With multiple channels receiving echoes in azimuth, multichannel SAR has the potential of forward-looking imaging. However, its angular resolution, especially the area along the platform flight path, is poor due to the small variation of viewing angle. In this paper, the scheme of angular resolution enhancement for multichannel forward-looking SAR imaging based on zero-shot learning is proposed. In the scheme, the preliminary imaging results are obtained firstly with the synthetic aperture processing, and then the results are divided into three equal parts in azimuth, namely left, middle and right parts, to construct the dataset. Then by incorporating the frequency domain loss and mixed attention module, the modified CycleGAN network is developed to learning the mapping between the middle part (the area with lower resolution) and the left/right parts (the area with higher resolution). At last, experimental results are illustrated to verify that the proposed scheme can enhance the angular resolution of the middle area to the level of left/right area for the forward-looking images even without ground truths.

Due to the high degree of freedom of bistatic synthetic aperture radar (SAR), how to design a suitable configuration to achieve forward-looking high-quality imaging is a crucial issue. Since the spatial resolution is the most critical performance metric, the configuration design of bistatic forward-looking SAR (BFSAR) driven by spatial resolution metrics is discussed in this letter. First, based on the general geometry configuration and generalized ambiguity function (GAF), the analytical expression of the ellipse resolution for BFSAR is derived. Then, a novel measure for spatial resolution is developed by considering both the orthogonality and balance. Finally, with the prior information of the transmitting platform, the optimal configuration of the forward-looking receiver is established by solving an optimization problem of the spatial resolution with genetic algorithm, and simulation results are illustrated to validate the effectiveness of the proposed method.

Multichannel synthetic aperture radar (SAR) has the potential of resolving left/right ambiguity and then achieving high-resolution forward-looking imaging. However, when the sparse channel configuration is adopted, there are always residual grating lobes and left/right ambiguity in the imaging results. In this paper, a scheme of simultaneously suppressing residual grating lobes and left/right ambiguity for sparse channel forward-looking SAR imaging is proposed. Firstly, the formation of the residual grating lobes and left/right ambiguity are analyzed based on the signal model and imaging procedure of multichannel forward-looking SAR. Then, the characteristics of the differences in the position of grating lobes for the imaging results of different snapshots, and the spatiotemporal coupling characteristics of suppression of grating lobes and left/right ambiguity are explained. Next, a space-time steering matrix based on range history information is constructed, which establishes a linear mapping relationship between the space-time echo and the original scene. By solving the linear equation with a regularized iterative adaptive approach, the residual grating lobes and left/right ambiguity are suppressed simultaneously. The extensive simulation and experimental results demonstrate the effectiveness of the proposed method.

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.

Conventional array radar systems require element spacing to be less than half a wavelength to mitigate grating lobes in forward-looking image, which results in a large number of elements and imposes a significant hardware burden. In this paper, an equivalent virtual uniform linear array is formulated with differential visualization technique and multiple snap-shot data. Then the regularized iterative adaptive approach (RIAA) is adopted to achieve superresolution imaging of the virtual linear array. Simulation results are given to illustrate the effectiveness of the proposed scheme.

To achieve high-quality forward-looking imaging of multi-channel radar, the fusion of synthetic aperture imaging result and real aperture superresolution result is usually necessary. However, the fusion processing by directly multiplying two results will seriously affect the image quality. In this paper, based on the analysis of the characteristics of synthetic aperture imaging results and super-resolution results, the logarithm transformation is introduced firstly on the synthetic aperture result before multiplication fusion processing to improve the image quality. Simulation results are illustrated to verify the effectiveness of the proposed scheme.

Multichannel radar has the potential of forward-looking imaging, but its azimuth resolution is usually poor due to the restriction of the platform size. Many superresolution methods have been developed to improve its azimuth resolution and sparse Bayesian learning (SBL)-based methods are popular within them. However, traditional SBL methods suffer from the over-sparse problem for extended targets, and they always fails to achieve good performance when there are both point targets and extended targets. In this paper, by judging the types of targets to assign different weights for different targets, and then applying the weighted average to the update results of hyperparameters in SBL iterations, a modified SBL-based scheme of multichannel radar forward looking imaging is proposed, and simulation results are illustrated to verify its effectiveness.

