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Marine radar plays a significant role in ship navigation. However, when contending with interference among cosailing navigation radars, the echo data may be unintentionally corrupted, and it becomes challenging to obtain high-quality imagery using current radar imaging methods. To overcome this problem, an efficient anti-interference imaging framework is presented in this article based on the theory of nonuniform sampling. First, a beam-recursive anti-interference method based on the signal-to-interference-plus-noise ratio (SINR) estimation is proposed to compensate for the shortcoming of the traditional interference rejection method. Second, a nonuniform sampling model is established to well model the echo data with missing samples, which facilitates reconstructing the marine radar imagery from the missing echo data. Finally, a fast super-resolution method based on the dimension-reduction iterative adaptive approach (DRIAA) is proposed to reconstruct the distribution of sea-surface targets at a much lower computational complexity. Simulated and experimental results demonstrate that our anti-interference imaging framework can provide radar imagery with higher quality and lower computational complexity than the existing radar imaging methods in the presence of unintentional interference.

Most existing super-resolution imaging methods fail to work in low signal-to-noise ratio (SNR) condition due to the ill-posed antenna measurement matrix, but the sparse-truncated singular value decomposition (TSVD) method can effectively suppress noise and improve azimuth resolution in low SNR condition. However, the current sparse-TSVD method encounters large computation cost, resulting in a slow algorithm speed. In this work, a fast sparse-TSVD super-resolution imaging method of real aperture radar is proposed. First, the proposed method is based on the results of TSVD, using the truncated unitary matrix and diagonal matrix to reconstruct the signal convolution model. The dimension of the reconstructed antenna measurement matrix reduces from $N \times N$ to $k \times N$ , and the dimension of the reconstructed echo matrix reduces from $N \times 1$ to $k \times 1$ , where $N$ is azimuth sampling points and $k$ is truncation parameter, $N \gg k$ . Much of the expensive matrix– multiplication computation can then be performed on the smaller matrices, thereby accelerating the algorithm. Second, an objective function is established as the ${l_{1}}$ constraint based on the regularization strategy. Lastly, this article employs iterative reweighted least square (IRLS) method to solve the objective function, and the dimension of the reversed matrix is lessened from $N \times N$ to $k \times k$ , speeding up the algorithm further. The simulation and real data verify that the proposed algorithm not only improves the azimuth resolution in low SNR condition but also increases computational efficiency compared with the sparse-TSVD method.

In synthetic aperture radar (SAR) imaging, moving target is generally mixed with stationary targets. Meanwhile, the image of a moving target is distorted and displaced due to the lack of its prior velocity information. Furthermore, imaging of a moving target for geosynchronous (GEO) spaceborne-airborne bistatic SAR (GEO SA-BiSAR) is a more challenging problem because the echo is sub-Nyquist sampled in azimuth. In this article, a simultaneous moving and stationary target imaging method for GEO SA-BiSAR is proposed. First, range models and the corresponding echo models of moving and stationary targets are established. The observation models for both moving and stationary targets with two receiving channels are derived based on the inverse of an efficient imaging algorithm. After that, the imaging problem of moving and stationary targets is modeled as a joint velocity estimation and sparse decomposition problem, which aims at optimizing the entropy of the moving target image and residual error of the formed images at the same time. Finally, a joint optimization method based on the particle swarm optimization (PSO) method and alternating direction method of multipliers (ADMM) is applied to achieve the imaging of moving and stationary targets and estimation of the moving target velocity. With two receiving channels, the accurate separation and focusing of stationary and moving targets as well as the precise estimation of moving target velocity can be achieved with sub-Nyquist sampling echo. Simulation results are presented to validate the effectiveness of the proposed method.

