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Increasing the synthetic aperture radar (SAR) imaging frame rate is vital for obtaining continuous SAR images and dynamic scene monitoring, which multistatic SAR can achieve. Multiple source synchronization errors in multistatic SAR enable the image to produce positional offset and target defocus, which deteriorate the image quality. In order to obtain highly precise multistatic SAR images, synchronization errors must be compensated for. However, geometry errors are unavoidable during platform movement, which introduce Doppler frequency errors and range cell migration just like synchronization errors. Therefore, these two errors will jointly affect the imaging quality when coupled together, which will increase the difficulty of compensation. Aiming to resolve the issue of obtaining imaging results when the two errors exist simultaneously, this article proposes a decoupled estimation and compensation method for geometry and synchronization error. At first, the coupling relationship between geometry error and synchronization error on echo delay and Doppler frequency is analyzed. Next, the decoupled estimation problem with geometry and synchronization error is transformed into a constrained optimization problem. Then, the differential evolutionary algorithm is employed to address the optimization problem. Finally, the estimated values are utilized to compensate for the echoes to obtain high-resolution imaging results, which are verified by the simulation results. The experimental results convincingly demonstrate the improvement of the proposed method in imaging frame rate and error decoupling ability.

Raw data simulation is very important for system parameter design, performance evaluation, and route planning of bistatic synthetic aperture radar (SAR). However, most conventional simulation methods focus on improving the efficiency of echo simulation, without considering the impact of terrain fluctuations, resulting in insufficient fidelity of simulated echoes. In this article, a raw data simulation framework under undulating terrain for bistatic SAR with arbitrary configuration is proposed. In the framework, based on the prior information of digital elevation model of undulating terrain and the small facet theory, the time-variant scattering coefficient is computed first with the empirical formula related with the angle variation of transmitter and receiver for bistatic SAR. Then the shadow is judged from the perspectives of transmitter and receiver, respectively, and the formation of double shadow is discussed. At last, the above process was embedded into the time domain pulse by the pulse echo simulation algorithm, forming a framework for simulating bistatic SAR raw data under undulating terrain with arbitrary configuration, and the simulated and measured data experiments are illustrated to verify the effectiveness of the proposed framework.

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, the LRSD-ADMM-net is proposed to achieve simultaneous super-resolution imaging and target detection for forward-looking scanning radar. First, low-rank and sparse constraints as regularization norms are incorporated to establish objective function, and the alternating direction multiplier method is used to solve simultaneous super-resolution and target detection problems. Then, the solving process is expanded into a neural network, where the weight parameters of each level are obtained through adaptive learning. At last, experiments are conducted to verify the effectiveness of the proposed method.

Maritime surveillance is extensively concerned by worldwide authorities, in which ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental component. Though some development has been achieved in the SAR ship recognition task, two areas remain inadequately explored, which are the comprehensive utilization of multiscale features and the deployment of the prior knowledge of the ship shape. In this article, a novel ship augmented attention network (SA$^{2}$Net) for ship recognition is proposed, which comprehensively utilizes the multiscale features and integrates the ship shape prior to the end-to-end network. On one hand, due to the unequal effects of different scales, a scale attention module is proposed to adaptively select and assign weights to desired feature scales while disregarding irrelevant scales. Moreover, a feature weaving module (FWM) is constructed to merge semantic and detailed features produced by the high-to-low backbone, enriching representations across all scales of ship targets. On the other hand, in order to incorporate the priory knowledge of the ship shape into the network, we develop a feature augmentation module (FAM) to further boost the ship recognition accuracy. This module can provide rectangular receptive fields that align with the shape of ships, wherein a limitation encountered with traditional square convolutions. Comprehensive experiments on representative three- and six-category OpenSARShip tasks and seven-category FUSAR-Ship tasks show that our SA$^{2}$Net demonstrates superior performance when compared to the current state-of-the-art methods.

In terms of the global coverage provided by global navigation satellite system (GNSS) signals, radar images of a large field-of-view can be obtained via passive synthetic aperture radar (SAR) using these signals of opportunity. Usually, large field-of-view imaging is challenging using conventional SAR methods owing to the space-variant resolution. In addition, only a moderate range resolution can be provided by the GNSS transmitters in the passive SAR system. In this article, an optimal imaging method for a large field-of-view using GNSS-based passive SAR is proposed, which exploits the inherent multistatic nature of GNSS. At first, the spatial resolution of each scatterer in the observation area relative to each GNSS transmitter is calculated according to the geometry. Then, the selection of the transmitter set is modeled as an optimization problem, where the minimal resolution cell is used as the optimal criterion. After that, an evolutionary algorithm is applied to solve the optimization problem. Finally, the space-time combination of the echoes over a long dwell time concerning selected GNSS transmitters is performed to form the radar image with a minimal spatial resolution. The proposed method is extensively validated using two experimental datasets, where the passive radar consists of BeiDou satellites and a fixed receiver. The spatial resolution improvement and the large field-of-view imaging capability of the proposed method are forcefully demonstrated according to the experimental results.

Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day. To reduce the sampling amount of video SAR, image processing can be formulated into a low-tubal-rank tensor recovery problem. In this article, we proposed an orthogonal rank-1 tensor pursuit (ORTP) algorithm to solve the low-tubal-rank tensor recovery problem in video SAR imaging. The proposed ORTP algorithm is an extension of the orthogonal rank-1 matrix pursuit algorithm in the matrix sensing problem from the matrix case to the tensor case under a tubal-rank model. It is capable of reconstructing the target tensor efficiently without requiring any prior information about the prespecified or pre-estimated tensor tubal-rank value. To achieve this, rank-1 basis tensors and weight tensors of the target tensor are estimated iteratively, and the residual error between the observed tensor and the estimated tensor through linear mapping is utilized as the stop condition. We theoretically prove the convergence and correctness of the proposed ORTP method. The methodology was tested on synthetic data, real video data, and video SAR data. These tests show that the proposed approach outperforms other video SAR imaging algorithms and low-rank tensor recovery algorithms.

Traditional computation is driven by parallel accelerators or distributed computation nodes in order to improve computing performance, save energy, and decrease delays in accessing memory. Recently, artificial intelligent algorithms, frameworks, and computing models are growing to help with high computational performance.

Bistatic SAR (BiSAR) imaging is becoming more and more mature, nevertheless restricted by the significant problem of 2-D space-variation of echo signal. In practical applications, the geometry configurations and platform motion of BiSAR are often complicated, making the problem of space-variation severe and causing it hard to efficiently process BiSAR data of large imaging scene. Focusing on this problem, this article proposes a new imaging method for large scene-size imaging that can adapt to general spotlight BiSAR with complicated platform motion. This method first roughly focuses the echo data in range-Doppler domain through azimuth-dechirp and removes the residual range-azimuth coupling via Keystone transform. Then making use of the tolerance of SAR imaging to slight phase error, the full Doppler axis is down-sampled with a certain ratio to form a much smaller amount of Doppler cells called Doppler bands. Through restricting the value of the down-sampling ratio, the variance of differential phase in each Doppler band after the dechirp is controlled within a given tolerance. As a result, the differential phase can be compensated for in variable Doppler bands for a large imaging scene with high efficiency. The computational complexity of the proposed method is indicated as low as logarithmic and its effectiveness is verified through numerical simulations.

Real-time passenger flow prediction plays an important role in subway network design and management. Most of the existing prediction algorithms only consider the sequence of passenger flow volume, however, ignore the influence of other outer factors, for example, the weather conditions, air quality and temperature. In this paper, a systematic framework, MetroEye, is proposed for weather-aware prediction of real-time passenger flow. The framework contains an offline system and an online system. The offline system adopts a conditional random field (CRF) model to establish the relationship between passenger flow volume and weather factors. Experimental results show the superior prediction accuracy of the model, especially in large stations. The online system provides efficient methods to simulate the real-time passenger flow volume. Due to its high practicality, MetroEye has been adopted by Beijing Urban Rail Transit Control Center to monitor the passenger flow status of the Beijing subway system.

Bistatic synthetic aperture radar (BiSAR) imaging is faced with two major challenges: large scene imaging and adaptability to unideal platform motion in practice. In order to deal with these two problems, a generalized wavefront-curvature-corrected polar format algorithm (PFA) is proposed in this article. The traditional PFA is little restricted on geometry configuration and platform motion, but its application to large scene imaging is limited by the far-field planar wavefront assumption. To solve this limitation, this article derives the phase error caused by wavefront curvature and analyzes its influence on both geometric distortion and defocusing effect in detail. Based on the analysis, we present a wavefront curvature completely correcting method through space-variant phase compensation using the analytical wavefront curvature phase in wavenumber-domain, which is derived through method of series reversion. What's more, an efficient realization of the space-variant phase compensation based on two-stage image division is given to avoid high overlap rate in the traditional image division method. The proposed method can obtain well focused and geometric undistorted image for BiSAR under complicated flight paths, and it also keeps the logarithmic complexity of traditional PFA. The effectiveness of the proposed method is verified by numerical simulations.

In recent years, the research of multistatic synthetic aperture radar (SAR) has become a hot spot because of its great potentials in military and civil applications. However, it is inevitably disturbed by electronic countermeasures. In the multistatic SAR image, the false target caused by transmitting deceptive jamming leads to misjudgment of the image information. In this article, a new antideceptive jamming method for multistatic SAR is proposed. This method can locate the deceptive jammer and effectively suppress it. First of all, the echo model of multistatic SAR in the jamming environment is set up. Based on this model, the relationship expression for the location of jammer is derived. Then, we use the maximally stable extremal region method and the Euclidean-distance-based method to detect and identify false target in the multistatic SAR image. By using the location information of false target, receivers, and transmitters, the expression of the jammer localization is solved to locate the jammer. Finally, the minimum variance distortionless response beamforming method and image mosaic method are used to effectively suppress the jamming. The simulation results show that the method is effective.