Multichannel radar can resolve left/right ambiguity and has the potential of high-resolution forward-looking imaging. However, when only a limited number of channels are available, in order to have good performance of resolving left/right ambiguity, there is always grating lobe in the imaging results. In this paper, a scheme of grating lobe suppression based on self-supervised contrastive learning is proposed. Firstly, a generative adversarial network is employed to achieve the mapping from the source domain X (i.e. the imaging results with fewer channels) to the target domain Y (i.e. the imaging results with more channels). Then, a contrastive loss is introduced to ensure across-domain preservation of the content information like the distribution of ground objects, preventing the generator from making unnecessary changes in results when suppressing the grating lobe. At last, simulation results are given to illustrate the effectiveness of the proposed method.

Subaperture processing is a commonly used method for time-domain SAR echo simulation. However, due to the effect of geometry configuration for bistatic SAR, doppler aliasing will appear in the simulated echo data, and then there will be ambiguity in the imaging result. In this paper, by analyzing the causes of ambiguity and introducing the interpolation in range, a modified subaperture echo simulation method for bistatic SAR is proposed. At last, simulation results are illustrated to verify the effectiveness of the method.

Radar forward-looking imaging is important in many fields, such as the ground mapping, autonomous driving. However, conventional monostatic SAR cannot realize forward-looking imaging due to left/right ambiguity. In this paper, based on the geometry model of multichannel forward-looking SAR, the principle of resolving left/right ambiguity is illustrated firstly, and then the performance is analyzed. At last, simulation results are illustrated to verify the effectiveness of theoretical analysis.

With the echo difference between different channels, the left/right ambiguity in single-channel forward-looking synthetic aperture radar (SAR) can be resolved using dual-channel radar. However, due to the restricted channel resources, existing methods have limited performance in resolving left/right ambiguity, especially in the area along the flight path. In this article, a novel left/right ambiguity resolving scheme is proposed to improve the imaging performance of dual-channel forward-looking SAR. In the scheme, the steering matrix considering the time-frequency–space information of echo was designed first, and the linear mapping equation between the echo and the original scene was established. Then, the regularized iterative adaptive approach (RIAA) is introduced to solve the equation, thereby achieving dual-channel forward-looking SAR left/right ambiguity resolving and superresolution imaging simultaneously and directly in the echo domain. At last, the simulated and experimental results were illustrated to demonstrate the effectiveness of the proposed scheme.

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.

Sidelobe suppression is an important step in SAR imaging, especially for forward looking areas with poor azimuth resolution. In this paper, the signal model of multichannel forward-looking SAR imaging is realized with BP algorithm firstly, and then spatial smoothing coherence factor(SSCF) is introduced to suppress sidelobes of the imaging results. At last, simulation results are given to illustrate the effectiveness of the proposed scheme.

With multiple channels receiving echoes in azimuth, multichannel radar has the potential of forward looking high resolution imaging. However, due to the limitation of platform size, its azimuth resolution is poor. In this paper, by considering the effect of platform motion and off-grid problem, a multichannel radar forward looking superresolution imaging method based on atomic norm minimization is proposed. Simulation results are illustrated to verify the effectiveness of the method.

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. With multiple channels in azimuth, multichannel radar can resolve the left/right ambiguity, and have the potential for forward looking imaging. However, multichannel radar have flexible channel configuration, and there are different processing schemes, which may have different imaging performance and challenges. In this papar, based on the signal model of SIMO radar, different processing schemes for multichannel radar forward-looking imaging are illustrated and simulated, as well as the corresponding potential and challenges.

Multichannel radar has the potential of forward-looking imaging, but its azimuth resolution is usually poor due to the restriction of the platform size. Many superresolution methods have been developed to improve its azimuth resolution. However, these methods always have the problem of grid mismatch. In this paper, sparse Bayesian learning (SBL)-based multichannel radar forward-looking super-resolution imaging considering off-grid error is proposed, and simulation results are illustrated to verify its effectiveness.

