Real aperture radar superresolution imaging of sea surface targets has significant applications in sea surface surveillance and maritime rescue. However, its performance rapidly deteriorates when the echo data of the valid targets are merged into the strong sea clutter. To address this challenge, this paper proposes a multi-feature divide-and-conquer (MF-D&C) framework by forming a complex multi-feature enhancement network (CMFE-NET) and data-divide-and-conquer-based (DD&C-based) sparse Bayesian learning (SBL) algorithm. First, to separate sea clutter echo from valid targets’ echo, a CMFE-NET is proposed to transform the complex echoes into four distinct feature spaces: amplitude, phase, frequency, and dwell time. Second, based on the separated sea clutter echo and the valid targets’ echo, a DD&C-based SBL algorithm is proposed to perform Bayesian parameter estimation on both the clutter and target components, which improves the model parameter estimation performance within the Bayesian framework. Finally, a parameter pruning solver is introduced in EM estimation to eliminate inactive parameters during the iterative super-resolution process, significantly reducing computational overhead. The proposed framework demonstrates superior capabilities resolution enhancement in sea surface target superresolution imaging.

The reconstruction of the scale and shape information of extended targets is a major challenge for real aperture radar. Due to the limitation of angular resolution, the reconstruction of extended targets is inaccurate. To this end, a sparse and two-dimensional high-order gradient (S-2DHG) hybrid prior-based Bayesian method was proposed for real aperture radar to reconstruct the extended targets by introducing a novel scale-constrained prior into the framework of existing hybrid priors. On the one hand, the proposed 2DHG prior establishes interconnections among multiple adjacent units in both the range and azimuth directions during the reconstruction of the scattering coefficient unit and the current target. This interconnection facilitates the formation of a 2DHG prior, which effectively mitigates the influence of sidelobes in both range and azimuth. The sparse prior helps to alleviate the resolution loss of the 2DHG prior. On the other hand, the proposed Bayesian solution framework introduces Jeffery uninformative prior, which can realize the adaptive update of sparse scale prior weight parameters, reducing the number of manually selected parameters. Simulation and experimental results present superior data fidelity and edge preservation ability of the proposed method, which can accurately reconstruct the scale information of the extended targets.

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.

In recent years, iterative adaptive approach (IAA) has been proposed for super-resolution imaging in scanning radar, providing improved azimuth resolution. Traditional IAA involves computing the correlation matrix R for target scattering in each range cell, leading to iterative row-by-row solving and matrix inversion operations, causing high computational complexity. To this end, this paper proposes a Fast Batch-Based Iterative Adaptive Approach (FBB-IAA) that enables parallel and synchronized super-resolution processing of each range cell in the echo matrix. Additionally, it utilizes the two-dimensional conjugate gradient (2D-CG) method to avoid matrix inversion operation, significantly reducing the computational complexity compared to traditional IAA. Simulation results validate the superiority of the proposed method.

Multistatic synthetic aperture radar (MuSAR) has the capabilities of short-time high resolution imaging and multi-angle target observation. The imaging quality of coherent MuSAR depends on the distribution of wavenumber spectrum (WS). In this paper, an imaging performance improvement method based on WS trimming is proposed to obtain high-quality imaging results when the WS distribution is not ideal. First, the echo signals of MuSAR are derived. Then, the distribution of the WS is analyzed, and the WS trimming problem is transformed into a constrained multiple objective optimization problem (CMOP), which is optimally solved by multi-objective particle swarm optimization (MOPSO) algorithm. Finally, numerical simulation are performed to verify the effectiveness of the proposed method.

High-squint super-resolution imaging for airborne scanning radar is crucial in remote sensing and earth information observation. Various methods have been proposed to enhance the azimuth resolution of imaging. However traditional methods are often limited by the requirement for manual adjustment of hyperparameters. In this paper, we propose a hyperparameter-free high-squint super-resolution method for airborne scanning radar based on likelihood based estimation of sparse parameters (LIKES). Compared to traditional sparse imaging methods, our presented approach ensures super-resolution while addressing the issue of manual hyperparameter adjustment. Simulation results demonstrate the effectiveness of the proposed method.

Deep learning has achieved remarkable success with the super-resolution of ordinary optical images. However, synthetic aperture radar (SAR) images have unique imaging mechanisms and features different from optical images, and are faced with problems such as low signal-to-noise ratio, limited resolution, speckle noise and sidelobe, which affect the readability and quality of images. Improving SAR image quality is an important research direction in SAR image processing, and the development of deep learning technology provides a new perspective for improving SAR image quality. Deep convolutional neural networks (CNNS) or other deep learning models are usually used for training and optimization, ignoring the multidimensional features of SAR images. Therefore, we propose a SAR image super-resolution reconstruction network based on multi-scale feature extraction. By considering the multi-dimensionality of SAR image features, the proposed algorithm achieves more accurate image reconstruction, and achieves good results in both quantitative and visual evaluation.

Real aperture radar (RAR) has a limited aperture of the antenna resulting in a low azimuthal resolution. To improve the azimuthal resolution, the L2regularization method is applied to the forward-looking scanning radar imaging. However, the traditional L2regularization method requires an inverse operation, which results in extremely low imaging efficiency. In this paper, we propose a fast frequency domain approach based on Gaussian prior for forward-looking scanning radar imaging. This method transforms the spatial domain inverse convolution problem to the frequency domain and avoids matrix inverse. The fast Fourier transform implementation greatly improves imaging efficiency. The simulation experiments demonstrate the effectiveness of the proposed method.

