Gait is an appealing biometric pattern that aims to identify individuals based on the way they walk. Gait recognition, a passive human identification technology utilized from a distance without subject cooperation, plays a considerable role in life monitoring, crime prevention, security guarantee, and other identity recognition applications. Although vision-based methods dominate the state-of-the-art field, their performance degrades under poor illumination. In contrast, radar signals are not affected by light and are more sensitive to micro-motion information. In this article, we design a Kinetic feature-based Radar-Vision fused (KiRV) gait recognition method, which leverages millimeter-wave radar echo signals and a video for illumination robust human identification. In the KiRV, we propose a novel kinetic gait feature representation framework based on radar micro-Doppler and visual optical flow information, which are the direct expressions of the gait motion process. The physical meaning of the kinetic features under the two modalities is similar, while the semantic information is complementary. Therefore, the two features can be effectively fused. To learn robust gait information, we propose two 2-D residual CNN-based lightweight backbone networks to encode the kinetic features, respectively, and further propose a two-stream cross-correlated fusion method, including radar-vision cross-correlated fusion (RVCF) and radar-vision gate unit (RVGU) modules. The RVCF adaptively adjusts the attention to radar and vision for better recognition performance, while the RVGU controls the contribution of each modality to the fused feature to improve the robustness of the model. Finally, the gait retrieval task can be achieved through the above innovative model and joint loss calculation at different feature levels. Extensive experiments are conducted in the real world and semi-simulation, demonstrating that the KiRV outperforms state-of-the-art gait recognition methods with well-illumination robustness.

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.

Bistatic Synthetic Aperture Radar (BiSAR) enables highly flexible configuration, offering broad application prospects. However, existing BiSAR imaging algorithms neglect the complex scattering characteristics of the target, resulting in the loss of target structural information in the imaging results. To enhance the target structural information in imaging results and improve the interpretability of BiSAR images, we first establish the BiSAR echo model based on the target scattering model and analyze the target’s imaging characteristics by incorporating the imaging mechanisms. Subsequently, based on the imaging characteristics, we propose a structure-driven multi-stage BiSAR trajectory planning method (SMTP). This method solves a multi-stage multi-objective optimization problem driven by target scattering characteristics, thereby fully presenting all discernible structural features in the imaging results. Numerical simulation experiments validate the proposed method, demonstrating its ability to recover target structural information. This approach addresses the gap where BiSAR mission planning has largely overlooked target-specific characteristics.

Synthetic aperture radar (SAR) is commonly used for ship imaging on the sea. By using small target detection algorithms, ship targets can be highlighted under different SAR backgrounds for detection and observation. Local contrast measurement (LCM) has poor detection performance in situations with strong background noise or uneven distribution, and its results cannot preserve the shape features of the original targets well. Inspired by LCM, this paper proposes a discrete cosine transform (DCT) based local contrast enhancement detection algorithm. This algorithm enhances the target area according to AC coefficient, and experimental verification and analysis show that the proposed algorithm has better detection performance and higher level of precision than LCM in the presence of complex background noise.

In the field of polarimetric synthetic aperture radar (PolSAR) automatic target classification (ATR), convolutional neural network (CNN) based methods have excelled owing to their adept feature extraction capabilities. However, these methods heavily rely on a sufficiently labeled training dataset for superior classification performance. Limited PolSAR training samples and inevitable noisy labels often render CNNs susceptible to overfitting. To tackle this challenge, a novel PolSAR image classification method employing beta mixture model and boundary amplification is proposed. Initially, the beta mixture model is utilized to fit the loss value distributions of noisy and clean samples, enabling the exploitation of distinct characteristics between these samples for probability estimation. Subsequently, to emphasize boundary samples, the boundary is delineated and expanded using the Sobel operator, amplifying losses for samples within this expanded region. Finally, a robust classification loss function is integrated into the training process to rectify losses incurred by network predictions. Experimental validation conducted on the Flevoland dataset demonstrates that the proposed method attains state-of-the-art performance.

Automatic target recognition (ATR) based on synthetic aperture radar (SAR) images has already obtained remarkable achievements on closed-set task. However, the recognition in a real-world scenario should not only identify the known classes but also appropriately deal with the unknown ones. To this end, we propose a SAR open-set recognition method aided by hierarchically reconstructive latent representation learning. First, a unsupervised representation learning via hierarchically-fused reconstruction network (HFRNet) is proposed to complement the lost information in supervised representation and obtain a preliminary closed-set result. Then, we adopt Openmax to correct closed-set recognition scores and give the probability of being the unknown ones, realizing the effective open-set recognition on SAR images. Finally, experimental results based on the measured dataset have shown the superior performance of the proposed method.

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.

Ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental step in the maritime surveillance. However, recognition of ships inevitably faces background interference in the maritime environment. The interference guides the network focusing on useless even harmful regions. To deal with issue, a mixed attention mechanism consists of coordinate and Squeeze-and-Excitation(SE) attentions is introduced. The mixed attention can guide the network to focus more on the target region, decreasing the influence of useless interference regions. Experimental and visualize results on benchmark dataset OpenSARShip validate the effectiveness of our idea.

Target recognition is one of the most significant tasks in synthetic aperture radar (SAR) image interpretation. However, due to the varying difficulty in acquiring SAR images for different categories, SAR target recognition often encounters the issue of categories imbalance. This make majority categories contribute more to the loss than minority categories, yielding a decline in classification performance. To this end, a novel SAR target recognition approach under imbalanced categories is proposed. Firstly, focal loss (FL) is introduced to balance contributions of minority and majority categories to model optimization. Then, a first-order flatness constrained FL is devised to minimize the high generalization error effectively. Finally, a gradient norm aware minimization (GAM) algorithm is implemented to integrate first-order flatness into optimization process, yielding favorable recognition results for both minority and majority categories. Experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the effectiveness of our proposed method.