Multichannel SAR can solve the problem of left/right ambiguity and has the potential of high-resolution forward-looking imaging. However, its azimuth superresolution in the adjacent area of flight path is still poor due to the small angle variation. In this paper, we propose a multichannel forward-looking SAR azimuth superresolution method based on CycleGAN. First, the multichannel forward-looking SAR imaging results are obtained, and then the mapping between forward-looking imaging results and the ground truth is learned with CycleGAN, to achieve azimuth superresolution. At last, simulation results are given to illustrate the effectiveness of the proposed method.

Imaging and target detection are usually regarded as two independent parts in conventional processing, which means that the detection performance will be affected by the imaging result. In this paper, the LRSD-ADMM-net is proposed to achieve simultaneous super-resolution imaging and target detection for forward-looking scanning radar. In addition, simulation results were provided to verify the effectiveness of the proposed algorithm.

Multichannel radar has the potential of forward-looking imaging with multiple channels receiving echoes on a single platform, but its azimuth resolution is usually poor. Superresolution algorithms have been developed to solve the problem, however, most of the methods have problems of difficulty in parameter adjustment and large amount of computation. In this paper, driven by the powerful learning ability of deep networks, the conventional ISTA reconstruction process is mapped into a deep network, and then the deep unfolding ISTA-Net is formed and used to realize multichannel radar forward-looking superresolution imaging. The simulation reults verify that the proposed method can provide high-quality reconstruction results while substantially reducing the imaging time.

To obtain high-resolution images of the objects in front of platform, a super-resolution scheme for multichannel radar forward-looking imaging considering failure channels and motion error is proposed in this study. In the scheme, a failure channel detection method based on the correlation of pulse-compressed data of different channels is presented first, and then a revised steering matrix considering failure channels and motion error is constructed. Finally, the echo data are processed by the iterative adaptive approach (IAA) with the revised steering matrix. Simulation results are given to illustrate the effectiveness of the proposed scheme when dealing with failure channels and motion error.

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. Although the ambiguity can be resolved by using multiple channels in azimuth, its azimuth resolution in the vicinity of flight path is usually poor due to the small angle variation. To solve the above problems, a hybrid real/synthetic aperture scheme for multichannel radar forward-looking imaging is proposed in this paper. In the scheme, the synthetic aperture imaging result with left/right ambiguity is obtained firstly using the information of platform motion. Then the real aperture superresolution imaging with regularized iterative adaptive approach(RIAA) is achieved using the instantaneous data of multiple channels. At last, the two imaging results are fused to obtain the forward-looking superresolution image without left/right ambiguity. Simulation results are given to illustrate the effectiveness of the proposed scheme.

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.

Receiving signals sequentially through multiple channels in azimuth, radar has the potential of forward looking high-resolution imaging. However, due to the limitation of platform size, its azimuth resolution is poor. In this paper, by considering the effect of platform motion and geometric distortion, a modified iterative adaptive algorithm(IAA) method is proposed to realize multichannel radar forward-looking superresolution imaging. Simulation results are illustrated to verify the effectiveness of the method.

In order to improve the angular resolution of scanning radar, a lot of super-resolution algorithms have been developed in recent years. However, the super-resolution performance is affected by many factors due to the ill-conditioned nature of inverse problem. In this paper, from the perspective of numerical simulation, we illustrated the influence of the antenna pattern sidelobes on the condition number of the convolution matrix, and then analyzed the influence on the performance of the super-resolution algorithm.

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 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.

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.

In this paper, an improved target extraction scheme is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained firstly by deconvolution technique, and then it is used to map a patch-image. Secondly, low rank and sparse matrix decomposition is conducted on the patch image. Thirdly, constant false-alarm rate (CFAR) detection method is used to extract the targets from the sparse matrix. Finally, simulation results are given to verify the effectiveness of the proposed scheme.

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.

Sparse regularization method has been widely used to realize super-resolution imaging in radar forward-looking imaging. However, most of existed methods directly minimize a nondifferentiable L1 regularization problem. In this paper, a Majorize-Minimization (MM) based super-resolution method is proposed to realize super-resolution for radar forward-looking imaging. According to MM principle, the proposed method converts the non-differentiable L1 regularization problem into a differentiable L2 regularization problem, and the real target distribution is obtained by solving the L2 regularization problem. Due to the introduction of the sparse prior, the proposed method can better improve the azimuth resolution of radar forward-looking imaging. In addition, the application of MM principle makes the non-differentiable L1 regularization easier to be solved. Finally, the superior performance of the proposed method is verified by simulation.

