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.

Obtaining the clear contours of ship targets via Synthetic aperture radar (SAR) is extremely valuable for monitoring the sea. Now there are many deep learning imaging methods for ground scenes with good results, but they will face three main challenges when imaging ship targets: 1) The translational and rotational motions of ship targets during travel and due to waves respectively bring spatial invariant and variant errors that are tough to be estimated and compensated, resulting in defocused SAR imaging results; 2) The varying motion of ships demands high generalization ability of the imaging reconstruction network to adapt to the ship targets with sample-wise variant motion parameters; 3) Since ships are noncooperative targets, the accuracy of motion parameter estimation should be evaluated based on image quality, whereas the available SAR image quality assessment functions exhibit limited robustness. To address these issues, this article proposes a deep learning-based SAR imaging framework for ship targets via deep unfolding. Firstly, the motion model and characteristics of ship targets are analyzed, and the SAR imaging model for ship target with complex translational and rotational motion is established. Secondly, an imaging network with high generalization ability is proposed to adapt echoes for imaging under different motion parameters. On this basis, a SAR ship image quality assessment network is proposed to assess the imaging results of SAR ship targets with different focusing qualities. Then, the high-resolution imaging problem of ship targets is regarded as a motion parameter optimization problem, with the image quality assessment results as the objective function. Finally, this problem is optimized to search for the most accurate translational and rotational motion parameter variables of the ship target, achieving error compensation and imaging. The validity of the method is verified though the simulation of point targets and real SAR scenario data.

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.

The Microwave Photonic (MWP) radar, designed for ultrawideband signal emission in high-precision imaging, encounters challenges in ultra-high-resolution images where strong scattering points obscure weak edge scattering areas of the target. Consequently, the resultant images exhibit isolated strong points instead of continuous edges on the physical structure, diminishing the interpretative capacity of highresolution radar images. This paper proposes a Microwave Photonic Inverse Synthetic Aperture Radar (MWP-ISAR) edge recovery method based on morphological theory to address this issue. The method employs image preprocessing, including dilation and edge detection, followed by the hough transform (HT) for accurate edge localization. Subsequently, An adaptive neighborhood enhancement operator is used to restore the target’s edge features. Notably, the method adeptly tackles the computational cost problem arising from numerous parameter estimates in the image reconstruction process based on parametric models while also automatically extracting edges for restoration. Finally, the proposed method undergoes validation and quantitative evaluation using authentic aircraft data, substantiating its effectiveness in enhancing radar image interpretation.

The separated platforms of a bistatic synthetic aperture radar (SAR) introduce synchronization error in the echo, resulting in offset and defocused target position and other characteristics. To deal with the problem, this paper firstly analyzes the synchronization error characteristics, then proposes a synchronization error separated method based on variational mode decomposition techniques, and ultimately uses the echo after the error separation to get the high precision SAR imaging results. Simulation experiment results verify the effectiveness of the proposed method.

High-resolution synthetic aperture radar (HR-SAR) is extensively used in ground remote sensing applications, including disaster monitoring and resource crop assessment. This is attributed to its exceptional high-resolution imaging capabilities. Microwave photonics technology plays a crucial role in enhancing the performance of SAR systems. It enables the direct emission of ultra-wideband signals across multiple frequency bands. However, this advancement also makes microwave photonic synthetic aperture radar (MWP-SAR) susceptible to complex and diverse electromagnetic interference. Particularly, it is vulnerable to radio frequency interference (RFI), and this interference seriously affects the high-resolution imaging results. In order to solve these problems, an MWP-SAR imaging algorithm based on depth spectral sensing and spectral reconstruction that is suitable for complex electromagnetic environments is proposed. Simultaneously, a sensing-removing-recovering (SRR) anti-interference imaging framework is established. To address the high-precision detection of various unknown interferences in MWP-SAR echoes, a deep learning network is constructed. This network is based on spectrum sensing theory and achieves an interference detection probability greater than 98% in various interference scenarios. Subsequently, targeting the continuous spectrum loss in the MWP-SAR signal after interference removal, a signal reconstruction algorithm is proposed. This algorithm employs Toeplitz transformation for signal loss scenarios, facilitating the recovery of signals with extensive spectrum loss post-interference removal. The factor group sparse regularization (FGSR) algorithm is used to quickly solve the signal recovery problem. Through simulation and measured data processing, the superiority of the proposed algorithm over existing interference suppression imaging algorithms is demonstrated.

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.

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.

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.

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) image registration technology is widely used, but some problems are exhibited in the available technologies, such as feature extraction, loss of phase information, low computational complexity, and so on. To cope with these problems, in this paper, a method regards target Attributed Scatter Center (ASC) as the feature is proposed. ASC model highlights electromagnetic characteristics, which can better represent SAR images than optical characteristics. First, the parameter set of target ASC model is estimated by Iterative Shrinkage Thresholding Algorithm (ISTA), as a stable point feature registration descriptor of the image. Then, Nearest Neighbor Distance Ratio (NNDR) method is used to find the feature matching points of the reference image and the image to be registered. The matching accuracy is improved by the Random Sample Consensus (RANSAC) method. Finally, by transformation model and image offset, image registration and phase retention are realized. The simulation results demonstrate that the proposed method enables a better registration performance since the scattering characteristics are effectually utilized.

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.

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.

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.

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.

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.

This paper unrolls some preliminary results of a spaceborne-airborne bistatic SAR experiment, conducted in October, 2020 in Zhejiang, China, using GF-3 SAR satellite as the transmitter. Some important aspects of the experiment are firstly introduced, including bistatic acquisition geometry, receiving system, signal synchronization and theoretical spatial resolution. Then, the imaging processing flow is given, with emphasis on the data synchronization. A modified BP imaging method is proposed, which is suitable for direct signal synchronization scheme commonly used in spaceborne-airborne experiments. Finally, the imaging result is given with evaluation of the spatial resolution.