In real application scenario of bistatic forward-looking synthetic aperture radar (BFSAR), ground moving target (GMT) is generally smeared severely in SAR image, due to its unknown range cell migration (RCM) and Doppler frequency migration (DFM). When GMT moves along an unknown curve trajectory, its high-order RCM and DFM (including the second- and third-order terms) would further aggravate the difficulty of GMT refocusing. To address this issue, an efficient GMT refocusing method via keystone-based delay-correlation transform and fast searching Fourier transform (KDCT-FSFT) is proposed. First, the KDCT is proposed to correct the first- to third-order RCMs regardless of target's motion state and position information. Meanwhile, the order of GMT's phase response is reduced as well. Then, FSFT is applied to estimate the third-order Doppler parameter of GMT. In the following, a 2-D fast Fourier transform (2D-FFT) can be applied to integrate the target signal coherently in Doppler parameters domain, where the Doppler centroid and Doppler frequency rate of GMT can be estimated. Finally, with the aforesaid estimated Doppler parameters, RCM and DFM can be well corrected and target with unknown curve motion can be finely refocused. Compared with the existing methods, not only the refocusing accuracy of the proposed method is higher, but also its processing is more efficient, since the procedures in the proposed method are performed with respect to all the range cells in the corresponding aperture, i.e., GMT refocusing is achieved by the 2-D data received in one aperture, rather than along every single range cell. Both the simulation and experimental results are given to validate the effectiveness of the proposed method.

Recently, compressive sensing (CS) has been applied in synthetic aperture radar (SAR) imaging, which is increasingly in the focus of study and shows great potential. In CS-based SAR imaging, motion errors of the moving platform introduce inaccuracies in the observation model, which cause various degradations in the final images. To accomplish accurate motion compensation during CS-based SAR imaging, we propose a fast CS-based SAR imaging integrated with motion compensation method. First, CS-based imaging based on the utilization of inverse observation deduced from the inversion of conventional imaging procedures is applied, which is much more computational efficient than the exact observation model. Then, an improved inverse observation model integrated with motion compensation is derived. In the improved model, spatially variant azimuth phase errors are taken into consideration. Joint SAR imaging and motion compensation are formulated as a sparse recovery problem and solved in an iterative way, wherein each iteration both image formation and motion compensation are carried out. The processing of SAR data shows that the proposed method can obtain better focused images compared with the existing SAR imaging and motion compensation methods.

Despite that the accuracy and efficiency of stereo matching technology have significantly improved in the past decades, the issue of edge-blurring remains a challenge to most of the existing approaches. In this paper, we propose a minimum spanning tree (MST) based stereo matching method by using the image edge and segmentation optimization to preserve the image boundary. We first exploit a fast disparity range estimation method by combining the Surf and Akaze feature points to improve the computational efficiency. Second, we utilize the image edges and brightness information to generate a self-adaptive weight function, which is able to significantly improve the accuracy of MST aggregating in the regions of complex texture and boundaries with similar color distribution. Third, we employ the image segmentation to extract the invalid regions of the estimated disparity map, and propose a post-processing scheme to refine the disparity result. Finally, we run our method on several Middlebury and KITTI datasets. The comparison results between our method and other state-of-the-art approaches demonstrate that the proposed method has high accuracy for disparity computation and is especially robust to the edge-blurring.

The constant false alarm rate with convolution and pooling (CP-CFAR) method, which can improve the detection efficiency via GPU parallel acceleration in the airborne synthetic aperture radar (SAR) images, is proposed in this paper. The constant false alarm rate (CFAR) method is one of the most widely used methods for target detection in airborne SAR images. However, since the CFAR is performed on a pixel-by-pixel basis, the time consumption will increase rapidly with the expansion of image scene. Even if the GPU is used for acceleration, the efficiency improvement is still limited, which cannot meet the real-time processing requirements. Therefore, the CP-CFAR method for the target detection of SAR images is proposed in this paper. The convolution layer uses the horizontal and vertical Sobel operators to improve the contrast between targets and background, and the pooling layer can reduce the processing dimension of the images. The convolution and pooling layers are added before the two-parameter CFAR, which can reduce the computational elements but without losing the main feature of the original image. More importantly, compared to the traditional CFAR, the proposed CP-CFAR is more suitable for GPU acceleration, which can improve the detection efficiency significantly. Experiments on the moving and stationary target acquisition and recognition SAR images with a size of 1478 × 1784 show that, compared with the traditional cell-averaging CFAR, two-parameter CFAR and their CPU, multithread CPU, and GPU acceleration modes, the proposed CP-CFAR with GPU acceleration can obtain the best detection performance with the highest acceleration ratio, and the operation time is less than 192 ms.