Total variation (TV) method has been utilized to realize super-resolution and preserve contour information of target in radar forward-looking imaging. However, its real-time ability is restricted to matrix inversion. In this paper, a fast TV (FTV) superresolution method is proposed to improve the real-time superresolution ability of traditional TV method. The proposed FTV method utilizes the low displacement rank features of Toplitz matrix and realizes fast matrix inversion by Gohberg-Semencul (GS) representation. It not only effectively improves the azimuth resolution and preserve the contour information of target, but also reduced the computational complexity of traditional TV method to improve its real-time superresolution ability. The superior performance of the proposed FTV method is verified by simulation and measured data processing.

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, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, 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, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm.

Real-aperture radar can be utilized to realize forward-looking imaging by antenna scanning the imaging region. However, low azimuth resolution seriously affects its practical application. Although traditional super-resolution methods could enhance azimuth resolution to a certain extent, effective preservation of contour information for important targets still remains to be a problem. In this article, a method of total variation-sparse (TV-sparse) multiconstraint deconvolution is proposed to improve azimuth resolution of forward-looking imaging as well as preserve contour information of important targets. Since our interested targets usually appear to be sparse, the sparse constraint of the target is introduced first to achieve high resolution of forward-looking images, which may cause the loss of target contour information in the meantime. Second, total variation (TV) constraint is introduced based on the sparse constraint, converting traditional single-constraint super-resolution problem to a multiconstraint problem. We then use the split Bregman algorithm (SBA) to solve the multiconstraint problem, whose solution is the super-resolution image of radar forward-looking region. Compared with traditional super-resolution methods, the proposed method can improve the azimuth resolution of radar forward-looking imaging as well as better restore target contour information by adjusting respective weights of sparse constraint and TV constraint. Finally, the performance of the proposed method is validated with the simulation and measured data.

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.

In this paper, an accelerated maximum a posteriori (AMAP) algorithm is proposed to realize fast and effective super resolution imaging of real beam scanning radar. The main idea of this algorithm is to construct a prediction vector based on the first and the second order of difference information before iteration. By using Taylor expansion series and second-order vector extrapolation technique, it aims to enhance the convergence speed of maximum a posteriori algorithm. Finally, the proposed algorithm is verified by simulations.

Target detection is an important function of forward-looking scanning radar search and tracking applications. However, it is difficult to detect the targets using the real beam image with low azimuth resolution. In this paper, a target detection scheme is proposed for forward-looking scanning radar. First, an image with better resolution is obtained by deconvolution technique, and it is used to map a patch-image. Then, according to the low rank characteristic of the patch-image and the sparse characteristic of the targets, the target detection is converted into an optimization problem of low rank and sparse matrix decomposition. Finally, the targets are obtained by solving this optimization problem. Simulations are given to verify its effectiveness.

The clear contour is required when realize azimuth superresolution of forward-looking radar imaging in many applications. Traditional deconvolution methods achieve the azimuth superresolution but are limited in contour recovery. Although the total variation (TV) method can be used to keep the contour information, it's sensitive to noise because of derivation. In this paper, we propose an improved total variation (ITV) method to realize azimuth superresolution of forward-looking radar imaging and recover the contour information. Firstly, the TV norm and L2norm are combined as the penalties under regularization framework. Then the regularization problem is solved by split Bregman algorithm. The proposed ITV method achieves higher azimuth resolution and better contour recovery performance than traditional methods, and the super performance is verified by simulations lastly.

Regularization technology can be utilized to improve the azimuth resolution for forward-looking scanning radar. In this paper, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging. This method can achieve azimuth superresolution and noise suppression. Simulations are given to verify the effectiveness of the method.