It is greatly significant to achieve radar forward-looking region imaging. Due to the limitation of phase ambiguity and small Doppler gradient in forward-looking region, synthetic aperture radar and Doppler beam sharpening cannot work for forward-looking imaging, while real aperture radar (RAR) has arbitrary imaging geometry. Nevertheless, restricted by the antenna aperture, azimuth resolution of RAR is coarse, super-resolution technology is required to improve its azimuth resolution. Exploiting the sparse prior information of the target, the super-resolution problem can be transformed into an $L_1$ norm minimization problem mathematically. Iterative reweighted algorithm can effectively solve the $L_1$ norm minimization problem by replacing $L_1$ norm with reweighted $L_2$ norm and computing the weight in each iteration. However, it suffers from a large computational load due to the repeated multiplications and inversions of large matrices. In this article, a fast azimuth super-resolution imaging method of RAR based on iterative reweighted least squares (IRLS) with linear sketching (LS) was proposed to achieve fast super-resolution imaging of RAR. The LS theory is employed to compress echo matrix and antenna measurement matrix into much smaller matrices via multiplying them by an embedded matrix. Then, the IRLS solver was utilized to address the reconstructed objective function. Much of the expensive computation can then be performed on the smaller matrices, thereby accelerating the algorithm. Simulations and experimental data prove that the proposed algorithm can offer a time complexity reduction without loss of imaging 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.

Due to its ability of forming continuous images for a ground scene of interest, the video synthetic aperture radar (SAR) has been studied in recent years. However, as video SAR needs to reconstruct many frames, the data are of enormous amount and the imaging process is of large computational cost, which limits its applications. In this article, we exploit the redundancy property of multiframe video SAR data, which can be modeled as low-rank tensor, and formulate the video SAR imaging process as a low-rank tensor recovery problem, which is solved by an efficient alternating minimization method. We empirically compare the proposed method with several state-of-the-art video SAR imaging algorithms, including the fast back-projection (FBP) method and the compressed sensing (CS)-based method. Experiments on both simulated and real data show that the proposed low-rank tensor-based method requires significantly less amount of data samples while achieving similar or better imaging performance.

A multistatic unmanned aerial vehicle-borne synthetic aperture radar (MuUAV-SAR) plays an important role in the applications of environmental monitoring and disaster warning because its distributed platforms can provide high-resolution imagery by fusing the multiple measurements. However, the flight paths of the multiple platforms are limited for such an unmanned system since the flight safety and the path length are basic conditions for guaranteeing the effective observation. This article first studies the observation signal model of the MuUAV-SAR imaging system and then analyzes the factors that determine the imaging resolution, while these factors are all determined by the flight path of unmanned aerial vehicles (UAVs). Second, an MuUAV-SAR imaging path planning problem is established as a constrained multiobjective optimization problem (CMOP), which considers the navigation and imaging performance of the UAV in the process of completing the path planning task in detail. For this CMOP, a heuristic search method is proposed to solve it, which can ensure that each step achieves local optimum, and it can also list all feasible solutions to meet the application requirements for selection. Finally, experimental results verify the effectiveness and practicability of the proposed heuristic path planning method.

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.

Because of the poor performance of the conventional total variation (TV) super-resolution imaging method in low signal-to-noise ratio (SNR) condition, a TV super-resolution imaging method based on the truncated singular value decomposition (TSVD) strategy is proposed. First, based on the regularization theory, the TV function is selected as the constraint term to construct objective function. Second, to solve the problem of noise amplification faced by the conventional TV method, this article reconstructs the objective function based on the TSVD strategy, which improves the antinoise performance by discarding small singular values of antenna convolution matrix. Finally, due to the nondifferentiable property of reconstructed objective function, this article utilizes the iterative reweighted norm (IRN) method. Since the influence of the noise is weakened by the TSVD strategy, the proposed method can achieve super-resolution imaging and contour preservation in low SNR condition. The simulation and experimental results demonstrate the effectiveness of the proposed method.

Forward-looking imaging for sea-surface target has raised many concerns in the fields of vessel monitoring and sea rescue in all-day and all-weather time. Constrained by the imaging principle, conventional high-resolution radar imaging techniques, such as synthetic aperture radar and Doppler beam sharpening, are incapable of forward-looking imaging. Currently, scanning radar is the main tool to obtain forward-looking image, but it suffers from low angular resolution. Recently, much attention has been paid to superresolution methods, which can provide improved angular resolution over that of real-beam scanning radar. However, these methods are mainly used for ground targets, which mainly consider the ground clutter. For sea surface imaging, the distribution of sea clutter is more complex than the ground clutter. Besides, the performance of sea surface imaging is limited by the moving speed of targets. If we directly use these existing methods to sea-surface target imaging, it will lead to amplification of clutter and even elimination of the real target. In this paper, we consider the sea clutter distribution and present a Rayleigh-based sparse maximum a posteriori (RSMAP) deconvolution algorithm to improve the angular resolution of the sea-surface low-speed and static targets. First, the Rayleigh distribution is considered as the likelihood function because it can describe the amplitude characteristic of the sea clutter. Second, since the target concerned is often sparse compared with the imaging region, the sparse constraint is considered as the prior information of the sea-surface targets in objective function. Finally, the targets distribution is obtained by MAP estimation under the Bayesian framework. The simulations and measured data processing results have shown that the proposed algorithm provides better angular resolution and target location accuracy than traditional methods.