With the launch of high-resolution (HR) synthetic aperture radar (SAR) imaging satellites and the rapid development of convolutional neural networks (CNNs), ship recognition in HR SAR images has shown further improvements. Unlike ship targets in low and medium-resolution SAR images, which only possess a few pixels and present a spot-like appearance, ship targets in HR SAR images pose a larger area of pixels. However, CNN lacks the power to model dependencies between long-range features occupying large areas of pixels. A convolutional transformer (CvT) is introduced to deal with this issue. CvT integrates the local features of CNNs and the capability of capturing long-range dependencies of transformers to model both local and global dependencies for ship recognition in an efficient way. The cosine-margin loss is also applied to constraint strictly the distribution of the features to further improve the performance. Experimental results on the benchmark FUSAR-Ship dataset demonstrate the effectiveness of the proposed method for ship ATR in HR SAR images.

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.

Synthetic aperture radar (SAR) has been widely applied in maritime target detection. However, most existing SAR ship detection algorithms based on convolutional neural network (CNN) only use single polarization SAR images for detection, neglecting to further improve the detection performance by utilizing the rich polarization information of the SAR images. To deal with this issue, this paper proposes a Cascaded Feature Fusion Pyramid Network (CFFPN) for ship detection in dual-polarization SAR images. The CFFPN builds a cascaded feature fusion module (CFFM) to fuse the enriched polarization information in SAR images. Extensive evaluations conducted on the the dual-polarization SAR ship detection dataset showcase the remarkable effectiveness of CFFPN, achieving an average precision (AP) of 93.4%. This outperforms the other five competitive methods. Notably, CFFPN exhibits a notable improvement of 1.3% in AP compared to the second-best 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.

Airborne scanning radar imaging is widely used both in military and civilian fields. However, the azimuth resolution of the imaging system is constrained by the size of the antenna. Iterative optimization super-resolution algorithms based on regularization can be used to overcome this limitation. But these methods demand manual tuning of parameters, which can be laborious. Deep unfolding network is a method of unfolding iterative optimization algorithms to deep learning networks, which combines the interpretability of iterative algorithms and the advantages of deep learning. Considering the excellent performance of the deep unfolding network in other signal processing tasks, this paper introduces the deep unfolding network based on fast iterative shrinkage-thresholding algorithm (FISTA) called FISTA-Net into the field of scanning radar imaging. For the task of scanning radar azimuth superresolution, we improve the model to fully learn the characteristics of radar azimuth data. Simulation results verify the effectiveness of the proposed method.

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.

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.

Deep learning contributes to significant improvements in synthetic aperture radar (SAR) target recognition performance. Most SAR target recognition methods are based on supervised learning and require labeled SAR data. There only exists limited labeled data due to the time-consuming and laborious work of labeling, and there is still a large amount of available unlabeled radar data. Therefore, we aim to explore whether unlabeled data can provide the network with sufficient feature information and enable the network to cluster similar target features and distinguish different target features, thereby improving the SAR target recognition performance. In this paper, we propose a new framework to train a deep neural network for SAR target recognition to eliminate the need for a large amount of labeled training data. Our idea is based on transferring knowledge from a self-supervised model, where the data can train without label information. Experiments are performed on the moving and stationary target acquisition and recognition (MSTAR) benchmark dataset, and the experimental results demonstrate the improvements in recognition performance achieved by our proposed method with limited labeled data.

This paper constructs a detection network that combines convolutional neural network and Transformer in parallel to address the challenges of multi-scale target detection in remote sensing images. The network utilizes global and local information interaction to further improve the effectiveness of multi-scale object detection tasks. Additionally, the network introduces both top-down and bottom-up pathways to fuse multi-scale information, and employs coordinate attention mechanism to perform feature selection. The proposed network is compared with some existing networks on the LEVIR remote sensing image dataset, and the results show that the proposed network achieves higher average detection accuracy in terms of multi-scale target detection, particularly for small objects.

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.

This paper considers waveform design for MIMO radar to synthesize a desired beampattern under similarity and constant modulus constraints. Generally, the constructed framework is a complex nonconvex optimization problem, which is difficult to solve directly. To tackle this problem, we convert it into a neural network-based learning problem. In particular, an objective function is developed to characterize the similarity constraint that makes the design waveform have good characteristics similar to the reference waveform. Then, we design a joint loss function for optimizing the transmit beampattern and waveform similarity, which allows the designed waveform to have better detection performance. Numerical simulation results show that the proposed method has better performance than the existing state-of-the-art method.

Synthetic aperture radar (SAR) target recognition networks performance has been remarkably improved, posing serious exposure risks to our high-value targets. Researches have shown that it is valid to protect our high-value targets by generating adversarial examples. However, most existing SAR adversarial examples generation approaches are based on the premise that irregularly global perturbation data can be directly added to SAR images, which is difficult to implement in practice. To this end, a target partial-occlusion SAR adversarial examples generation approach is proposed in this paper. First, the target region in SAR image is extracted using the combination of OTSU algorithm and morphology operations. Then, the random search (RS) algorithm is introduced to optimize the occlusion position in the extracted target region with the constraint of occlusion area and value, so as to misclassify the SAR target recognition networks. Experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown 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.

