long signal duration time is taken into account. The proposed BPTRS significantly simplify the TI bistatic FMCW SAR processing. Based on the spatial linearization of GLBF, the Stolt transformation is further derived. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method.

Current literatures and reports about Bistatic forward-looking SAR (BFSAR) imaging theories are mostly concentrated on static scene. In this paper, a novel GMT detection and imaging method in Doppler-rate domain for BFSAR is presented. The steps of this method involve bulk-deramp filtering operation, Keystone transform (KT), extend azimuth nonlinear chirp scaling (EA-NLCS) processing and the last detection step based on product second-order ambiguity function (PSAF). After the above steps of the method, if the PSAF of a certain range bin has more than one sharp peak, we can argue that there is a GMT in the relative range bin. Then using the GMT's Doppler rate estimated by PSAF, the echo of GMT can be focused, thus the goals of GMT detection and imaging for BFSAR are achieved. Numerical simulations verify the effectiveness of the proposed method.

One-Stationary bistatic forward-looking SAR (OSBFSAR) is a SAR system that using a geostationary satellite or a near-space low-speed platform as the transmitter and using an airborne or a missile as the receiver. Current literatures and reports about OSBFSAR are mostly about imaging theory and stationary scene imaging methods. In this paper, we propose an OSBFSAR moving target detection and imaging method with a linear antenna array. This method is associated with the two-dimensional spatial variation imaging technology of OSBFSAR and the extended velocity-SAR technology. Using this method, not only the stationary clutter can be suppressed but also the moving targets can be detected and focused well. Some numerical experiments are given to verify the validity of the method shown in this paper.

Ground moving target detection (GMTD) for squint synthetic aperture radar (SAR) is a more challenging task than that of the broadside SAR. The severe range and azimuth coupling induced by the squint mode combined with the unknown motion parameters makes the problem of indicating the moving targets more involved. In this paper, a novel ground moving target detection method for squint SAR is proposed. Firstly, an extended Keystone Transform (KT) is performed to remove the range cell migrations (RCMs) of both the stationary and moving targets. Secondly, the range compressed data undergoes two extended azimuth nonlinear chirp scaling (EANLCS) process separately with different sets of parameters based on the stationary scene. Then, after azimuth de-ramp processing, two images are obtained where all the targets are well focused except for the moving targets. Finally, by subtracting the amplitudes of the two images, the stationary targets are eliminated and the moving targets are detected. This algorithm is suitable for large squint angle cases and is computationally efficient. Simulation results verify the effectiveness of the algorithm.

The butterfly algorithm is introduced to achieve fast imaging of near space circular synthetic aperture radar (SAR) raw data with high resolution and large scene. The fast algorithm benefits from projecting the frequency domain signal after matched filtering to subimage. Quad tree structures constructed on both signal and image spaces are used to project signal recursively. Following the traversal of the quadtree, the image resolution is ameliorated gradually. The algorithm run on parallel processors and fast imaging is accomplished. Except for the ability of precise and quick imaging, large scene imaging is also achieved. The simulation results show the high quality image with good resolution.

Near-space Synthetic Aperture Radar (SAR) possesses useful features such as High-Resolution Wide-Swath (HRWS) and high revisiting frequency etc., which are difficult for the conventional SAR, e.g. spaceborne and airborne SAR, to simultaneously provide. Slow-speed SAR is introduced to solve the contradiction between high resolution and wide swath of Near-space SAR and a frequency domain algorithm based on Two-Step Processing Approach (TSPA) and Stolt interpolation is proposed. Simulations are provided to demonstrate the validity of the proposed algorithm.

This paper presents a two-dimensional (2-D) generalized omega-K algorithm for high-precision processing of the data of missile-borne Synthetic Aperture Radar (SAR) with constant acceleration. The almost adequate expression of the 2-D spectrum of the echo signal is obtained by the Method of Series Reversion (MSR). Then a 2-D generalized Stolt interpolation is carried out. The proposed algorithm can be applied to situations involving large dive and squint angle and high maneuvering speed. Simulations are presented to demonstrate the validity of the proposed algorithm.

Doppler parameters, range cell migrations (RCMs), and higher order coupling terms in the raw data of translational variant bistatic synthetic aperture radar (TV-BiSAR) exhibit 2-D spatial variations. The 2-D spatial variations result in significant performance degradation of TV-BiSAR imaging. To solve these problems, an Omega- k imaging algorithm based on linearization theory is proposed in this letter. Compared with the traditional Omega- k algorithm that uses the 1-D Stolt transformation to eliminate only the spatial variations in the range direction, the proposed algorithm applies the 2-D Stolt transformations to achieve the goal of both 2-D frequency linearization and 2-D spatial-domain linearization. After the 2-D Stolt transformations, a focused image can be obtained by performing a 2-D inverse fast Fourier transform (IFFT). In the proposed Omega- k imaging algorithm, the 2-D spatial variations of the Doppler parameters, RCM, and higher order coupling terms for TV-BiSAR can be simultaneously eliminated. However, in previous publications about BiSAR imaging algorithms, the spatial variations in the azimuth direction are barely considered, which reduces the imaging accuracy. Numerical simulations verify the effectiveness of the proposed method.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications, such as self-navigation and self-landing. In the mode of BFSAR with a stationary transmitter (ST-BFSAR), the two-dimensional spatial variation makes it difficult to use traditional data focusing algorithms. In this letter, an imaging algorithm based on keystone transform and nonlinear chirp scaling (NLCS) is proposed to deal with this problem. Keystone transform is used to remove the spatial variation of range cell migration. NLCS can eliminate the variation of azimuth reference function. Numerical simulations show that by combining first-order keystone transform and azimuth NLCS operation, the raw data of ST-BFSAR can be well imaged.