Super-resolution techniques based on the convolution model of target scattering coefficient and antenna pattem have been widely used in scanning radar forward-looking imaging for past few years. In previous work, the sparse regularization model is proved to be effective in solving the recovery problem of sparse scenes. However, traditional regularization methods usually only add constraints to the target and the regularization parameters are difficult to choose, thus easily causing noise amplification and image recovery distortion. In this paper, a parameter-free estimation method based on low-rank and sparse hybrid constraints is proposed. Firstly, based on the traditional regularization model, sparse and low-rank constraints are added to the target and background respectively to effectively suppress the noise amplification; then, a parameter-free estimation solver is proposed to solve the problem that the regularization parameter is much more difficult to choose. In addition, the superior performance of the proposed method is verified by simulations.

In recent years, the Sparse Iterative Covariance Estimation (SPICE) algorithm has been applied to Direction of Arrival (DOA) estimation in MIMO radar, significantly enhancing radar resolution and quality. However, the high computational complexity of this algorithm poses challenges for real-time processing performance. In this paper, we introduce an online implementation framework for MIMO radar DOA estimation based on the SPICE algorithm. By incorporating sliding window processing, the complexity of the parameter space in each iteration is reduced with minimal resolution loss. Compared to existing SPICE algorithms, the proposed online sliding window qSPICE method achieves substantial computational savings without sacrificing performance. Simulation results demonstrate the superior performance of the proposed method.

Sparse super-resolution algorithm has been used in scanning radar imaging to improve its azimuth resolution. For sparse targets, traditional super-resolution methods usually introduce L1norm to improve azimuth resolution. However, the results obtained based on the L1norm are usually biased estimates, which leads to the limited effect of improving the azimuth resolution. In this paper, a sparse target reconstruction method based on non-convex penalty term is proposed. On the one hand, in order to reduce the bias effect, the L1norm in the cost function is replaced with the SCAD (smoothly clipped absolute deviation) penalty term that is closer to the L0norm. On the other hand, ADMM method is used to solve multi-constraint problems, and we use iterative shrinkage threshold method to solve non-convex optimization subproblem. Compared with the traditional sparse super-resolution method, the proposed method has better performance. The superior performance of the proposed method is verified by simulation and measured data processing.

The imaging process of synthetic aperture radar (SAR) inherently introduces distortions such as blurring, noise, and various disturbances, leading to a notable degradation in image quality. Especially in challenging environmental conditions, SAR images often suffer from reduced resolution and limited detailed information. This letter proposes an innovative super-resolution reconstruction approach utilizing a self-normalized enhanced generative adversarial network (SNEGAN) to address these challenges. The utilization of the scaled exponential linear unit as the generator’s activation function enhances the self-normalization capability of the generative adversarial network, enabling improved adaptation to SAR image scenes. Furthermore, the exclusion of the batch normalization layer is introduced to alleviate computational demands and mitigate model oscillations. Experimental evaluations conducted on RSDD-SAR datasets validate the method’s superior performance in terms of both resolution enhancement and denoising.

Two-dimensional absolute velocity estimation of moving target is a key challenge for real-aperture scanning radar because 2-D velocity estimation methods suffer from low precision or heavy computation load. For example, the traditional Hough-transform-based method can only estimate the along-track velocity with one-order range migration. In this letter, a 2-D absolute velocity estimation method is proposed using the multiorder range migration information. The method extracts the multiorder range variation in adjacent echo sequences, and uses least-square linear fitting to estimate the along-track and cross-track velocity based on the relationship between the target echo and 2-D absolute velocity. Simulation experiments show that the velocity estimation error of the proposed method can be lower than 0.6 m/s, and the estimation time can be less than 1 s.

Automatic target recognition (ATR) holds a crucial position in synthetic aperture radar (SAR) image interpretation. Despite deep learning advancements have significantly propelled SAR ATR, addressing the challenge of target recognition with a few training data remains a vital concern in SAR applications. Two main issues still exist: 1) In few-shot SAR ATR, the depth and width of CNN-based models are limited, which restricts its modeling capacity, and thus extracting discriminative generalized features remains challenging. 2) With only a few labeled SAR images, the resultant class distribution is biased due to the intra-class diversity and inter-class similarity of SAR samples, which degrades the recognition performance. To address these challenges, in this letter, we propose a novel dynamically weighted prototypical learning (DWPL) method. Firstly, to extract discriminative generalized features from SAR images, we propose a new convolutional transformer network with great capacity to capture long-range dependencies of local features, together with an effective random task augmentation strategy. Secondly, in consideration of intra-class diversity and inter-class similarity, a dynamically weighted prototypical module (DWPM) is designed to adaptively assign weights to the few labeled samples that have varying discriminative information. This enables the model to effectively explore the hidden features in few samples. Through experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset, our method achieves recognition accuracies of 97.22% and 92.01% for 3-way 5-shot and 3-way 1-shot SAR ATR tasks in SOC, revealing significant and robust recognition performance.