This paper proposes a space variant-based maximum a posteriori (MAP) angular super-resolution algorithm for highspeed moving real-beam scanning radar. Firstly, the aberration model of the antenna modulation function is established through analyzing the relationship between the scanning angle and the sight angle. Afterwards, an efficient piecewise constant model is formulated for the sake of reducing the computational complexity and restoring cost. Finally, the space variant-based MAP algorithm is derived based on the aberrant model. Simulation experiments demonstrate that the proposed method can improve the super-resolution performance of the high-speed platforms more efficiently than the traditional MAP method.

For high-quality synthetic aperture radar (SAR) processing, Doppler centroid estimation is an essential procedure that can help to solve the SAR platforms motion parameters conversely, construct azimuth matching function and is important for target location as well. In this paper, a Doppler centroid estimator based on phase center tracking is proposed for SAR. An evaluation function is designed to measure the estimated Dopper centroid accuracy. In order to improve the calculation accuracy and computational efficiency, the evaluation function is solved by binary stage optimization to estimate the Doppler centroid. Simulations validate the effectiveness of this method.

Deconvolution techniques provide efficient implementations for super-resolution imaging for forward-looking scanning radar. However, deconvolution is normally an ill-posed problem, and the solution is extremely sensitive to noise. From a statistical perspective, maximum likelihood (ML) methods are able to condition the ill-posed problem into a well-posed one. Nevertheless, traditional ML methods only consider the amplitude of the echo and by ignoring the phase that do not adequately model the radar imaging system. In this letter, an I/Q-channel modeling ML method is proposed for forward-looking scanning radar. First, the probability model of the echo is deduced by jointly considering noise in the I and Q channels. Then, a probability density function of the received data is deduced and used to formulate the likelihood function. Finally, the targets can be precisely estimated by maximizing this likelihood function. The results of simulations and experiments are provided to illustrate the effectiveness of the proposed method.

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.

High-resolution scanning radar mapping of the surface is an effective tool for addressing concerns in local environmental and social investigation fields. Regrettably, the azimuth resolution of a scanning radar is constrained by the antenna beamwidth. Multiple super-resolution approaches have been applied to the scanning radar to enhance the azimuth resolution, but they suffer from limited resolution improvement. In this paper, a methodology to derive surface estimates from the scanning radar at an improved azimuth resolution is proposed. We first consider the truncated spectrum by discarding the unreliable frequencies to suppress the noise amplification. Then, based on the iterative adaptive approach (IAA), a novel inverse filtering method is formulated to obtain lower sidelobes and a higher resolution. Finally, by taking advantage of the Fourier property of the steering matrix and the Toeplitz structure of the covariance matrix, we exploit the Gohberg-Semencul representation and the data-dependent trigonometric polynomials to derive a fast IAA (FIAA)-based inverse filtering to mitigate the computational burden. Simulation results and real data processing demonstrate that the proposed FIAA-based inverse filtering outperforms the existing super-resolution approaches in resolution improvement and results in a higher computational efficiency.

Regularization technology can be utilized to improve the azimuth resolution for forward-looking scanning radar (FLSR). Among various regularization methods, L1 norm constrained method is usually adopted for its strong ability in resolving the sparse targets. Nevertheless, the solution of L1-norm constrained regularization method (L1-CRM) is sensitive to noise and the iterations would quickly diverge from the desired result if too many iterations are performed. In this paper, a regularization imaging method via joint L1-L2 norm constraint is proposed. By combing the L1 norm constraint and L2 norm constraint together, a new objective function is obtained and then the popular fast iterative threshold/shrinkage (FIST) method is adopted to minimize this nonsmooth convex function. This new method can not only have strong ability in resolving the sparse targets, but also be more robust to the noise. Simulations are carried out to demonstrate the effectiveness of the proposed method in terms of resolving ability and noise robustness.

In bistatic forward-looking synthetic aperture radar (BFSAR), conventional autofocus algorithms cannot estimate the phase errors accurately when the range walk is compensated in the azimuthal time domain. This problem stems from the influence of the azimuth-variant Doppler coefficients after linear range cell migration correction in azimuthal time domain. In principle, nonlinear chirp scaling processing can be carried out to remove the azimuth variance. Nevertheless, Doppler azimuth variations of the Doppler parameters induced by the motion errors are not taken into consideration and serious defocus would emerge in the final image. To cope with such a problem, an estimation-evaluation-equalization scheme is proposed before conventional autofocus for BFSAR. Different from the conventional autofocus method, the azimuth-variant Doppler coefficients are additionally estimated and equalized before autofocus, and as a consequence, phase errors can be precisely estimated. The BFSAR data processing results demonstrate the validity of the proposed method on the improvement of autofocus in BFSAR.

