Due to the strong consistency with the synthetic aperture radar (SAR) system, deceptive jamming can be well integrated with SAR images and has high concealment. Therefore, deceptive jamming suppression in SAR is an urgent problem that needs to be solved. This article proposes a slow-time group phase coding (GPC) scheme for deceptive jamming suppression. Specifically, the proposed method can be divided into three steps: first, by using the slow-time GPC, the SAR transmitted signals are encoded separately in pulses and divided into two groups. Second, based on each group of signals, we propose a new optimization problem to reconstruct the SAR images and eliminate the unmatched deceptive jamming, i.e., the deceptive jamming combined with the second kind of GPC is unmatched with the first kind of GPC. Third, due to the design of GPC, each group of signals generates a SAR image where the scene stays almost the same, while the residual matched deceptive jamming is located at different azimuths. In this context, this difference is successfully used to eliminate the remaining deceptive jamming. Finally, the RADARSAT-1 and MiniSAR datasets are used to evaluate the effectiveness of the proposed method.

Ground moving target detection is one of the foremost tasks for multichannel synthetic aperture radar (SAR) system. The traditional robust principal component analysis (RPCA) method is capable of separating low-rank and sparse components from mixed echo signals, and it has been widely applied in SAR ground moving target indication (GMTI). However, it suffers from sensitivity to channel mismatch, high computational complexity, and excessively high false alarm rates. To address these issues, a novel method that combines adaptive multichannel data reconstruction (DR) with improved pseudo-skeleton decomposition (IPSD) is proposed. First, the iterative weighted approach is presented to precisely reconstruct the multichannel data vector with the joint-pixel model. After that, IPSD is presented to achieve the moving target detection, in which the row and column index sets are selected using the generalized inner product (GIP) and the amplitude histogram distribution criterion. Compared to the existing algorithms, the proposed algorithm effectively addresses the challenge of improving local region coherence in multichannel image sequences. In addition, compared to previous RPCA methods, the proposed algorithm significantly reduces false alarm rates in strong clutter backgrounds while achieving higher efficiency. Simulation results and real SAR data experiments validate the effectiveness of the proposed algorithm.

Smart contracts are tailored software services that provide consistency and autonomy. The emergence of blockchain has powerfully facilitated the development of smart contracts but also brought dramatic challenges to their security and trustwor-thiness. Plenty of malicious traps are hidden in smart contracts, causing irreversible damage and obstructing the progress of this technology. Although researchers have gradually emphasized the identification of trap contracts, existing approaches suffer from a few concerns, viz., the shortage of an efficient detection model, the unbalanced categories of trap contracts, and the absence of a high-quality dataset with multi-trap contracts. In this paper, we propose an architecture called TrapFormer to intelligently detect trap contracts in the blockchain solely by leveraging the opcodes of smart contracts. We introduce a densely connected transformer that can segmentally extract opcode features and thus distinguish any potential traps. Furthermore, we implement an adaptive data augmentation method to alleviate the category imbalance of trap contracts. To demonstrate the feasibility of the proposed solution, we construct a multi-trap contract dataset from Ethereum. The experimental results reveal that the proposed solution can achieve superior performance for practical use.

As an active radar system, synthetic aperture radar (SAR) is often affected by different types of strong, complex, and variable electromagnetic interferences, which severely degrades the final imaging performance. Thus, how to effectively detect and suppress complex electromagnetic interferences is a crucial challenge currently. In this paper, we propose a self-supervised learning interference suppression method based on deep learning, including interference localization filtering and radar signal recovery. First, we construct a novel convolutional Autoencoder deep learning model —LocNet via the proposed optimization criterion, which is utilized to detect and locate the interference for subsequent filtration. Aiming at the issue of signal loss in the filtering process that is generally ignored in the current literature, we then reconstruct a novel U-Net neural network model—RecNet for the low-loss recovery of signal. Compared with the traditional parametric/non-parametric anti-interference methods, the most significant advantage of our method is that it overcomes the requirement for interference priori information, which is more consistent with the actual situation, and effectively solves the target information loss. Furthermore, since no interference information is involved in the training process (self-supervised training), our method applies to multiple types of interference rather than a specific one. Moreover, with our method, interference detection and suppression can be achieved simultaneously instead of separating the two steps as in existing literature. Measured and simulated SAR interference-contaminated data test results validate the effectiveness and robustness of the proposed method.

For the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, the increasing imaging width results in a serious range ambiguity problem, which affects the performance of ground moving target indication (GMTI). In this article, a novel nonuniform subpulse coding (NSPC) scheme is proposed. It is characterized by resorting to range-frequency band resources and detailed coding design for each subpulse, enabling the beam auto-scanning in elevation. Also, the bandpass filtering and digital beamforming (DBF) technology with improved data reconstruction are utilized to realize the separation of subpulses and suppress range ambiguity. The NSPC technique exchanges the signal bandwidth for increasing swath without range ambiguity, and the coded subpulses can be directed to the prescribed regions, while skipping the invalid areas where the echoes are blocked. After that, through the robust principal component analysis (RPCA) method, the moving target detection is performed for each separated region without residual range-ambiguous interference. The proposed approach has been theoretically deduced in detail and the simulation experiments demonstrate its effectiveness.

Downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) cross-track dimensional imaging always suffers from incomplete observation which does not satisfy the Nyquist sampling theorem and leads to the failure of conventional 3-D frequency-domain methods. Although several sparse reconstruction-based methods have been presented to solve this problem, the basis mismatch issue in sparse reconstruction theory will degrade the image reconstruction performance. To address this issue, this article proposes a novel 3-D imaging method for DLSLA 3-D SAR, which provides another idea for 3-D imaging through sparse recovery. It utilizes recovered full-sampled data to achieve cross-track dimensional imaging instead of using the under-sampled data directly as before. The Along-track-Height plane imaging is first finished by the range-Doppler (RD) algorithm and motion error compensation. Then, an advanced nuclear norm and low-rank matrix factorization (NU-LRMF)-based matrix completion (MC) algorithm and a vector reconstruction framework are built to achieve accurate recovery of full-sampled data. Finally, the cross-track dimensional imaging is completed with recovered full-sampled data by geometric correction and beamforming. Moreover, a fast two-stage iteration strategy for NU-LRMF (TS-NU-LRMF) is also presented to accelerate convergence. The robustness and effectiveness of the proposed 3-D imaging method are verified by several numerical simulations and comparative studies based on both the complex 3-D ship model and the simulated 3-D distributed scenario.

Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) usually uses a sparse and nonuniform linear array that often does not satisfy the Nyquist sampling theorem. Therefore, the cross-track dimensional imaging will fail with the traditional 3-D frequency-domain imaging algorithms. Several grid-based sparse reconstruction (GB-SR) algorithms have been presented to solve this issue. However, they assume that the scatterers are located on the discretized grids; otherwise, the off-grid effect or basis mismatch problem will occur. To address this issue, we propose a novel hyperparameter-free gridless-based sparse reconstruction (GL-SR) algorithm (i.e., 2-D adaptive iterative reweighted atomic norm minimization algorithm called 2-D IRAN) by a combination of the optimal covariance fitting criterion and atomic norm. It is a generalized model, while the other GL-SR algorithms (e.g., GLS, RGLS, and RAM) can be interpreted as the variants of 2-D IRAN. Moreover, since the interior-point method employed in toolboxes has high computational efficiency only for the small-scale matrix optimization problem, a fast implementation of 2-D IRAN via alternating direction method of multipliers (ADMM) is presented for the large-scale matrix optimization problem. Finally, we carry out extensive numerical simulations to demonstrate the advantages and effectiveness of 2-D IRAN for DLSLA 3-D SAR imaging based on the complex 3-D ship model and 3-D distributed scenario.

In high-maneuverability bistatic forward-looking synthetic aperture radar (HMBF-SAR) imaging, the acceleration leads to an increased residual range curve and a deepened two-dimensional spatial variance of Doppler parameters, which cannot be processed by the traditional algorithms. To address these problems, this paper establishes a more accurate digital representation for HMBF-SAR model and investigates an extended azimuth nonlinear Chirp Scaling (EANLCS) imaging method. In the flowchart of this paper, we first propose a more precise slant range model with improved expansion coefficients, and defines the range and azimuth direction of HMBF-SAR imaging. Then, a novel fast reference point (i.e., azimuth and range reference point) selection method is proposed to analyze two-dimensional spatial variance of signal characteristics, which is used to construct a high order model of residual range cell migration and Doppler parameters. Based on above analysis, we put forward an advanced imaging algorithm of combining the Second-order keystone and extended azimuth nonlinear chirp scaling (EANLCS) to compensate the increased residual range curve and two-dimensional spatial variance of Doppler parameters. Finally, the effectiveness of the proposed HBMF-SAR method is verified by several numerical simulations and comparative studies based on both the simulated and raw data.

High computational complexity and phase errors (PEs) are the main limitations of time-domain (TD) synthetic aperture radar (SAR) imaging algorithms. Accelerated fast backprojection (BP) (AFBP) algorithm avoids interpolation through wavenumber spectrum connection and is an efficient fast TD imaging algorithm. In order to deal with the image defocusing problem caused by PEs effectively and ensure rapid imaging, a TD autofocus method is proposed in this article, which is based on generalized sharpness metrics and the AFBP imaging model. The autofocus method is divided into two stages. First, for each subaperture (SA), the PE estimation model is established in unified polar coordinate (UPC), where the strong-scattering range-cell pixels are chosen to reduce memory burden and avoid repetitive imaging. The PE estimation is converted into a nonconvex optimization problem. Then, the genetic algorithm (GA) and the maximizing-maximum-pixel-value (MMPV) method are used to estimate the PEs. Second, SA images’ matching and constant PE’s compensation are performed to eliminate the residual PEs. The full-aperture well-focused image is obtained by the coherent accumulation of SA images. The effectiveness of the proposed method is proven by the results of simulation and real SAR data processing.