Forward-looking radar (FLR) has been widely discussed because of its super-resolution capability. However, for the high-speed radar platform, the super-resolution performance of FLR degrades significantly due to the limited signal model accuracy. In this article, to observe the azimuth–elevation information of multiple targets based on a high-speed radar platform, a sparse Doppler phase convolution (SDPC) model is proposed by randomly and sparsely scanning the radar beam to reduce the coherent processing interval (CPI) and limit the signal model errors. On the one hand, the Doppler phase is introduced to characterize the vector superposition relations of the echo in each azimuth–elevation direction, thus limiting the error of the conventional convolution model (CM). On the other hand, an azimuth–elevation sparse scanning scheme is proposed to reduce the CPI, allowing for accurate second-order approximation of the range history and further limiting the reconstructed errors for high-speed radar platforms. In addition, the velocity application boundary and the sparsity boundary of the SDPC model are quantitatively analyzed. Simulations compare and validate the performance of the proposed SDPC model with the conventional CM using three classical super-resolution algorithms. Based on the proposed model, azimuth–elevation information of multiple targets can be accurately reconstructed on high-speed radar platforms.

Deconvolution methods can be applied in airborne scanning radar to enhance its angular resolution for improving the collision avoidance ability in the forward-looking direction. However, as the movement speed of the airborne platform increases, the traditional convolution signal model cannot be applied because of the model errors in the amplitude profile and Doppler phase. In this article, an accurate profile-phase deconvolution method is proposed to achieve scanning radar forward-looking super-resolution imaging, particularly for high-speed platforms. On one hand, a profile-phase convolution (PPC) model is established by analyzing the influence of high-speed platform on echo amplitude profile and Doppler phase. The proposed model accurately captures the variation of beam dwell time caused by the coupling of platform motion and beam scanning, which directly affects the echo amplitude profile. On the other hand, relying on the proposed PPC model, an adaptive regularization (AR) deconvolution method is derived to avoid hyperparameter selection. Point-target and surface-target results demonstrate that the proposed PPC model and the AR deconvolution method are competent for super-resolution imaging on high-speed platforms.

Frequency modulation continuous wave (FMCW) radar has been paid much attention in forward-looking navigation applications because of its no-blind-range capability. However, after dechirp processing, pulse interference signals may appear in the range time domain, which seriously pollutes the whole radiation direction. In this article, a cross-domain low-rank and sparse (CD-LRS) optimization framework is proposed to enhance the angular resolution and suppress the pulse interference signals based on the scanning mode of its antenna. On the one hand, to cut off and recover the polluted signals, a low-rank spectra reconstruction approach is proposed by utilizing the low-rank characteristic of the Hankel matrix formed by the interference-rejected data in the range time domain. On the other hand, to suppress the residual interference signal and enhance the angular resolution simultaneously, an adaptive sparse reconstruction method is formed in the azimuthal time domain by adopting an alternating direction method of multipliers (ADMMs)-based solver. Compared with the traditional anti-interference methods, the proposed framework can enhance the angular resolution and suppress the interference signals based on the signal features in different domains. Simulations and experimental results are applied to verify the effectiveness of the proposed framework.

Currently, regularization methods are widely applied to radar forward-looking super-resolution imaging, but imaging performance is greatly affected by the regularization parameter. In order to address this issue, the regularization method with weak-depended on parameter for forward-looking super-resolution imaging is proposed in our work. First, the objective function is established under the premise of sparse target prior; then, iteratively reweighted solver is applied to resolve the objective function. The key idea is to join the regularization weighting factor in the process of solving sparse regularization problem, which reduces the sensitivity to the regularization parameter and avoids the imaging error caused by unreasonable parameter selection. Compared to traditional sparse regularization method, the proposed method is less dependent on the regularization parameter, and the imaging performance is superior under the same conditions. Simulation results verify the effectiveness of the proposed method.

Constrained by the coarse angular resolution, the real aperture radar (RAR) typically suffers from low location accuracy. Superresolution methods have been proposed to improve its location performance by an enhanced radar image. However, the limit of angular location has not been fully investigated. In this paper, an angular deterministic (conditional) Cramer-Rao bound (CRB) of the RAR is deduced to describe the angular location error bound. First, a virtual array projection model is analyzed in spatial frequency domain, which normalize the signal model of different antenna types. Then, a general form CRB based on complex signal is deduced. Last, the result for a single target is illustrated to verify the deduced CRB. The bound is significant to the design of radar system parameters and superresolution methods.

Recently, a two-dimensional (2-D) scanning radar super-resolution model has been proposed to simultaneously achieve azimuth-pitch super-resolution imaging. However, due to the addition of the pitch dimension, the complexity of the state-of-art methods becomes extremely high. In this paper, based on the sparse learning via iterative minimization (SLIM), we propose a low-complexity 2-D sparse scanning radar super-resolution method. First, the signal model of 2-D scanning radar is established. Then, base on the traditional SLIM method, the 2-D scattering estimation of the target can be iteratively solved by exploiting the conjugate gradient (CG) algorithm and the Kronecker product property. Compared with the existing methods, the proposed method has lower computational complexity and stronger adaptive ability without losing resolution performance. The simulation verifies the effectiveness of the proposed method.