Deconvolution techniques can be utilized to realize angular resolution enhancement for real-beam scanning radar (RBSR). However, most deconvolution algorithms are sensitive to noise and time consuming. In this paper, an accelerated iterative shrinkage/thresholding (AIST) algorithm is proposed to overcome these disadvantages. AIST is developed from iterative shrinkage/thresholding (IST) algorithm which can reduce the noise sensitivity through shrinkage/thresholding operation. And a vector extrapolation method is adopted to accelerate the IST algorithm. This acceleration method makes a prediction before IST iteration step and thus can converge faster than IST. Simulations are carried out to demonstrate the AIST algorithm can effectively accelerate IST algorithm without performance loss.

This paper proposes a novel registration method for optical and SAR images which is based on straight line features and mutual information. Firstly, different edge detectors are employed to detect the line segments in both optical and SAR images respectively. Then, through the Hough transform and a straight line fitting and filtration method, the main straight lines of each image are extracted and their intersections are obtained and taken as the candidate matching points. With the RANSAC (RANdom SAmpling Consensus) method, corresponding point pairs (CPPs) are found with these candidate points and a coarse registration between the heterogeneous images is implemented. At last, by using the mutual information of the separated patches generated from the coarse registered images, a fine registration result is finally achieved. The experiment with a pair of X-band air-borne SAR and optical images validates the efficiency and precision of the proposed method.

In this letter, an improved regularized iterative adaptive approach (IAA) is proposed for scanning radar angular superresolution. Because the IAA requires matrix inversion, the increasing condition number of the covariance matrix leads to the ill-posed problem of the IAA. Based on this reality, the diagonal loading method is introduced to solve the ill-posed problem. Because the loading value controls the tradeoff between the azimuth resolution and noise amplification, we use the radiometer uncertainty principle to determine the optimum loading value. When compared with the existing angular superresolution approaches, the proposed regularized IAA is shown to provide significant resolution improvement. Numerical results illustrate the superior performance of the proposed regularized IAA.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Owing 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, first, the spatial-variance properties of motion errors are analyzed analytically and quantitatively. Different from the side-looking monostatic and bistatic SAR, 2-D space-variant motion errors should be taken into consideration in BFSAR. The 2-D spatial variance of the motion errors can be categorized into two parts, range-variant motion errors of the transmitter and azimuth-variant motion errors of the receiver. Moreover, these two parts are independent of each other. Based on this property analysis, second, a motion compensation (MoCo) approach with cubic-order processing is proposed to deal with the spatially variant motion errors in BFSAR. In the cubic-order processing, the first-order MoCo is performed to correct the spatially independent motion errors on the raw data. The second-order MoCo is accomplished on the non-range-cell-migration (RCM) data to deal with the range-variant errors. After the second-order MoCo, since the signal direction of the non-RCM data coincides with the variant direction of the uncompensated phase errors, the azimuth-variant motion errors and slow time signal are coupled together. To cope with such a problem, the slow time signal is transformed into the direction perpendicular to the azimuth by a novel procedure named azimuth-slow time decoupling. At this stage, the coupling between the azimuth-variant motion errors and slow time signal has been eliminated. Azimuth-variant motion errors can be corrected precisely. Simulation and experimental results verify the effectiveness of the proposed method.

To obtain high resolution image for real beam scanning radar, angular superresolution is studied, and a two-step scheme is proposed in this paper. In the scheme, using the threshold determined by CFAR-OSTU, the real beam data is divided into two parts firstly, and then maximum likelihood deconvolution with different number of iterations is conducted on the two parts of data. At last, the superresolution image is obtained by combining the two parts of data.

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.