Airborne forward-looking radar (AFLR) imaging has raised many concerns in fields of Earth observation, independent of weather and daytime. Constrained by imaging principles, conventional high-resolution radar imaging techniques such as synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) are incapable of AFLR imaging. The real aperture radar (RAR) can obtain AFLR images using a scanning antenna, but suffers from coarse cross-range resolution. Recently, there has been much attention paid to the iterative adaptive approach (IAA), which draws from the benefits of RAR imaging and provides improved cross-range resolution. However, earlier work on the IAA imposed a convolution model on the received azimuth echo, neglecting the effect of the Doppler phase. This model mismatch degrades the imaging performance for moving platforms. To settle this problem, this paper first establishes a Doppler-convolution model of AFLR imaging, where both Doppler phase and antenna convolution effects are considered, allowing more accurate reconstruction when applying the IAA to formulate a super-resolution image. Then, a data-depended approach for Doppler centroid estimation is proposed to circumvent the problem of low estimation precision using platform motion parameters delivered by navigational devices mounted on the radar platform. Simulation results demonstrate that the proposed implementation of the IAA based on the Doppler-convolution model and Doppler centroid estimation can overcome the deficiencies of the SAR and DBS techniques in the forward-looking imaging direction, and present a noticeably superior performance as compared with conventional AFLR imaging methods.

In our earlier study, an energy-efficient passive UAV radar imaging system was formulated, which comprehensively analyzed the system performance. In this article, based on the evaluator set, a mission planning framework for the underlying energy-efficient passive UAV radar imaging system is proposed to achieve optimized mission performance for a given remote sensing task. First, the mission planning problem is defined in the context of the proposed synthetic aperture radar (SAR) system and a general framework is outlined, including mission specification, illuminator selection, and path planning. It is found that the performance of the system is highly dependent upon the flight path adopted by the UAV platform in a 3-D terrain environment, which offers the potential of optimizing the mission performance by adjusting the UAV path. Then, the path planning problem is modeled as a single-objective optimization problem with multiple constraints. Path planning can be divided into two substages based on different mission orientations and low mutual correlation. Based on this property, a path planning method, called substage division collaborative search (Sub-DiCoS), is proposed. The problem is divided into two subproblems with the corresponding decision space and subpopulation, which significantly relax the constraints for each subproblem and facilitates the search for feasible solutions. Then, differential evolution and the whole-stage best guidance technique are devised to cooperatively lead the subpopulations to search for the best solution. Finally, simulations are presented to demonstrate the effectiveness of the proposed Sub-DiCoS method. The result of the mission planning method can be used to guide the UAV platform to safely travel through a 3-D rough terrain in an energy-efficient manner and achieve optimized SAR imaging and communication performance during the flight.

Miniaturized radar near-field imaging systems have become a hot spot for SAR imaging in recent years. The irregular synthetic aperture caused by arbitrary motion of miniaturized system is an important factor affecting imaging quality. On the other hand, the design of fast and high resolution 3D imaging methods is also a major issue. In this paper, a fast time-frequency joint three-dimensional imaging algorithm based on aperture linearization is proposed. First, irregular synthetic aperture is adjusted through special linearization method, and then the target is quickly and accurately imaged. Through formula derivation and simulation test analysis, the results show that the proposed method is feasible and maneuverable, which can solve the current problems.