In translationally variant (TV) bistatic synthetic aperture radar (BSAR), 2-D spatial variation is a major problem to be tackled. In this paper, a generalized Omega-K imaging algorithm to deal with this problem is proposed. The method utilizes a point target reference spectrum of the generalized Loffeld's bistatic formula (LBF) (GLBF). Without the bistatic-deformation term, GLBF is the latest development of LBF. Similar to the monostatic case, it has a much simpler form than other point target reference spectra. Based on the spatial linearization of GLBF, the Stolt mapping relationship is derived. Different from the traditional Omega-K algorithms for monostatic SAR and translationally invariant BSAR, this approach uses a 2-D Stolt frequency transformation. Through this transformation, the method can deal with the 2-D spatial variation. It can also consider the linear spatial variation of Doppler parameters, which is usually not considered in the previous publications on bistatic Omega-K algorithms. This method can handle the cases of TV-BSAR with different trajectories, different velocities, high squint angles, and large bistatic angles. In addition, a compensation method for the phase error caused by the linearization is discussed. Numerical simulations and experimental data processing verify the effectiveness of the proposed method.

In this paper, an omega-K imaging algorithm to focus the raw data of translational invariant (TI) bistatic synthetic aperture radar (BSAR) is proposed. The method utilizes a point target reference spectrum of generalized Loffeld's bistatic formula (GLBF). Without the bistatic deformation term, GLBF is the latest development of Loffeld's bistatic formula. It is comparable in precision with the method of series reversion (MSR), but it has a much simpler form than MSR and a similar form to a monostatic case. Based on the spatial linearization of GLBF, the Stolt transformation relationship is derived. The method can consider the linear spatial variation of Doppler parameters, which is always ignored in previous publications about bistatic omega-K algorithms. This method can handle the cases of TI BSAR with high squint angles and large bistatic degrees. In addition, a compensation method for the phase error caused by the linearization is discussed. Numerical simulations and experimental data processing verify the effectiveness of the proposed method.

Due to left/right Doppler ambiguity, and the small difference in Doppler frequencies of adjacent points in flight direction, conventional monostatic SAR can not be used for forward-looking imaging. Then bistatic SAR with transmitter and receiver mounted on different platforms is coming into the eyes of researchers. In this paper, the feasibility of forward-looking imaging using airborne bistatic SAR is studied. Firstly, the potential two-dimension resolution ability of bistatic SAR for forward-looking imaging is discussed from the aspect of iso-Doppler and iso-range line. Then the airborne bistatic SAR experiment using a side-looking airborne transmitter and a forward-looking airborne receiver is described, and the preliminary results are presented. To the authors' knowledge, this is the first bistatic forward-looking SAR experiment in the world that both transmitter and receiver are mounted on aircraft platforms.

The generation of SAR echo is very important for both the system design and the validity test of imaging algorithm. Common time-domain-based echo generation methods are usually time-consuming and inefficient. Hence, this article will focus on the research of efficient echo generation methods. In translational variant bistatic mode, two-dimensional (2D) spatial variability is the major problem to be faced with in efficient echo generation. To solve this problem, this paper proposes an efficient echo generation method of translational variant bistatic SAR based on 2D inverse Stolt mapping. The proposed method first uses 2D FFT to generate 2D linear spectrum, and then completes nonlinearized operation of the spectrum by 2D inverse Stolt mapping. This process fully considers the 2D spatial variability in translational variant bistatic mode, which guarantees the accuracy of the generated echo. Finally, simulation results are presented to verify the validity of the proposed method.

A novel ground moving target detection and imaging model called Near-Space Slow SAR (NSS-SAR) is introduced in this paper. It is not only effective for fast-moving targets but also slow-moving and micro-moving targets, which is hardly possible for conventional airborne and spaceborne SAR. Meanwhile, this model only needs mono-channel to distinguish Doppler signature of moving targets from the competing ground clutter returns via Doppler filtering. In addition, following analysis demonstrates that the NSS-SAR also has the potential to simultaneously achieve high-resolution and wide-swath (HRWS) imaging. Simulations given at the end of this paper verify the validity of the new NSS-SAR mono-channel moving target detection and imaging model.

One-stationary bistatic side-looking synthetic aperture radar (OS-BiSAR) data are more challenging to process than the translational azimuth-invariant bistatic counterparts because 2-D spatial variances exist in OS-BiSAR. To overcome the problem, extended-keystone-transform (EKT)-nonlinear-chirp-scaling (NLCS) imaging algorithm is proposed in this letter. The key steps of the proposed algorithm are deducing the twice EKTs to deal with the 2-D spatial variance of range cell migrations and equalizing the 2-D spatial variant Doppler FM rates by using the associative NLCS. As a result, raw data of OS-BiSAR can be well focused even with the 2-D spatial variances. The simulations given at the end of this letter verify the effectiveness of this imaging algorithm.