Most of the existing angular super-resolution techniques are based on the convolution model of the target scattering coefficient and the antenna pattern, and this convolution relationship provides the potential for improving the angular resolution. However, due to the non-ideal working environment in practical applications, the antenna pattern generates phase and amplitude errors, resulting in model mismatch. Model mismatch produces errors during super-resolution processing, degrading the final imaging quality. In order to address the issue, an angular super-resolution method of real aperture radar under model mismatch condition is presented in this paper. First, we introduce an error matrix on the original convolutional model to consider model mismatch errors. Secondly, the target sparse prior is exploited to construct the objective function under the model mismatch condition. Finally, the alternating direction method of multipliers (ADMM) solver is utilized to solve the objective function, obtaining the final imaging result. Simulations verify the effectiveness of the proposed method.

To design configuration parameters for coherent multistatic synthetic aperture radar (C-MuSAR), a wavenumber spectra projection (WSP) approach is proposed in this paper based on the relationship between the wavenumber support regions (WSRs) and configuration parameters, including synthetic aperture time, positions and flight directions of receivers. First, the projected pattern of multiple WSRs is deduced, and the relationship between multiple WSRs and the point spread function (PSF) is analyzed. Second, the primary WSR is designed based on the relationship between the transmitter and the leading receiver. A WSP method is proposed to quickly deduce the configuration parameters of the following receivers. Finally, based on the designed configuration parameters of C-MuSAR, an adaptive WSP method is adopted to reconstruct the targets. Simulations are carried out to testify the proposed method.

Scanning radar (SR) super-resolution imaging has been widely reported recently. However, most of the existing methods are based on the amplitude convolution relationship between the reflectivity function and the antenna pattern, realizing super-resolution imaging through deconvolution. When the radar platform moves fast, Doppler phase and pattern distortion caused by the movement destroys the above convolution relation and decreases the super-resolution performance. In this paper, we proposed a pattern distorted complex convolution model to describe SR super-resolution imaging under high-speed platform. Simulations show that the proposed model is more suitable for SR super-resolution imaging under high speed platform.

Recently, multistatic airborne SAR (MuA-SAR) is becoming a research hotspot due to its flexibility. Multi-platform data fusion requires that the imaging algorithm has strong adaptability to the flight path and relative spatial configuration of the airborne platforms. Therefore, the time domain algorithm based on back projection (BP) is suitable. However, in the existing BP-based methods, data needs to be projected into each grid one by one. In fact, not all pixels are target pixels that need to be projected, and the back projection of non-target pixels leads to a lot of invalid computation. Applying these methods directly to MuA-SAR will inevitably lead to a great increase in computation. To reduce the redundant back projection operation of BP algorithm and improve the efficiency of imaging processing in MuA-SAR, a fast imaging method based on segmentation before imaging is proposed in this paper. On the basis of fast factorized back projection (FFBP) algorithm architecture, an image segmentation method based on maximally stable extremal regions (MSER) is introduced. In the process of recursive fusion at each stage, only the pixel information of the segmented suspected target area is transferred to the next stage for fusion, and then the imaging efficiency is improved. The simulation and comparative experiments verify the effectiveness of the proposed method.

Ground-based two-dimensional (2D) array radar suffers from low angular resolution, including azimuth and pitch directions because of the limited size of antenna aperture. In this paper, to improve the two-dimensional angular resolution for a ground-based 2D array radar, a 2D low-rank Truncated Singular Value Decomposition (2D-LRTSVD) superresolution algorithm is proposed by transforming the 2D deconvolution problem into several low-rank inversion problem. First, the traditional 2D convolution signal model is transformed as a low-dimensional signal model by dividing the Kronecker product matrix into several low-dimensional steering matrices. Second, a 2D-LRTSVD method is proposed by compressing the data dimensions of the low-rank steering matrices. Based on the proposed method, the operational complexity can be reduced by avoiding direct high-dimensional matrix inversion. Finally, the 2D angular resolution of array radar can be enhanced without performance loss in a low computational complexity. Simulations are carried out to verify the proposed method.

Several radar systems have been proposed in the past decades, including real aperture radar (RAR) and synthetic aperture radar (SAR). Spatial resolutions of different radar systems cannot be compared together because their work modes are different. In this paper, a normalized spatial resolution analysis model is proposed to deduce the spatial resolution of different systems. First, the normalized wavenumber spectra of different radar systems are deduced. Second, the relationship between spatial resolution and the wavenumber spectra distribution is analyzed. Finally, the point spread functions (PSFs) of different radar systems are simulated.

Spatial resolution of synthetic aperture radar (SAR) is a vital index to evaluate the performance of its observed image. However, high spatial resolution of SAR is achieved at the cost of system resources. Therefore, super-resolution methods can be applied in SAR systems to improve their spatial resolution without system resource increases. In this paper, we propose a new residual network-based structure for super-resolution of SAR images. The proposed method adopts the structure of global residuals and adds several convolutional layers before and after the residual module to take into account the depth and width of the network. The simulation results show that the proposed method is effective as the visual effect and data evaluation.