Traditional synthetic aperture radar (SAR) imaging methods focus on high-resolution imaging of the observation scene. In the application of specific target imaging and detection, such as threat target search, the priori knowledge of target can be used for better task performance. Therefore, it is highly desirable to improve the image quality of the target. In this paper, we propose a SAR formation method based on the sparse dictionary. Firstly, the sparse dictionary is learned through the SAR images of a specific target via K-SVD method. Then the SAR formation model is established as a sparse reconstruction problem by incorporating the sparse dictionary. Lastly, the problem is solved via Augment Lagrange Multiplier (ALM) method and Alternating Direction Method of Multipliers (ADMM) method. The proposed method is validated by the simulation, and the results show that the proposed method can effectively reconstruct the target.

Iterative adaptive approach (IAA) is an effective super-resolution method to improve the resolution of airborne forward-looking radar imaging. Regretfully, the noise sensitivity caused by the non-full rank of matrix lead to the poor performance under low signal-to-noise ratio condition in the forward-looking imaging process. In response to this problem, a regularized IAA method (RIAA) based on singular value decomposition is proposed in this paper which utilizes singular value theory to decompose the autocorrelation matrix in the iteration which is applied to suppress the noise amplification and keep the main information of targets. Compared with conventional IAA method, the proposed method enjoys a preferable noise suppression performance without image quality degradation. Simulations are given to verify the performance gain.

Video Synthetic Aperture Radar (SAR) has received extensive attention in recent years due to its continuous imaging capabilities. Video SAR requires continuous image reconstruction, and there are many redundant operations in the reconstruction process. In order to meet the real-time requirements of video SAR and improve data utilization, we propose a subaperture imaging algorithm based on PFA. First, the echo is divided into multiple subapertures, and coarse focusing is achieved respectively. Then the subaperture echo is subjected to wavenumber mapping to obtain high-resolution high-frame image output. All subaperture data has only been coarsely focused once, and interpolation is not required for wavenumber mapping, which improves the efficiency of image output. Finally, we use experimental data to verify the effectiveness of the algorithm.

Unmanned ariel vehicle (UAV) can provide superior flexibility and cost-efficiency for modern radar imaging systems, which is an ideal platform for advanced remote sensing applications. In this paper, an energy-efficient passive UAV SAR system is proposed and investigated. The UAV platform passively reuses the backscattered signal from an external illuminator, such as SAR satellite, GNSS or ground-based stationary commercial illuminators, and achieves data communication and bi-static SAR imaging at a ground processing station. The mission concept and system block diagram are first presented with justifications on the advantages of the system. A set of mission performance evaluators is established to quantitatively assess the capability of the system in a comprehensive manner, including UAV navigation, passive SAR imaging and data communication. Finally, the validity of the proposed performance evaluators are verified by numerical simulations.

Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution. However, a narrower beam means a larger antenna aperture. In practical applications, due to platform limitations, the antenna aperture is limited, resulting in a low azimuth resolution. The conventional sparse super-resolution method (SSM) has been proposed for improving the azimuth resolution of scanning radar imaging and achieving superior performance. This method uses the L1 norm to represent the sparse prior of the target and solves the L1 regularization problem to achieve super-resolution imaging under the regularization framework. The resolution of strong-point targets is improved efficiently. However, for some targets with typical shapes, the strong sparsity of the L1 norm treats them as strong-point targets, resulting in the loss of shape characteristics. Thus, we can only see the strong points in its processing results. However, in some applications that need to identify targets in detail, SSM can lead to false judgments. In this paper, a sparse denoising-based super-resolution method (SDBSM) is proposed to compensate for the deficiency of traditional SSM. The proposed SDBSM uses a sparse minimization scheme for denoising, which helps to reduce the influence of noise. Then, the super-resolution imaging is achieved by alternating iterative denoising and deconvolution. As the proposed SDBSM uses the L1 norm for denoising rather than deconvolution, the strong sparsity constraint of the L1 norm is reduced. Therefore, it can effectively preserve the shape of the target while improving the azimuth resolution. The performance of the proposed SDBSM was demonstrated via simulation and real data processing results.

Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Residual (SR) and CFAR detector, with the recent advancements in machine learning theory, new methods based on machine learning show better performance in SAR target segmentation. In this paper, we proposed a deep deformable residual learning network for target segmentation, which attempts to preserve the precise contour of the target. The deformable convolutional layers and residual learning block are applied in this technique, which could extract and preserve the geometric information of the target as much as possible. The proposed method is evaluated based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and the experimental results have shown the superiority of the proposed network for the precise segmentation of the target.