In the field of maritime surveillance, ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental step. Compared with traditional methods, convolutional neural networks (CNNs) tend to be the mainstream in SAR ship recognition. However, these methods ignore one core issue. Multi-scale features can enhance the expression ability of features, which are currently not well-exploited. In response to this problem, a novel feature weaving pyramid network (FWPN-Net) is proposed. FWPN-Net contains a multi-scale feature weaving module (MFWM), which can integrate high level semantic information and low level detailed information to obtain better representations of multi-scale SAR ship features. Experimental results on benchmark dataset OpenSARShip show that the proposed FWPN-Net performs better than classic CNN methods and modern SAR ship recognition CNN method.

Recently, deep learning has gained increasing popularity in array signal processing. In this paper, we estimate the direction of arrival (DOA) for the multiple-input and multiple-output (MIMO) radar system based on deep learning. First, we convert DOA estimation into a linear inverse problem with spatial sparsity, and construct a neural network based on the iterative shrinkage thresholding algorithm (ISTA) to improve the interpretability of the network. Then, a stacked denoising autoencoder (DAE) is employed to achieve data-driven denoising, which improves the anti-jamming ability of DOA estimation. Finally, a new deep unfolded network named denoising learned ISTA (Denoising-LISTA) is proposed for DOA estimation. Simulation results illustrate that the proposed method improves the robustness of DOA estimation with single snapshot sampling and keeps significant predominance in beam sharpening and sidelobe suppression.

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.

Recently, super-resolution techniques have been widely used in real aperture radar super-resolution imaging. And the majorize-minimization(MM) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this paper, we strive to alleviate this problem, deriving an online MM algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The proposed method is a regularized extension of the current MM implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current MM algorithm. Our experimental assessment, conducted using simulated data, demonstrates the advantage of the online MM(OMM) algorithm in the task of sparse reconstruction for scanning radar.

Reasonable analysis of radar interference effect is of great significance for adjusting jamming strategy in radar counter-measures (RCM). The modern battlefield is confronted with non-cooperative targets, so the conventional offline evaluation methods are difficult to apply. In this paper, a comprehensive evaluation method for radar interference effect based on the integrated cloud model is proposed. Firstly, a multi-layer index system for interference effect evaluation is established. Subsequently, the entropy method is employed to determine the weight of each indicator. To avoid the occurrence of hypertrophy as an imaginary number, the cloud parameters for each indicator are calculated using a modified inverse cloud generator. Eventually, a comprehensive assessment of the interference effect can be obtained by drawing the integrated cloud. The experimental results show that the proposed method is effective and can be applied to the evaluation of interference effectiveness in non-cooperative environments.

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

Recently, a sparse super-resolution method relying on L1iterative reweighted norm (IRN) has been proposed to improve the imaging resolution of scanning radar. However, the method has poor adaptability due to the noise-sensitive user-parameter. To this end, an adaptive L1iterative reweighted sparse super-resolution method with no user-parameter is derived. Firstly, the scanning radar super-resolution model is established. Secondly, the user-parameter selection in the L1-IRN method is analyzed. Finally, the adaptive iteration weights are derived by transforming the sparse estimation problem into a maximum posterior (MAP) estimation problem. Compared with the existing L1-IRN method, the proposed method does not have any user-parameter, so it has adaptability to different signal-to-noise ratios (SNR) and is more robust. Simulation verifies the superiority of the proposed method.

In this paper, the constrained waveform design of multiple-input multiple-output (MIMO) radar is considered to achieve transmit beampattern assignment. Firstly, we construct a framework that minimizes the spatial integrated sidelobe level ratio (ISLR) as the objective function and constrains the transmit waveforms in terms of amplitude fluctuations and similarity. To solve the resulting non-convex problem, an iterative optimization method based on coordinate descent (CD) is developed by transforming the multivariate problem into multiple univariate problems. Finally, numerical simulation results demonstrate the effectiveness of the proposed method in beampattern assignment and waveform similarity.

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

Multiview high-resolution range profiles (HRRPs) of aerial targets contain more target information than single-view one and will benefit accurate classification. However, feature information in HRRPs dynamically varies across different views, thus a dynamic classification framework is required to adjust the structure of the network along with the feature information variations and effectively make full use of those multiview features. To this end, we propose a dynamic-structure neural network (DSNN) with skip extraction and adaptive fusion blocks to adjust the network structure and adaptively fuse multiview features, enabling accurate aerial target HRRPs classification. In the skip extraction block, the skip gate automatically changes the block depth of each view to fit feature information variations, which ensures multiview HRRP features are dynamically exploited and extracted by the network. Then, in the adaptive fusion block, features from different views are weighted by the adaptive weight gate and effectively fused using associated attention, which further contributes to the classification. Besides, since the skip gate dynamically downsizes the extraction block for some views, the computational cost of DSNN is also reduced to some extent. Experimental results demonstrate that the proposed method has superior aerial target multiview HRRPs classification performance and computational efficiency over other state-of-the-art methods.

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.

Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful and challenging to distinguish the unseen target classes, i.e., open set recognition (OSR) problem, which is an urgent problem for the practical SAR ATR. The key solution of OSR is to effectively establish the exclusiveness of feature distribution of known classes. In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes. Through meta-learning tasks, the proposed method learns to construct a feature space of the dynamic-assigned known classes. This feature space is required by the tasks to reject all other classes not belonging to the known classes. At the same time, the proposed entropy-awareness loss helps the model to enhance the feature space with effective and robust discrimination between the known and unknown classes. Therefore, our method can construct a dynamic feature space with discrimination between the known and unknown classes to simultaneously classify the dynamic-assigned known classes and reject the unknown classes. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown the effectiveness of our method for SAR OSR.