Staggered SAR is an innovative 2-D imaging system utilizing the digital beam-forming (DBF) technique and the continuous variation of pulse repetition frequency (PRF). However, due to the PRF variation, the echo data in the azimuth is non-uniformly sampled. To achieve good imaging quality, it is indispensable to interpolate the data of the staggered SAR to be uniform before further imaging processing. In order to realize efficient interpolation for different PRF variation schemes, we propose a non-uniform fast Fourier transform (NUFFT)-based interpolation method to resample the non-uniform data of the staggered SAR in this paper. The complexity of the proposed interpolation method is in the order of O(MlogM), where M is the number of azimuth sampling. Simulation is performed based on the echo data of point target from the staggered SAR.

The angular superresolution is of great significance for scanning radar in forward-looking imaging. There are many techniques documented in literature to enhance the angular resolution, of which deconvolution method and power spectral density(PSD)methods are favored and attain many interests. In this paper, we focus on analyzing the advantages and challenges of PSD methods in comparison with the deconvolution method. Firstly, three typical PSD estimation approaches are introduced, followed with the comparison with deconvo-lution method that summarizes the advantages and challenges of PSD methods in theory. Simulations are provided in terms of coherence and number of snapshots, which presents the performance of different PSD methods and Lucy-Richardson deconvolution method, better demonstrating the advantages and challenges of PSD methods.

A scheme of fast standard Capon beamformer (SCB) is proposed for scanning radar angular superresolution aiming at the computation burden caused by large swath mapping in azimuth. First, using the similar block tridiagonal property between the covariance matrix and Schur complement of its submatrix, the fast matrix inverse works in an improved divide and conquer (D&C) approach by recursively breaking down the problem of fast inverse into the same sub-problems. Secondly, based on the circulant structure of steering matrix, the SCB estimate of every assumed grids are rewritten by linear convolution. The resulting algorithm is shown to reduce the necessary computational load with one power without noticeable loss of performance.

Most of ever proposed scanning radar angular superresolution algorithms are iterative, but the optimum iterations are difficult to determine. Since the performance of them is related to the signal to-noise ratio (SNR), an accurate SNR estimation would be of great significance to provide reference for assistance of adaptive iteration termination condition analysis. In this paper, a data-aided (DA) SNR estimation approach is developed. This scheme first utilizes the known antenna pattern and identity matrix to construct the steering matrix of signals and noise. Then we introduce the iterative adaptive approach (IAA) to estimate SNR. Simulation validates that this scheme, termed as IAA-SNR can present an adaptive and effective SNR estimation for scanning radar.

Scanning radar imaging has significant and extensive applications, such as surveillance, autonomous landing of aircraft, navigation, guidance. However, scanning radar imaging system suffers troublesomely antenna-induced poor angular resolution. In this letter, a scheme of angular superresolution based on maximum entropy (ME) framework is presented. Firstly, radar received signal in azimuth is modeled as convolution of the antenna pattern and targets' scattering coefficient approximately. Then, principle of angular super-resolution algorithm is deduced. The algorithm can endure disturbance of noise more significantly than conventional techniques. Simulations validate that this algorithm can effectively enhance radar angular resolution. At last, real scanning radar imagery data has proved effectiveness of the proposed method by comparing with Wiener filter technique.

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.

This paper present an efficient matrix inverse algorithm of the recent iterative adaptive approach (IAA) in the application of real beam superresolution (RBS). Based on the inherently band structure of the covariance matrix, the computational complexity of inverse can be reduced by avoiding the computation of zero elements. To achieve this, the divide and conquer (D&C) method will be introduced to fast inverse the covariance matrix. Numerical simulations illustrate the efficiency of the proposed algorithm.

Angular resolution of real aperture radar is limited by the aperture size and wavelength. In this paper, a novel angular superresolution algorithm based on Fourier-Wavelet regularized deconvolution (ForWaRD) is proposed. Orthogonal expansion and scalar shrinkage in a tandem transform domain lie at the core of the ForWaRD. Duo to deconvolution is a noise sensitive process, the method is used to deconvolve and denoise the echo signal simultaneously. We also conclude that optimum performance of this algorithm is simultaneously determined by the Fourier structure of the antenna pattern and the wavelet structure of the surface scatterers. Simulation results show that the algorithm can efficiently enhance the resolution of scanning radar under a low SNR environment.