Recently, the sparse ℓ1-alternating direction method of multipliers (ADMM) based estimation algorithm was introduced for scanning radar, resulting in significant enhancements of the azimuth-pitch angular resolution. Regrettably, not only this method selects the hyperparameters manually, but also its time and space complexity increases rapidly with the data size, which restricts the capacity for applying in hardware system. To this end, a 2D weighted sparse iterative algorithm is derived in this paper, allowing for the hyperparameter-free and efficient sparse reconstruction result of scanning radar for the simultaneously azimuth-pitch resolution enhancement. The proposed method is a fast 2D extension of the current weighted Sparse Iterative Covariance-based Estimation (WSPICE) algorithm, which not only offers much less computational and storage cost, but also enjoys the adaptability with no hyperparameter and finer resolution. Experimental results of simulation and measured data demonstrate the advantage of the proposed method in azimuth-pitch resolution enhancement for scanning radar.

Sparse superresolution algorithms have been applied in scanning radar imaging to improve its azimuth resolution. However, the inverse matrix in each iteration is usually diagonal loading by the updating result, which leads to huge computational complexity for two-dimensional echo data. In this letter, a batch-processing superresolution framework is proposed to process the echo data in parallel. On the one hand, the optimization problem for sparse target recovery is modified as matrix form, which presents batch-processing potential for two-dimensional echo data. On the other hand, the optimization problem is solved by the proposed alternating direction method of multipliers (ADMM)-based batch-processing framework, which can avoid high-dimensional matrix inversion along different range bins. Compared with traditional sparse superresolution methods, the proposed batch-processing framework is much suitable for two-dimensional echo data superresolution.

Scale information is a significant index for target measurement by real aperture radar (RAR). However, the measured target scale information by RAR is inaccurate because of the limited angular resolution. In this paper, to enhance the scale measurement ability of RAR, a generalized hybrid regularization (GHR) approach is proposed by combining the generalized sparse (GS) regularization norm and the generalized total variation (GTV) regularization norm. On the one hand, the GHR approach is proposed to simultaneously enhance the angular resolution and the scale information of targets by combing the generalized regularization norms. The GS regularization norm can improve the reconstructed angular resolution due to its sparsity over the L1 norm. The GTV regularization norm can preserve the steep target contour because of its edge enhancement ability over the total variation (TV) norm. On the other hand, based on the GHR optimization function, an adaptive iterative reweighted (AIR) solver is proposed to reduce the number of manually selected regularization parameters, allowing for accurate scale information reconstruction. Simulations and experiments verify the performance of the proposed method. Based on the proposed approach and solver, the target scale information can be accurately observed.

Multistatic airborne synthetic aperture radar (MuA-SAR) systems can achieve high-resolution imaging in a short time by fusing observation data from multiple radar platforms. However, its imaging quality relies on a rigorous design of the spatial configuration (SC) of each platform, mainly including the relative spatial separation and velocity. The rigorously designed SCs make it difficult to obtain in actual flight and weaken the flexibility advantage brought by the airborne platforms. Therefore, it is meaningful and necessary to explore a new SC design method to obtain relaxed SCs under the condition of ensuring imaging quality. In this paper, to relax the limitations of SC, an optimal design method for MuA-SAR SC is proposed. First, the relationship between the spatial configuration, wavenumber spectrum (WS) distribution, and imaging performance is established, and it visually reveals the configuration limitations. Second, an optimized search space of SC is defined by the peak to sidelobe ratio (PSLR) to relax the space to compromised configurations. Finally, the SC design problem is transformed into a constrained multiple objective optimization problem (CMOP) which is solved by the multiple objective particle swarm optimization (MOPSO) algorithm. The simulation results show that the proposed method can still obtain the optimized SC beyond the strictly restricted configuration space, which expands the SC limitations of the MuA-SAR system.

Multistatic Synthetic Aperture Radar (Mu-SAR) can obtain rich target information through multi-platform collaboration, and topology configuration is one of the most important factors that affecting the imaging performance. In this paper, a Mu-SAR topology design method is proposed. First, the echo of Mu-SAR is analyzed in wavenumber domain, the relationship between wavenumber spectrum and topology configuration is deduced. Then, a topology design method based on wavenumber spectrum range extension is proposed to obtain topology configuration that can achieve high resolution imaging in range direction. Finally, through numerical simulation, the effectiveness of the proposed method is verified.

Stochastic radiation radar (SRR) transmits space-time two-dimensional stochastic signals to achieve superresolution imaging and can overcome the geometric acquisition limitations of traditional synthetic aperture radar (SAR). However, the resolution of an SRR system is limited by the number of effective singular values of its stochastic radiation field (SRF). In this letter, an interpulse frequency hopping accumulation (IFHA) method is proposed to improve the resolution of an SRR system. First, an SRR signal model is introduced. The rank of the traditional SRF generation method is quantitatively analyzed and is limited by the number of transmitting array elements. Second, an IFHA method is proposed to increase the number of the effective singular values of the SRF matrix, which can improve the superresolution imaging performance of SRR. Finally, the simulation results verify the effectiveness of the proposed method.