Maritime surveillance is indispensable for civilian fields, including national maritime safeguarding, channel monitoring, and so on, in which synthetic aperture radar (SAR) ship target recognition is a crucial research field. The core problem to realizing accurate SAR ship target recognition is the large inner-class variance and inter-class overlap of SAR ship features, which limits the recognition performance. Most existing methods plainly extract multi-scale features of the network and utilize equally each feature scale in the classification stage. However, the shallow multi-scale features are not discriminative enough, and each scale feature is not equally effective for recognition. These factors lead to the limitation of recognition performance. Therefore, we proposed a SAR ship recognition method via multi-scale feature attention and adaptive-weighted classifier to enhance features in each scale, and adaptively choose the effective feature scale for accurate recognition. We first construct an in-network feature pyramid to extract multi-scale features from SAR ship images. Then, the multi-scale feature attention can extract and enhance the principal components from the multi-scale features with more inner-class compactness and inter-class separability. Finally, the adaptive weighted classifier chooses the effective feature scales in the feature pyramid to achieve the final precise recognition. Through experiments and comparisons under OpenSARship data set, the proposed method is validated to achieve state-of-the-art performance for SAR ship recognition.

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.

Ship recognition in synthetic aperture radar (SAR) images is essential for many applications in maritime surveillance tasks. Recently, convolutional neural network (CNN)-based methods tend to be the mainstream in SAR recognition. Though considerable developments have been achieved, there are still several challenging issues toward superior ship recognition performance: 1) Ships have a large variance in size, making it difficult to recognize ships by using a single scale features of CNN. 2) The SAR ship’s large aspect ratio presents an obvious geometric characteristic. However, standard convolution is limited by the fixed convolution kernel, which is less effective in processing elongated SAR ships. 3) Existing CNN classifiers with softmax loss are less powerful to deal with intraclass diversity and interclass similarity in SAR ships. In this paper, we propose a task-specific hierarchically designed network with a spherical space classifier (HDSS-Net) to alleviate the above issues. Firstly, to realize SAR ship recognition with large size variation, a feature aggregation module (FAM) is designed for obtaining a feature pyramid that has strong representational power at all scales. Secondly, a FeatureBoost module (FBM) is devised to provide rectangular receptive fields to refine the features generated by FAM. Finally, a novel spherical space classifier (SSC) is proposed to expand the interclass margin and compress the intraclass feature distribution by fully taking advantage of the property of spherical space. The experimental results on two benchmark datasets (OpenSARShip and FUSAR-Ship) jointly show that the proposed HDSS-Net performs better than classic CNN methods and novel SAR ship recognition CNN methods.

Without sufficient data, the quantity of information available for supervised training is constrained, as obtaining sufficient synthetic aperture radar (SAR) training data in practice is frequently challenging. Therefore, current SAR automatic target recognition (ATR) algorithms perform poorly with limited training data availability, resulting in a critical need to increase SAR ATR performance. In this study, a new method to improve SAR ATR when training data are limited is proposed. First, an embedded feature augmenter is designed to enhance the extracted virtual features located far away from the class center. Based on the relative distribution of the features, the algorithm pulls the corresponding virtual features with different strengths toward the corresponding class center. The designed augmenter increases the amount of information available for supervised training and improves the separability of the extracted features. Second, a dynamic hierarchical-feature refiner is proposed to capture the discriminative local features of the samples. Through dynamically generated kernels, the proposed refiner integrates the discriminative local features of different dimensions into the global features, further enhancing the inner-class compactness and inter-class separability of the extracted features. The proposed method not only increases the amount of information available for supervised training but also extracts the discriminative features from the samples, resulting in superior ATR performance in problems with limited SAR training data. Experimental results on the moving and stationary target acquisition and recognition (MSTAR), OpenSARShip, and FUSAR-Ship benchmark datasets demonstrate the robustness and outstanding ATR performance of the proposed method in response to limited SAR training data.

Remote sensing images (RSIs) play an important role in a wide range of applications. However, they are frequently contaminated by multiple kinds of noises and existing methods are mostly applied to suppressing single noise type and performs poorly for various noises. To deal with above deficiencies, we propose a learning-based multi-type noise suppressing method (MNSM). Firstly, “Parallel” denoising approach is utilized to obtain partially denoised images that supply sufficient information for the subsequent fusion task. Mean-while, the noise recognition net identifies noise type and adjusts the brightness of every partially denoised image, realizing the adaptivity for different noises. The fusion net lastly merges these images to acquire one clean image. Experimental results show that this approach obtains higher peak-signal-to-noise ratio (PSNR) than existing methods.

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.

In the field of remote sensing, harbor detection in SAR images has an important application prospect. However, the complex coastline of SAR images increases the difficulty of harbor detection. In response to this problem, a cascaded harbor detection (CHD) method for SAR image based on corner and coastline features is proposed in this paper. First, coast-line is extracted from SAR image by sea-land segmentation. Then, in the first step rough detection, corner detection is performed on the coastline and the detected corners are automatically clustered to locate the harbor candidate areas. Finally, the second step precise detection is carried out on the coast-line of harbor candidate areas, where coastline feature detection is completed by using corners again to remove the fake harbor targets in harbor candidate areas. Experimental results based on satellite-borne SAR data prove the proposed CHD method enjoys a preferable detection performance compared with existing harbor detection methods.

