Video Synthetic Aperture Radar enables high-resolution, continuous imaging of observed scenes under all-weather and day-night conditions. Nevertheless, video SAR image reconstruction remains challenged by substantial data volumes and high computational complexity. This study addresses these limitations by exploiting temporal redundancy in sequential frame data. Through systematic analysis of video SAR data characteristics, we formulate video SAR imaging as a sparse tensor recovery problem by introducing a tailored correlation function to leverage inter-frame dependencies. An iterative solution is derived by integrating the alternating direction method of multipliers (ADMM) and proximal-alternating inexact minimization (P-AIM) frameworks. Based on this formulation, we propose an imaging network (ViSAR-UTNet) by unfolding the iterative process into a Transformer architecture. ViSAR-UTNet comprises two core modules: a weighted self-attention (WSA) mechanism that learns inter-frame correlations and a linearized ADMM (LADMM) operator for sparse tensor recovery. By leveraging the unfolded Transformer structure, ViSAR-UTNet effectively exploits data redundancy, thereby enabling high-quality video reconstruction from reduced measurements. Experiments on synthetic and real datasets are conducted to validate ViSAR-UTNet. The results demonstrate enhanced reconstruction accuracy and computational efficiency of the proposed method.

Due to its all-day and all-weather capability, synthetic aperture radar (SAR) plays an important role in many remote sensing and monitoring applications. However, conventional SAR image reconstruction methods generally perform undifferentiated imaging, complicating target detection. To address this challenge, we propose a SAR image reconstruction method based on self-attention deep prior learning for differentiated image reconstruction and target detection. The proposed method can separate the target from the clutter using their feature priors during image reconstruction, thus helping improve target detection performance. Specifically, a deep prior learning operation based on a self-attention convolutional neural network (SACNN) is proposed. SACNN can help enhance target and suppress clutter by learning both the local and global features. Finally, the proposed method is implemented through an unrolled deep network with a loss function designed to make the reconstructed image beneficial for target detection. Simulation experiments have been conducted to verify the efficacy of the proposed method.

Reasonable analysis of radar interference effect is of great significance for adjusting jamming strategy in radar counter-measures (RCM). The modern battlefield is confronted with non-cooperative targets, so the conventional offline evaluation methods are difficult to apply. In this paper, a comprehensive evaluation method for radar interference effect based on the integrated cloud model is proposed. Firstly, a multi-layer index system for interference effect evaluation is established. Subsequently, the entropy method is employed to determine the weight of each indicator. To avoid the occurrence of hypertrophy as an imaginary number, the cloud parameters for each indicator are calculated using a modified inverse cloud generator. Eventually, a comprehensive assessment of the interference effect can be obtained by drawing the integrated cloud. The experimental results show that the proposed method is effective and can be applied to the evaluation of interference effectiveness in non-cooperative environments.

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.

Despite its widespread use in Earth remote sensing, synthetic aperture radar (SAR) image reconstruction remains challenging. The difficulties mainly lie in the handling of diverse scenes and motion errors with sparsely sampled data. Existing matched filtering (MF)-based methods cannot handle sparsely sampled data, while regularization-based methods lack adaptability to scene diversity. Although deep learning-based SAR methods can deal with these two issues, their performance will be degraded by motion errors. To address this, we propose a Transformer-based SAR image reconstruction method called RATIR-Net. The proposed method can obtain SAR images of various scenes under sparse sampling and motion errors by learning the correlations between echo data. In RATIR-Net, CNN-based encoding and decoding blocks are constructed to implement azimuth processes of range profiles (RP) in the range-Doppler domain according to the MF-based method. Meanwhile, a Residual Attention Transformer (RAT) block is designed to extract correlations between RPs, compensating for information loss caused by sparse sampling and suppressing non-correlated perturbations caused by motion errors. The CNN-based encoding and decoding blocks help reduce computing costs, and the RAT block mitigates the dependence on scene features and the influence of motion errors. These make RATIR-Net efficient and effective. Simulation experiments have been conducted to verify the proposed method.

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.

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.

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.

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.

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.

In the imaging processing of translational variant bistatic forward-looking SAR (TV-BFSAR), range cell migration correction (RCMC) is an essential procedure. Based on the azimuth variance property of the range cell migration (RCM), the keystone transformation is used for RCMC in TV-BFSAR. Before the RCMC, the ambiguity correction of the Doppler frequency must be done. However, the motion error makes the ambiguity correction hard to be realized. An iterative RCMC based on Keystone transformation scheme is proposed in this paper to overcome the challenge motion error. By searching for the correct Doppler ambiguity number based on the minimum entropy, this scheme corrects the RCM in TV-BFSAR. Simulations validate the effectiveness of this method.