Spatial-variant phase errors (PEs) are important factors which defocus the synthetic aperture radar (SAR) image. In time-domain SAR imaging, the exact calculation of instantaneous range is carried out to realize imaging. Accurate trajectory is the key to compensate spatial-variant PEs and ensure image focus. Thus, an improved time-domain autofocus method based on three-dimensional motion errors (3-D MEs) estimation is proposed in this article. First, an improved maximizing-maximum-pixel-value method is used to estimate nonspatial-variant PEs. Meanwhile, a theoretical explanation combined with$N$-dimensional Euclidean space is described. Then, residual PEs and wrapped PEs are discussed successively. A part-overlapped partitioning scheme for sub-block images (SBIs) and a wrapped-PE model are proposed for 3-D MEs estimation. Then, the estimation problem is turned into a mixed integer programming problem, which can be solved by the combination of genetic algorithm (GA) and Tikhonov regularization. Finally, the well-focused image is obtained through updated trajectory. The effectiveness of the proposed method is proven by results of simulation and real SAR data processing.

Phase gradient autofocus (PGA) is widely used in estimating residue phase error due to its efficient and robust. However, its precision severely relies on sample quality. In this paper, an expediting phase gradient autofocus algorithm is proposed to solve the above problem. First, we extract valid echo data area from the received contaminated data. Second, the optimization of the azimuth window size is presented. It gets rid of the limitation that the traditional PGA depends on the experience value. Third, the phase error can be fast calculated without numerous IFFT and zero padding which decrease computational complexity. Furthermore, the computational cost is derived in detail. Finally, Numerical simulation and raw SAR data demonstrate that the new method can achieve better performance than conventional PGA.

In multi-channel synthetic aperture radar-ground moving target indication (SAR-GMTI), most radial velocity estimation methods are based on the phase difference between channels. However, the image coregistration and channel phase errors will have a severe impact on the phase difference between channels. Moreover, it deteriorates the performance of the moving target radial velocity estimation. To solve this problem, a robust radial velocity estimation method is proposed using a frequency diverse array-SAR (FDA-SAR) in this letter. By introducing the step frequency, the interferometric phase among channels is a linear function of the Doppler frequency. The radial velocity of moving targets is embedded in the first-order term of the linear function. Meanwhile, the first-order term does not include channel phase error terms. Therefore, the accurate velocity of moving targets is estimated by the first-order coefficient which is solved by the least-squares fitting method. Afterward, according to the analysis and derivation, the proposed method is robust on the condition of image coregistration error. At last, simulations and data analysis illustrate the effectiveness of the proposed method.

Clutter background suppression is a critical problem in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, a great quantity of secondary data is not easily acquired in heterogeneous environments. To solve the problem of clutter suppression, a method based on nonlocal self-similarity-robust principal component analysis (NSS-RPCA) is proposed for airborne SAR systems. First, discrete clutter is separated from the echo data by RPCA after range pulse compression. Second, similar blocks of the residual-clutter background are extracted to overcome the training sample limitation using the NSS method in the 2-D time domain. Third, subcovariance matrices are structured by the similar blocks, and the subcovariance matrix is stacked into a tensor. Then, the subclutter covariance matrix can be obtained from the stacked subcovariance matrix tensor by RPCA, where the residual-clutter tensor is of low rank and the target tensor is sparse. Finally, the residual clutter can be suppressed by the subclutter covariance matrix. In this manner, the source of independent identically distributed (IID) samples will be increased significantly without aperture loss by the proposed method. Simulation and analysis based on the experimental data illustrate the effectiveness of the proposed method.

In order to reconstruct the linear frequency modulated (LFM) signal, such as radar signal due to the complexity. A novel parameter estimation method based on a modified convolution kernel function (MCKF) is proposed for multi-component LFM signal in this paper. The method has fewer external cross-terms and light computational burden because of non-searching operations. Moreover, it is robust against additive noise. Finally, simulated and real data results confirm the proposed method.

Ground moving target indication (GMTI) is an important research field in multichannel-synthetic aperture radar systems (SAR). The robust principal component analysis (RPCA) method can separate the sparse matrix of moving targets from the low-rank matrix of static backgrounds in image or video data. As the correlation coefficient variation caused by phase different between channels is weak for SAR, it results in that the conditions of applying RPCA method cannot be satisfied. To solve this problem, an improved moving target detection method based on RPCA is proposed in this paper. By analysis, the phase different between channels is transformed to the shifting in time-frequency domain. Therefore, the correlation coefficient is decrease between channels, which means that the sparse matrix of moving targets is extracted with higher probability. Simulation and analysis illustrate the effectiveness of the proposed method eventually.