Exploiting the sparse property of the target of interest to achieve super-resolution imaging has been applied to real aperture radar (RAR) forward-looking imaging field. In this paper, we proposed a regularized FOCUSS method to realize RAR forward-looking super-resolution imaging. In addition, we discussed the influence of initialization on the imaging result, and selected the most suitable initialization for RAR forward-looking super-resolution imaging. Compared with the traditional sparse method based on Majorize-Minimization, our proposed algorithm has faster convergence speed under the same parameters condition.

The real aperture radar (RAR) system can present full-view observation capability, but the coarse azimuth resolution restricts its application. Therefore, various super-resolution deconvolution methods are widely used in the real aperture super-resolution imaging field. But conventional deconvolution approaches only rely on amplitude information of antenna pattern profile, it will behave worse when forward-looking imaging with high speed or squint imaging. This paper analyzes the influence of phase and constructs a corresponding amplitude-phase model to resolve this problem. Finally, the effectiveness of the proposed amplitude-phase convolution model for forward-looking imaging with high speed or squint imaging is verified by simulations.

SAR and optical images registration is a key step for remote sensing image processing, match navigation and information fusion. Although there are many methods for SAR images registration, their performance will decrease between SAR and optical images. Moreover, these algorithms suffer from lack of matching pairs of the feature points and uneven distribution between SAR and optical images. Therefore, they cannot accurately achieve the registration between optical and SAR images. To solve the above deficiencies, we propose an efficient image registration approach based on SAR and optical image-scale invariant feature transform (SOI-SIFT). Firstly, a linear edge enhancement based on gray feature and histogram equalization is introduced. In this stage, we enhance the edge features of the image so that the number of image feature points can be greatly increased. Then, for feature points purification, we use fast sample consensus algorithm to filter duplicate and wrong matching feature points. SOI-SIFT can be more adapted to the heterogeneous image matching. Experimental results have shown the superiorities of the proposed method.

Recently, super-resolution techniques have been widely used in real aperture radar superresolution imaging. In this paper, we propose a fast sparse superresolution algorithm which is based on majorize-minimization(MM) method to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we establish a model of rader forward-looking imaging and analyze the echo signal. Second, we use the majorize-minimization (MM) method to obtain the real target distribution. Due to the expensive computational cost of MM algorithm, we proposed an fast matrix inversion approach which is based on divide and conquer strategy. The superior performance of the proposed method is verified by simulations.

Multi-view SAR images contain richer target information than single-view, which is beneficial to synthetic aperture radar automatic target recognition (SAR ATR). It is a huge challenge to select the best observation viewpoints and the most suitable flight path for multi-view SAR ATR in an unknown environment. Therefore, we propose a multi-view SAR ATR optimal observation path planning method in this paper. The geometrical and the optimization mathematical models based on the task requirements are constructed, and the convolutional neural networks with two inputs are designed as the base classifier. An autonomous path planning method forms the best observation path planning in the absence of global information of the surroundings. Thus the selection of the optimal viewpoint for multi-view SAR ATR is solved by the path search algorithm. The multi-view SAR images are collected on the solved optimal viewpoints, and the final recognition result is obtained by the base classifiers ensemble. Experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown that the proposed method obtains superiority in optimal observation path planning.

Modern warfare has entered the era of information and networking, where electronic warfare (EW) is of vital importance. Operation mode recognition occupies an important po-sition in EW, while the overlapping waveform parameters of airborne radar operation modes make it difficult to accom-plish the recognition task in complex electromagnetic environments' especially under low signal-to-noise ratio (SNR) regions. Analyzing the time-sequential regularity of radar pulse parameters and intermediate frequency (IF) sampling signals, this paper designs a novel representation of operation modes, and proposes a multi-feature residual-and-shrinkage ConvNet (RS-ConvNet) with an attention mechanism to iden-tify multiple air-to-air modes. Simulation results show the proposed method has superior performance under low SNRs.

The ambiguity function (AF) of the transmit waveform is an important reflection for cognitive radar detection system performance, and spectral coexistence is equally critical in the current frequency-congested electromagnetic environment. In this paper, a joint optimization metric related to AF and energy spectral density (ESD) is considered to improve the probability of target detection, which is accomplished by designing the radar transmit waveform. Additionally, the unimodular constraint limited by the radar transmitter is imposed on the transmit waveform. To handle the resulting nonconvex problem, an iterative optimization procedure with a closed-form solution is developed leveraging the iterative sequential quadratic optimization (ISQO) framework. Numerical simulation results are provided to demonstrate the effectiveness of the proposed approach.

Multiple-input multiple-output (MIMO) radar beampattern design has aroused extensive attention, in view of the improved detection capability, the enhanced system adaptiveness and the controlled spatial energy distribution. In addition, the ambiguity function of transmit waveform is also an important indicator of radar system performance. Thus, jointly considering the radar beampattern design and waveform similarity constraints, this paper applies the coordinate descent (CD) algorithm framework to develop an indirect ripple control approach, where beampattern ripple suppression can effectively reduce target distortion. Numerical simulation results verify the effectiveness of the proposed method in controlling ripple and similarity.

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.

In radar forward-looking super-resolution imaging, improving the azimuth resolution while acquiring the contour information of the target has significant research value. In this letter, an approach based on the balanced Tikhonov and total variation (TV) deconvolution is proposed for radar forward-looking super-resolution imaging. We combine the Tikhonov regularization and TV regularization to construct the objective function and resolve the respective cost function using the alternating direction method of multipliers (ADMM). In each iteration, the gradient function of the target scattering coefficient is used as the adaptive weighted parameter to control automatically the weighting between the penalty terms from TV and the Tikhonov regularization. For the target with a sharper outline, the proportion of TV regularization penalty terms is increased; for the target with a smoother outline, the proportion of penalty term from the Tikhonov regularization is enhanced. The simulation and experimental results are considered to show the effectiveness of the proposed method. Compared with traditional super-resolution imaging methods, the proposed approach has superior outline retention capacity.