For real aperture radar, its azimuth resolution is much coarser than the range resolution after pulse compression, super-resolution algorithms are desired to enhance its azimuth resolution. However, the super-resolution algorithms must require enough azimuth sampling to ensure its performance. When wide scanning scope or dense azimuth sampling, the amount of data will increase significantly, which brings large computational burden to super-resolution processing. To cover this problem, we propose a two-step dimension reduction strategy. Firstly, by using linear sketching technology, the high-dimensional matrices are projected to the low-dimensional space, thus accelerating the matrix-matrix multiplications in super-resolution algorithms. Secondly, exploiting Sherman-Morrison formula, we further realized the acceleration of the matrix inversion in super-resolution algorithms. The proposed two-step acceleration strategy in our work is applicable to the existing deconvolution super-resolution algorithms, including regularization methods, Bayesian methods. It can be verified by simulation and experimental data that the proposed accelerated algorithms have advantages in computing time without losing the quality of super-resolution imaging.

Superresolution methods can be applied to real aperture radar (RAR) to improve its angular resolution by solving an inverse problem. However, traditional superresolution methods are achieved after batch data collection, which requires extensive operational complexity and storage space. To solve this problem for RAR, an online detect-before-reconstruct (DBR) framework is proposed in this article based on the sparse property of targets. First, along the range direction, each sample of the echo data is detected to reduce the computational complexity by reducing the dimension of the effective data. Second, along the azimuth direction, a data-adaptive online processing structure is proposed to reduce the storage requirement for the angular superresolution problem. Finally, within the online processing structure, a target data-adaptive updating strategy is proposed to reduce the number of iterations for each target grid. The online DBR-based framework can effectively reduce the operational complexity caused by the noise values of the echo data. Based on the proposed online processing structure, the storage requirement and the operational complexity of the angular superresolution for an RAR system can be greatly reduced without significant reconstruction performance loss. The results of simulations and experimental data verify the proposed framework.

Angular resolution of real aperture radar (RAR) can be improved using deconvolution methods to achieve enhanced target information based on the convolution relationship between target scatterings and an antenna pattern. However, depending on the wide scanning scope and dense sampling angular interval, the computational complexity of the deconvolution methods will drastically increase as the dimension of azimuthal data increases. In this paper, to efficiently improve the angular resolution of RAR, a generalized adaptive asymptotic minimum variance (GAAMV) estimator that relies on a normalized projection array (NPA) model is proposed. On the one hand, the traditional convolution model of RAR is transformed into an NPA model to compress the data dimension. The proposed NPA model can normalize the signal model to make it independent of the sampling parameters. On the other hand, based on the NPA model, a GAAMV estimator is proposed to efficiently reconstruct the targets by adaptively updating each grid. Moreover, the penalty parameter is extended as a generalized case to improve its adaptability to different scenes. Based on the proposed model and method, the computational complexity can be decreased, especially for high-dimensional azimuthal data. Simulations and experimental data verify the proposed model and method.

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.

Synthetic aperture radar (SAR) automatic target recognition (ATR) is an essential field in SAR application. However, a sufficient number of labeled training SAR images for each target type plays a crucial role in existing SAR ATR methods, while the acquisition and annotation of SAR images are difficult and time-consuming in practice. Therefore, the recognition under the limited labeled training SAR images is the basic and crucial problem in SAR application. In this paper, we propose a novel hierarchically-designed lightweight method (HDLM) by recognition in label and discrimination in feature to address the problem of limited data in SAR ATR. The proposed method is hierarchically designed from top to bottom. In the top phase, the framework is constructed by dual loss to force the deep model to optimize by label recognition and feature discrimination, which is noted as recognition in label and discrimination in feature. In the middle phase, the architecture of the network is built up using a novel lightweight extractor and multi-level cross fusion to boost the amount and diversity of the features for the framework. In the bottom phase, two modules, coordinate attention, and depth-wise separable convolution modules are employed to enhance the feature quality and density with fewer parameters for the phases above. The experimental results on MSTAR and OpenSARship showed that the proposed HDLM performs better than the existing methods under the limited training samples.

Cross-resolution enhancement for airborne high-squint radar (AHSR) imagery is mathematically equivalent to the ill-conditioned problem of inverse-scattering reconstruction. Although a variety of inversion methods with regularization can be introduced to advance the field of AHSR imagery, they turn out to be computationally intensive when extended to 2-D (range and cross-range dimension) image formulation due to the range-by-range calculation for the space-variant inversion operators over the full range swath. To tackle the problem of efficiency, this article presents a low-complexity inverse-scattering strategy. Our underlying idea is to equalize the space-variant Doppler centroid embedded in an inversion operator for a reference range cell using Doppler centroid compensation. With the proposed strategy, the necessary computational complexity required for 2-D AHSR inverse-scattering reconstruction can be significantly reduced by requiring only the calculation of the inversion operator, independently of the number of range cells. Our experimental assessment, conducted using both the simulation and real data, demonstrates that our proposed inverse-scattering strategy offers preferable computational reduction in the task of inverse-scattering reconstruction for 2-D AHSR imagery without resolution loss.

Recently, a variety of super-resolution (SR) methods have been devoted to enhancing the angular resolution of real beam mapping (RBM) imagery in modern microwave remote sensing applications. When addressing large-scale datasets, however, they suffer from notably high computational complexity due to high-dimensional matrix inversion, multiplication, or singular value decomposition (SVD). To overcome this limitation, this article presents a low-complexity SR strategy based on adaptive low-rank approximation (LRA). Our underlying idea is first to construct a random matrix sketching to sample the raw echo measurements and restore the surface map of reflectivity in a low-dimensional linear space. The resulting low-complexity strategy enables substantial computational complexity reduction for a group of SR methods, at the cost of introducing a manually adjusted LRA parameter. Using the Fourier transform-based antenna analysis method, we further reveal that the LRA parameter that ensures support resolution improvement can be determined by a closed-form function of the aperture length, the wavelength, and the field of view, allowing for adaptively and efficiently selecting the optimal LRA parameter that well balances the tradeoff between LRA error and computational efficiency. We use both simulated and real datasets to demonstrate that the proposed LRA-based SR strategy can provide significant speedup without performance loss.