Iterative adaptive approach (IAA) is an effective way to improve the resolution of airborne forward-looking radar imagery. Regretfully, the computational complexity of autocorrelation matrix inversion is too high to reach the real-time technical requirement of radar imaging. In response to this problem, an efficient IAA method based on the graphics processing unit framework (IAA-GPU framework) is proposed in this paper which utilizes the multi-core, parallel, and multi-threaded characteristics of GPU framework to perform IAA processing. Compared with conventional implementation based on CPU framework, the proposed IAA-GPU framework enjoys a preferable computational efficiency without performance degradation. The method of implementing parallel IAA computation based on the GPU framework will be explained in detail, and simulations are given to verify the performance gain.

Limited by Mars’s complicated conditions of atmosphere and light, it is difficult to realize all-time and all-weather landing guidance based on optical instrument. To solve this problem, we propose a radar downward-looking method, allowing for mapping the surface of Mars in the stage of probe hovering. To this end, we firstly establish an accurate radial downward-looking echo model according to the mapping geometry, breaking through the mechnism limitation of conventional side-looking synthetic aperture radar (SAR). Then, we incooperate the Multiple Signal Classification (MUSIC) method with the proposed model to reconstruct the super-resolution surface map of Mars. Compared with the existing radar imaging methods, the proposed method make up for the blind area, and improve the safe landing capability for the Mars probe in the complex environment. Simulations are given to verify the excellent imaging performance.

In order to resolve the contradiction between noise suppression and angular resolution improvement of the traditional L 1 regularization method, this paper proposes a hybrid L 1 −L 2 regularization super-resolution method. In the preprocessing stage, the L 2 norm regularization is utilized to smooth the noise and modify the ill-posed property of the deconvolution process. Then an objective function is established as the L 1 constraint to increase the angular resolution. Compared with the conventional L 2 norm regularization method, the resolution is higher; and the sensitivity to noise is reduced compared with the conventional L 1 norm regularization method. Simulation and experimental data results demonstrate the effectiveness of the hybrid norm regularization method.

Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform.

The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an L1 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable L1 regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg–Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from O(N3) to O(N2). It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice.

Synthetic Aperture Radar (SAR) is widely used in environmental monitoring and disaster early warning due to its high resolution imaging performance. A distributed radar system can be established by mounting radars on multiple unmanned aerial vehicle (UAV) platforms. Distributed radar utilizes multiple transmitters distributed in different spatial positions, flying along a certain planned path and enable multiple transmitters to obtain as large an aperture as possible in a certain time. In this paper, an intelligent optimal path planning method for distributed radar short-time aperture synthesis is proposed, which can deal with terrain obstacles and line-of-sight occlusion in UAV flight path and achieve the goal of maximum aperture accumulation in a specific time. Simulation results verified the effectiveness of the UAV intelligent optimal path planning method.

Radar antenna can work in scanning mode to obtain a wide region observation. However, for the targets located at the scene edge, the targets are only swept by less than half of the radar beam. Therefore, the scene edge targets are recovered distortedly using the conventional angular super-resolution methods. To keep the performance of recovered targets in the full scene, in this paper, a data extrapolation-based parallel iterative adaptive approach (PIAA) is proposed. First, we analyze the cause of scene edge target distortion. Then, the echo data is extrapolated by half of the radar beam to compensate the unobserved data. Last, a parallel iterative adaptive approach is proposed to recover the targets efficiently. Simulation data is applied to verify the proposed method.

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.

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.

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.

Airborne radar can observe a wide-region image with coarse resolution using a scanning antenna. Because the aperture size of the antenna on a motion platform is limited, super-resolution imaging methods based on different echo models are presented to improve its angular resolution. In this paper, the applicable scopes of the super-resolution imaging models are compared quantitatively. First, the traditional models of scanning radar, such as the approximate convolution model (ACM) and the complex phase-convolution model (CPM), are introduced, and we analyze the echo models in spatial frequency domain. Then, according to the comparison of the spatial bandwidth of the antenna pattern and that of the platform motion, the strict region bound and the relax region bound for scanning radar are analyzed. At last, the numerical region bounds are deduced to analyze the influencing factors. The bounds are significant for the working mode design and imaging method selection for a scanning radar. Simulations verify the analyzed region bound.