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.

Convolutional neural networks (CNNs) have dominated the synthetic aperture radar (SAR) automatic target recognition (ATR) for years. However, under the limited SAR images, the width and depth of the CNN-based models are limited, and the widening of the received field for global features in images is hindered, which finally leads to the low performance of recognition. To address these challenges, we propose a Convolutional Transformer (ConvT) for SAR ATR few-shot learning (FSL). The proposed method focuses on constructing a hierarchical feature representation and capturing global dependencies of local features in each layer, named global in local. A novel hybrid loss is proposed to interpret the few SAR images in the forms of recognition labels and contrastive image pairs, construct abundant anchor-positive and anchor-negative image pairs in one batch and provide sufficient loss for the optimization of the ConvT to overcome the few sample effect. An auto augmentation is proposed to enhance and enrich the diversity and amount of the few training samples to explore the hidden feature in a few SAR images and avoid the over-fitting in SAR ATR FSL. Experiments conducted on the Moving and Stationary Target Acquisition and Recognition dataset (MSTAR) have shown the effectiveness of our proposed ConvT for SAR ATR FSL. Different from existing SAR ATR FSL methods employing additional training datasets, our method achieved pioneering performance without other SAR target images in training.

Ship target segmentation plays an important role in synthetic aperture radar (SAR) image interpretation. However, existing segmentation methods for marine SAR images have the problem of inaccurate edge segmentation, a concern for real-world applications. In this letter, we propose a clustering center shifted adaptive target segmentation (CCSATS) method. Firstly, the proposed clustering center shift method is used to update the clustering centers of each iteration, which can quickly and accurately capture ship pixels. Then, based on regional homogeneity coefficients, we define a new similarity measurement criterion with two adaptive weight factors to ensure the homogeneity of segmentation results. Finally, neighborhood patches are used to represent pixel information, which can reduce the influence of speckle noise and enhance the target edge fitting ability. Our segmentation results of measured SAR images show that the proposed method effectively ensures segmentation accuracy. Compared with other existing methods, the proposed target segmentation method achieves better edge capture performance.

The generalized sparse iterative covariance-based estimation ($q$-SPICE) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this letter, we strive to alleviate this problem, deriving a beam-updating$q$-SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online$q$-SPICE implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current online$q$-SPICE. Our experimental assessment, conducted using both simulated and real data, demonstrates the advantage of the beam-updating$q$-SPICE method in the task of sparse reconstruction for scanning radar.

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.

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).

To realize high azimuth resolution for sea-surface targets, this paper proposes a superresolution imaging method that relies on the Weibull distribution. The proposed method introduces the generalized Gaussian distribution and Weibull distribution to represent the statistical distribution function of the target prior information and sea clutter, respectively. The corresponding objective function was derived under the maximum a posteriori (MAP) criterion. To address the nonlinearity of the objective function, this paper adopts the NewtonRaphson iterative method to resolve it. Simulations and experimental data assessment indicate that the proposed method has superior superresolution imaging performance compared with other traditional superresolution methods for sea-surface target imaging.

SAR image segmentation is a key step of SAR image interpretation, boosting target detection and recognition. Since similar targets may exist in complex and changeable scenes, under-segmentation and over-segmentation often occur in SAR image segmentation. To solve the above deficiencies, we propose a superpixel aggregation method based on multi-direction gray level co-occurrence matrix (GLCM) for SAR image segmentation. Firstly, a linear similarity judgment based on gray feature and spatial distance of pixels is introduced. In this stage, we expand the search range of clustering centers and add constraints to reduce the deviation, so as to alleviate over-segmentation. Then, for the spatial adjacent su-perpixels, we use multi-direction GLCM to measure texture similarity between them, merging homogeneous superpixel-s to solve under-segmentation. Experimental results based on satellite-borne SAR images from different scenes illustrate that the proposed method performs well with excellent pixel accuracy, effectively solving under-segmentation and over-segmentation.

Convolutional neural networks (CNN) show superior potential in synthetic aperture radar automatic target recognition (SAR ATR). However, due to the difficulty of obtaining SAR images and the scarcity of labeled SAR images, supervised learning has poor performance in this area and is not widely applicable. To address this problem, a semi-supervised conditional generative adversarial network with a multi-discriminator (SCGAN-MD) is proposed in this paper. In our method, a conditional generative adversarial network (CGAN) is adopted with two discriminators for training the generated images and predicting the labels for unlabeled samples. Compared with other semi-supervised learning-based methods, our proposed method has more accurate image generation capability and can achieve improved recognition accuracy of SAR ATR. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database indicate that the proposed method can effectively improve the recognition accuracy and robustness of the network with a small number of labeled samples.

Airborne radar high-squint looking imaging is an important research for remote sensing. The traditional Doppler beam sharpening based on fast Fourier transform (FFT) has good real-time performance but low cross-range resolution. Many super-resolution methods have been proposed to enhance the cross-range resolution for airborne radar. However, these methods generally adopt the batch processing mode with high computational complexity and high memory usage, which lead to poor real-time performance. This paper proposes an online super-resolution imaging approach for airborne scanning radar based on sliding window recursive least square (SWRLS) algorithm. The current scattering estimation can be derived recursively through downdating and updating. The proposed method effectively improves the cross-range resolution as well as the real-time performance and memory occupancy, which is beneficial to high-quint continuous realtime imaging for airborne radar. Simulation results are given to demonstrate the effectiveness of the proposed method.