Waveform design has become an attractive topic in the field of colocated multiple-input multiple-output (MIMO) radar that allows antennas to transmit different waveforms. Waveform properties of MIMO radar in space, time and Doppler domains determine the performances of resource utilization, interference suppression, and moving target detection. Therefore, simultaneous optimization of multi-domain properties through waveform design is significant to improve the performance of MIMO radar. In this paper, a novel MIMO radar waveform design framework that constrains the beampattern while maximizing the similarity between the designed and desired waveforms is proposed for simultaneous space-time-Doppler domain optimization. To solve the resulting multi-constraint non-convex problem, an efficient beampattern control and similarity maximization (BCSM) algorithm is developed and its convergence is demonstrated. Especially, the coupling problem due to the similarity constraint is handled by transforming the number domain and introducing the proximal algorithm. While reducing the target distortion in mainlobe region and interference in sidelobe region, the proposed method can also maximize the similarity of MIMO transmit waveforms. Numerical simulation results, apart from verifying that the proposed method outperforms existing methods in space-time-Doppler domain, also illustrate the robustness of proposed method in terms of mainlobe width and desired peak sidelobe level (PSL).

Multistatic synthetic aperture radar (SAR) can adopt flexible topology structures to accomplish different missions. When we aim to coherently fuse multiple measurements of receivers, the topology structure of multi static SAR is the key to affect the imaging quality. In this paper, a topology design method based on wavenumber spectrum generation is proposed. The wavenumber spectrum distribution forms the dependency relationship between the imaging quality and topology structures. Based on the analysis of the kernel wavenumber spectrum distribution, the wavenumber spectrum generation is proposed to improve the spatial resolution. Using the generated wavenumber spectrum, the topology structure can be designed accurately. The proposed method effectively enhances the imaging resolution of multi static SAR at a low time cost. Simulation results verify the validity of the proposed 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.

Scene surveillance radar, which generates radar stochastic radiation field with time and space to obtain more observation information, plays a significant role in disaster monitoring and environmental security. To explore its three-dimensional (3-D) imaging capabilities, in this paper, we propose an echo rearrangement super-resolution imaging method to achieve 3D high resolution imaging for SRR. Because the echo of SRR is uncorrelated along sampling time, we adjust the conventional intrapulse frequency hopping to interpulse frequency hopping. In this way, the proposed method can improve the imaging resolution by echo rearrangement utilizing the noncorrelation with time of stochastic radiation field. The 3-D image provides the scene reflectivity estimation along polar coordinate system including pitch, azimuth and space distance. Simulation results are given to illustrate the performance of the proposed method.

Airborne bistatic (and multistatic) radar, which utilizes the spatial diversity of radar platforms to achieve high-resolution imaging, plays a significant role for the next generation radar. Based on the distribution structure of radar platforms, we can deduce the spatial spectrum to reconstruct the targets. However, the processing efficiency of spatial spectrum is different because the echo data can be projected into different shapes in spatial spectrum region. In this paper, an efficient imaging method based on spatial spectrum projection algorithm (SS-PA) for bistatic radar is proposed. First, the spatial spectrum distribution is illustrated based on the system structure. Then, the minimum external rectangular grids are depicted when the spatial spectrum is projected into different directions. Finally, the most efficient imaging view is obtained according to the ratio of spatial spectrum distribution to processing area. The proposed method can provide the most efficient imaging view for bistatic radar, which reduces computational complexity for the system implementation. Simulation result verifies the proposed method.

The truncated singular value decomposition (TSVD) method has been applied to radar forward-looking imaging, however which suffers limited resolution. Especially under low signal to noise ratio (SNR) condition, there is a contradiction between keeping more singular values to improve resolution and suppressing noise amplification. In this paper, a method based on singular value weighted truncation is proposed to improve the resolution under low SNR condition. First, this paper analyses the essence of the conventional TSVD method. Then, the passage constructs a new singular value function to reserve more singular value on the original truncation parameter. Compared with the conventional TSVD method, the more singular values are retained which can improve the resolution under the premise of suppressing noise. Simulations demonstrate the effectiveness of the proposed method.

High resolution is always the most concerned issue of radar imaging. Traditional radar systems, which obtain echo data using single platform, can achieve limited imaging resolution in a specific view angle. Distributed radar system, which expands multi-platform in space to obtain high imaging resolution by forming a large aperture, is a novel and hot research point. Matched filter, such as inverse fast Fourier transform (IFFT), is a conventional method to deal with distributed radar imaging. However, the method relies strictly on geometric configuration. In this paper, an iterative adaptive approach (IAA) based method is proposed to solve the problem of configuration adaptability. It can maintain the performance of matrix during the iteration. Then, the distributed radar system can keep high resolution in different geometric configurations. Simulation results verified the excellent performance of the proposed IAA-based imaging method.