Conventional split Bregman algorithm (SBA) has been used for improving the azimuth resolution for radar forward-looking imaging. However its convergence speed is not satisfactory. In this paper, a vector extrapolation based fast SBA (VEFSBA) is presented to accelerate the convergence of traditional SBA and improve azimuth resolution of radar forward-looking imaging. Based on the principle of vector extrapolation, the proposed VEFSBA adopts the second-order vector extrapolation strategy to reduce the number of iterations of traditional SBA. It not only effectively improves the azimuth resolution of radar forward-looking imaging, but also greatly reduces the number of iterations compared with the traditional SBA. Simulation and measured data are presented to demonstrate the superior performance of the proposed VEFSBA.

Iterative adaptive approach (IAA), based on the weighted least squares estimation (WLS) criterion, can effectively improve the azimuth resolution of airborne forward-looking radar imagery. Regretfully, the brute force IAA requires a large number of inversions of high-dimensional autocorrelation matrix, resulting in notably high computational complexity. In this paper, a low-rank iterative adaptive approach (LR-IAA) is proposed to solve this problem. Our underlying idea is to construct a low-rank Gaussian matrix to randomly sample the original echo model, and restore the original scene through spectral estimation method in a low-dimensional linear space. Compared with brute-force implementation, the proposed LR-IAA enjoys a preferable computational efficiency without performance degradation. Simulations are given to verify the performance gain.

The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) is promising to benefit the higher degree of freedom for synthetic aperture radar (SAR) imaging. Combining the traditional two-dimensional (2D) SAR and the vortex EM wave, vortex SAR can obtain the three-dimensional (3D) information to realize 3D imaging. This letter proposes a novel 3D vortex SAR imaging method based on the vortex EM wave to obtain 3D target information. First, a vortex SAR imaging model is established by the proposed 3D imaging algorithm. Then, the modified range-doppler (RD) algorithm is therefore proposed to reconstruct the target's profiles. The objective of the 3D RD algorithm is to decompose the time-domain correlation operations of the SAR 3D space into three independent one-dimensional correlations and then perform separate focusing processing on each dimension. Finally, the 3D information of the targets can be obtained according to the modified RD algorithm. The point target simulation is performed in this paper. The simulation results establish the effectiveness of the proposed technique. This paper lays the groundwork for the development of vortex EM waves in the field of 3D radar imaging.

Target reconstruction is one of the most important missions in the fields of radar signal processing. In this paper, we propose a new deep learning-based approach to reconstruct the target information from the scanning radar returns. Unlike the traditional analytical methods, a deep neural network with a topology of linear chains of convolutional layers is designed, and the input radar signals will be learned layer by layer through the network, which a direct map from the radar echo to the reflectivity function of the targets is obtained during the learning procedure. Finally, we can get the optimal deep learning network as the reconstructing map to recover the scanning radar target information effectively. Simulation results have shown the superiority of the proposed method under different target scenes.

Distributed radar is more flexible because of transmitters and receivers are separately distributed in this system. It can form multiple bistatic SAR systems according to the detection requirements in a self-organized manner, and obtain more effective information in a shorter time. During the movement of the radar platform, a specific spatial spectrum will be generated in the wavenumber domain. By splicing the spatial spectrum of multiple bistatic SAR systems, the resolution in the range direction or cross range direction can be improved. In this paper, the rule of spatial spectrum distribution in the wavenumber domain is analyzed in detail, and a method is provided for the topology design of radar platforms in combination with the splicing spatial spectrum. Numerical simulation gives imaging results after topology design, which demonstrates the effectiveness of distributed radar topology design method for improving range or cross range resolution.

Motion error and compensation is a crucial problem for bistatic SAR. In this paper, based on the geometry model of bistatic SAR considering motion error and topographic variation, the concept of bistatic SAR iso-MoCo curve is proposed, which refers to the contour line of motion error compensation for bistatic SAR. Analytical expression of bistatic SAR iso-MoCo curves under flat terrain conditions is deduced via a conic section model. Properties of motion errors considering spatial variation with and without topographic variation are analysed based on simulation results.