Real aperture super-resolution (RAS) technology is widely used in the field of radar forward-looking imaging. However, traditional RAS technology is based on the space-to-ground scanning mode. The essence of this technology is azimuth (angle) super-resolution, which is a one-dimensional super-resolution technology. In our work, we consider applying RAS technology to the space-to-space scanning. In this mode, we regard the echo of each range slice as the convolution of the target scattering coefficient distribution and the antenna pattern function. Its essence is azimuth and pitch super-resolution, which is a two-dimensional super-resolution technology. Finally, a reasonable objective function is constructed under the framework of regularization, and the ADMM solver is used to achieve two-dimensional super-resolution imaging. Simulations will prove the effectiveness of the proposed two-dimensional super-resolution algorithm.

In the field of radar electronic reconnaissance, radar signal recognition is a key technology. In the actual task, part of the signals to be recognized may come from new types of emitters, which can not be identified directly by the existing recognition system. In order to get the ability to recognize new categories, it is necessary to analyze the unrecognized samples for incremental learning. In this paper, a new categories identification method based on reliability test is proposed. Firstly, an existing clustering method is used to label the unrecognized samples, and then the reliability test criteria are designed, including quantity criterion, distance criterion and frequency criterion, to screen the clustered sample points. The proposed method provides better data support for incremental learning in radar signal recognition. Simulation results show the effectiveness of the proposed method.

Recently many deep neural networks have been utilized to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (A-TR). However, in actual applications the types and amount of data that can be obtained are limited and difficult, which makes it hard to train the networks effectively. In this paper, we propose a multi-view deep learning framework combined with deformable convolution for SAR ATR. The scattering distribution characteristics and morphological characteristics of the target will be learned by the special structure of the deformable convolution, providing more sufficient information for subsequent fusion of features from the distinct views. Experimental results have shown the superiority of the proposed network based on the Moving and Stationary Target Acquisition and Recognition data set and the better recognition performance in the condition of a small number of raw SAR images.

Marine surveillance radar can monitor the marine environment in all-weather conditions, but the presence of sea clutter will seriously affect its target detection performance. In this paper, we proposes a sea clutter suppression method based on machine learning that contains two pairs of generative adversarial networks (GANs), in which one GAN is used to learn the mapping relationship of sea clutter suppression, and the other is used to ensure the performance of clutter suppression. Matching loss is proposed to preserve clutter suppression performance. Experimental results have shown the superior performance of the proposed method in improving the signal-to-clutter ratio (SCR) and the stability of clutter suppression.

The echo received by ground-to-air radar is a range-azimuth-pitch three-dimensional data. After pulse compression, the data of each range unit can be regarded as an azimuth-pitch two-dimensional (2D) echo. The resolution of azimuth and pitch is limited to antenna aperture. In this paper, the well-known Wiener filtering, Richardson-Lucy (RL) and total variation (TV) methods are introduced to simultaneously improve the azimuth-pitch resolution of ground-to-air radar. We first analyze the received signal of ground-to-air-radar, and model the echo of each range unit as a 2D convolution of target reflectivity distribution and azimuth-pitch antenna pattern. Then we deduce the Wiener filter, RL and TV methods in detail, and theoretically realize the super-resolution imaging of the azimuth and pitch. Finally, the super-resolution performance of different methods is verified by simulation.

Designing radar waveforms with desired autocorrelation properties is a key point in the development of cognitive radar. To solve the problem of concealing weak targets by strong targets in detection, we consider minimizing the weighted integrated sidelobe level (WISL) metric in frequency domain where the weak targets are located. In order to directly solve the complex non-convex optimization problem, an iteration algorithm based on the general framework of the iterative sequential quartic optimization (ISQO) algorithm that can guarantee fast convergence to a static point is developed. Numerical simulations are provided to assess the effectiveness of the proposed algorithm.

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.

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.

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.

Synthetic aperture radar (SAR) is an important microwave detection for remote sensing and reconnaissance, and identifying the attributes of the targets from the SAR images which is known as SAR automatic target recognition (ATR), is an important tool in SAR applications. Many SAR ATR methods based on deep learning need to acquire a large amount of training data first, and the types and amount of data that can be obtained are limited and difficult in actual applications. Thus, we propose a deformable convolution neural network for SAR ATR, which not only extracts the scattering distribution characteristics of the target, but also extracts the morphological characteristics of the target through the special structure of the deformable convolution, providing more sufficient information for recognition. Experimental results demonstrate that the proposed network has an extremely high recognition rate in the moving and stationary target acquisition and recognition data, and it can maintain excellent recognition performance even when the amount of data gradually decreases.

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.

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

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

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm.

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.

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.

This paper proposes a parking space information monitoring system by millimeter wave synthetic aperture radar (SAR) based on unmanned aerial vehicle (UAV). Parking space information that people are concerned about includes vacant parking place, parking place occupied by obstacles and place parked by vehicles. Specially, the free parking space detection is an important module for the parking guidance system (PGS) that can help drivers to find parking space efficiently. In this system, we obtain high resolution SAR images of parking lots at first. Then, in order to define the free parking space, Maximally Stable Extremal Region (MSER) method is exploited to leach the candidate regions occupied by vehicles from millimeter wave SAR images. Next, the system utilize visual saliency detection method to extract obstacles from the non-parked parking space acquired by pre-detection. Ultimately, the three types of information have been determined, including vacant parking space, parking space occupied by obstacles and the parked place. Experimental results prove that the integrated scheme performs well in parking information determination.