Distributed radar imaging utilizes expanded array elements in space to form a large aperture and obtain high imaging resolution. A great number of array elements are required in traditional distributed radar system which uses multiple platforms. The distribution of spatial spectrum is affected by the number and the signal form of array elements. In this research, to improve the utilization efficiency of platform resources, a resource allocation optimization method based on Unmanned Aerial Vehicle(UAV) is proposed. It chooses the optimized bandwidth and sampling frequency points of array elements by analyzing the relationship between spatial spectrum and imaging performance. This method can use a small number of UAVs to maintain high imaging resolution. Simulation results verified the effectiveness of the resource allocation optimization method for image quality improvement.

Conventional synthetic aperture radar (SAR) reconstructs the illuminated scene via fast Fourier transform (FFT), which results in high sidelobe level, and poor cross-range resolution due to the finite synthetic aperture length. In this paper, we formulate a sparse reconstruction method for SAR imaging based on the covariance fitting criterion, which assumes that only a few strong scatters exist in the whole scene. The method is able to fully control over the sparsity level and reconstruct the scenario in an adaptive manner. Experimental results with real SAR data show the better performance of our method compared with the conventional methods in terms of resolution improvement and sidelobe suppression.

Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods.

Angular resolution of scanning radar is constrained by the size of antenna aperture. Such coarse resolution can not satisfy the applications of microwave remote sensing that require high resolution. Iterative adaptive approach (IAA) is a recently introduced method for scanning radar angular super-resolution, which could notably improve the angular resolution and suppress the noise amplification. In this paper, we further this development, by presenting a beam-recursive I-AA, allowing for adjusting the regularization parameter adaptively and dynamically for varying scenario. Such implementation could effectively eliminate the artifacts on background when applying the batch IAA to resolve closely spaced strong targets. Moreover, the technique offers a promising potential that deserves further attention on computationally efficient implementation and real-time imaging along antenna beam scanning. Simulations are provided to validate the effectiveness of the proposed approach.

The accuracy of Doppler centroid estimation affects the target location and the scene mosaic in the Doppler beam sharpening imaging. Though it can be measured by different sensor instruments of servo, attitude, inertial, the inaccurate measurements decrease the imaging performance. In this paper, a Doppler centroid estimation method based on the morphological edge detection is proposed to obtain the Doppler centroid from the received data with loose-measured parameters. The Doppler frequency in the forward-looking region is symmetrical, but the target carries the highest Doppler frequency. The characteristic can be vividly reflected in range Doppler domain with morphological edge. The Doppler centroid can be estimated via the detection of the edge. The results of centroid estimation and Doppler beam sharpening imaging are given to verify the performance of the proposed method.

The conventional deconvolution approaches which just rely on amplitude information behave worse when the radar platform speed is fast, and the approaches which just use the doppler phase caused by the platform moving can't achieve forward-looking imaging. To achieve forward-looking angular super-resolution for moving radar platform, in this paper, a complex deconvolution method which utilizes both amplitude and doppler phase information is proposed. The complex convolution matrix is constructed through the corresponding relation between the amplitude and doppler phase. The truncated singular value decomposition (TSVD) method is applied to suppress noise amplification to achieve deconvolution. Simulations demonstrate that the proposed method can achieve forward-looking angular super-resolution for moving radar platform.

This paper presents an adjustable angular superresolution method to realize high azimuthal resolution of forward-looking area in scanning radar imaging. Firstly, the received signal in azimuth dimension is established as the convolution model of target scattering coefficient and antenna pattern. Then based on the Poisson statistic assumption, the Generalized Gaussian distribution as prior constraint is used in the maximum a posterior (MAP) method due to the adjustability of dispersion parameter. At last, how to choose suitable dispersion parameter is discussed for better superresolution performance of different scenes. The simulations and experimental results are given to verify the performance of proposed method.

In forward-looking scanning radar imaging, the azimuth resolution can be improved by adding the sparse constraint. However, the azimuth resolution is limited with noise influence by traditional sparse regularization methods. In this paper, we propose a Bayesian super-resolution method that solves the L1regularization problem using the split Bregman algorithm. This method decouples L1and L2norms for the independence of them to reduce the computational complexity. The simulations verify that the proposed algorithm provides a better resolution and de-noising ability compare with conventional methods.

The low angular resolution of scanning radar limits the application in the forward-looking imaging field. This paper proposes the mixed method of truncated singular value decomposition (TSVD) with regularization l1norm to achieve the angular super-resolution. First, the TSVD technique is applied to suppress the noise amplification and keep the main information of targets. Then the angular super-resolution is obtained by analyzing the main information in the form of regularization l1norm. The mixed method has the better performance, comparing with the TSVD method and regularization method. The performance has the lower sensitivity to the regularization parameter. Simulations and experimental results verify the efficacy of this method.

High cross-range resolution of forward-looking region is the key problem of radar imaging. This paper presents a multi-beam system to extend high resolution imaging domain based on the digital beamforming (DBF) technique. The Doppler bandwidth is increased in forward-looking direction while the symmetrical Doppler domain could be significant reduced. Then matched filter technique is employed to deal with the composed received echo. The feasibility and effectivity of this strategy were verified by simulation results.