Near space slow speed SAR owns the ability of sustainable and large-scene imaging. However, the output speed of azimuth image is slow due to the slow speed motion, which is a disadvantage for some applications. In this paper, the terrain observation by progressive scans (TOPS) mode is applied in near space slow speed SAR, and the signal properties are analyzed.

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.

Pulse repetition frequency (PRF) redundancy exists in near space slow speed SAR due to slow speed motion, and then the beam can point to different scene at the same position. Hence, the large scene imaging can be realized. However, the range migration and azimuth resolution is azimuth variant, which makes the imaging difficult. In this paper, imaging mode of near space slow speed SAR is presented, and the variable aperture BP imaging algorithm is proposed. Simulations are given to verify the effectiveness of the imaging scheme.

The angular resolution is limited by the aperture size in real beam radar. To improve the angular resolution, some deconvolution algorithms have been proposed. However, it becomes challenging to estimate the amplitude and location parameters of illuminated targets as signal-to-noise ratio decreases. Through our research, we analyze the similarities of mathematic model and physical principle between array processing and real beam imaging, then in this paper we will show how the iterative adaptive approach (IAA), a spectral estimation method, can be applied to real beam radar for superresolution. The simulation results of real beam radar will be presented to demonstrate the performance of IAA.

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.

In this paper, a frequency domain imaging algorithm is proposed for the spaceborne/airborne bistatic synthetic aperture radar (SA-BSAR) with highly squint angle. The imaging processing is carried out with the following two stages. In the first stage, the space-invariant part of the raw spectrum data is compensated by multiplying with the conjugate of the spectrum from the reference target. In the second stage, the two-dimensional space-variant component, which manifests obvious nonlinear coupling between the range frequency and the Doppler frequency in the high squint case, is effectively corrected by the two-dimensional non-uniform fast Fourier transform (NUFFT) operation. The computation burden of the proposed imaging reconstruction method is O(MN logMN), where MN is the number of the image pixels. Simulation experiments demonstrate the validity of the proposed method.

For high-quality synthetic aperture radar (SAR) processing, Doppler centroid estimation is an essential procedure. An incorrect Doppler centroid would cause a loss of signal-to-noise ratio, an increase in the azimuth ambiguity level, and a shift in the location of the target. Based on the analysis of the range migration characteristic of the transmitter-fixed bistatic forward-looking SAR, a geometry-based Doppler centroid estimator is proposed in this letter. By searching for the range walk slope based on the minimum entropy, this method estimates the unambiguous Doppler centroid directly. Simulations validate the effectiveness of this method.

Doppler centroid is an important parameter for high quality synthetic aperture radar (SAR) imaging. In this paper, several geometry-based Doppler ambiguity resolvers are discussed and compared. The result show that these methods can give accurate estimation of Doppler ambiguity number, and the speed of the iterative scheme and improved Radon transform scheme is much faster than conventional Radon transform method.

Doppler rate is an important parameter in synthetic aperture radar (SAR) imaging. Incorrect Doppler rate would cause great degradation of image quality. In this paper, by estimating the Doppler rate in high-contrast scene first, and then utilizing the fact that Doppler rate is inversely proportional to range, the Doppler rate in low-contrast scene is estimated indirectly and effectively.

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 this paper, we present a vehicle-borne BFSAR experiment, including the imaging principle, system setup, echo property and data processing result. In the experiment, the transmitter is located stationarily besides the moving receiver. The transmit and receive antennas both point to the up forward-looking area to illuminate a point target. To the authors' knowledge, this is the first result of BFSAR with stationary transmitter in the world right now.

For high-quality synthetic aperture radar (SAR) processing, Doppler centroid estimation is an essential procedure. An incorrect Doppler centroid would cause a loss of SNR, an increase in the azimuth ambiguity level, a shift in the location of the target, etc. An improved Radon-transform-based Doppler centroid estimation scheme of bistatic forward-looking SAR is proposed in this letter. First, this scheme performs edge detection on the range-compressed data and then does the coarse and fine Radon transforms to estimate the Doppler centroid. Simulations and real-data experiments validate the effectiveness of this method.