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

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.

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

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

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.

Coastal surveillance has long been paid a lot of attention for the threat of flooding due to some natural phenomena, such as global warming. Prompt and accurate reaction to the visualization of the flooded areas is the key. An image fusion rule is thus proposed in this paper to achieve image enhancement of the flooded areas. The rule, targeted at high-frequency parts of the synthetic aperture radar (SAR) and optical images, is able to exploit and combine the merits of both SAR and optical images to obtain the exact flooded areas with the clear boundaries. Experimental results validate the performance of the proposed fusion rule and show that not only the clarity of fusion images is improved, but also the texture and brightness contrast are greatly enhanced.

Due to the difficulty of sea scene synthetic aperture radar (SAR) trial, SAR image simulation for sea-ship scene is vitally important for the research of sea remote sensing and surveillance. In this paper, a new SAR image simulation method for sea-ship scene is proposed. Firstly, the geometrical models of sea surface and ship target are obtained through sea spectrum and CAD modeling technology respectively. Then the SAR image intensity data of sea surface is calculated by small perturbation method (SPM) and velocity bunching (VB) theory, meanwhile the radar cross section (RCS) data of ship target is computed through physical optics (PO) method. Finally, the SAR image of sea-ship scene is generated by SAR imaging method after transforming image intensity data and RCS data to the same spectrum domain. The simulation result has verified the effectiveness of the proposed method.

Airborne radar forward-looking imaging is of great significance in many remote sensing applications. However, the existing synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) imaging techniques are incapable of forward-looking imaging. The real aperture radar (RAR) using a scanning antenna can provide forward-looking images, but suffers from coarse azimuth resolution. In this paper, we extend the iterative adaptive approach (IAA) to forward-looking super-resolution imaging. Different from the conventional forward-looking convolution model, both the Doppler phase and antenna convolution are considered in the new model, allowing for more accurate reconstruction of the forward-looking imaging scenario when applying the IAA. Simulation results demonstrate that the IAA-based super-resolution imaging can overcome the deficiencies of the SAR and DBS techniques in forward-looking imaging direction.

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.

Identifying the aspect for a given target is an important issue in synthetic aperture radar (SAR) image interpretation. A new SAR target aspect identification method based on machine learning theory is proposed in this paper. First, the aspect angles of the SAR target are discretized, and the spatial relationships of the neighborhoods of the SAR target samples are established. Then an optimal linear mapping is solved based on the proposed subspace aspect discriminant analysis. The samples will be projected into a low-dimensional space and be of a better aspect identifiability than in their original space. Finally, the projected samples are fed into a multilayer neural network, and the aspects of the SAR targets will be indicated. Experimental results have shown the superiority of the proposed method based on the moving and stationary target acquisition and recognition (MSTAR) data set.

Bistatic synthetic aperture radar (SAR) can provide additional observables and scattering information of the target from multiple views. In this paper, a new bistatic SAR automatic target recognition (ATR) method based on multi-input deep convolutional neural network is proposed. The geometry of the multi-view bistatic SAR ATR is modeled, and an electromagnetic simulation approach is utilized as an alternative to generate enough bistatic SAR images for network training. Then a deep convolutional neural network with multiple inputs is designed, and the features of the multi-view bistatic SAR images will be effectively learned by the proposed network. Therefore, the proposed method can achieve a superior recognition performance. Experimental results have shown the superiority of the proposed method based on the electromagnetic simulation bistatic SAR data.

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.

It is great significant to achieve clear outline reconstruction for radar forward-looking imaging. In this paper, we apply the total variation (TV) function as the regularization term operator to obtain the forward-looking imaging with clear outline. Firstly, we establish the deconvolution model, by which the forward-looking super-resolution imaging problem is converted into inverse problem. Then, taking the TV function as regularization constraint term, we construct the objective function to solve the inverse problem. Finally, we obtain the minimum of the objective function, by which we can achieve radar forward-looking super-resolution imaging with clear outline. Simulations verify effectiveness of the proposed method in reconstructing the outline of targets.

This paper presents an angular superresolution method based on maximum a posterior (MAP) criterion to improve the azimuthal resolution of forward-looking scanning radar in the background of sea clutter. Firstly, in consideration of the statistical property of sea clutter, the Rayleigh distribution is employed to express the likelihood function. And then, the generalized Gaussian constraint is used as prior information about targets for better property of noise suppression and positional accuracy. Finally, the iterative expression is derived to recover the scattering coefficient of original sea-surface targets. Compared to the conventional Bayesian approaches, The results of simulation experiment are given to verify the superior performance of proposed method.

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 this paper, the Cramér-Rao Bound (CRB) for scanning radar sensing is investigated, providing an algorithm-independent bound on the angular estimation error. Based on the deterministic signal model, we first derive a numerical CRB for unknown real signal parameters. Then, the approximate closed-form expression of CRBs are further provided for the single target case. Meanwhile, the potential estimation error of various classical super-resolution sensing methods are quantitatively investigated in this paper, and compared with the presented CRB.

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.

It is a feasible and promising way to utilize deep neural networks to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (ATR). However, it is too difficult to effectively train the deep neural networks with limited raw SAR images. In this paper, we propose a new approach to do SAR ATR, in which a multiview deep learning framework was employed. Based on the multiview SAR ATR pattern, we first present a flexible mean to generate adequate multiview SAR data, which can guarantee a large amount of inputs for network training without needing many raw SAR images. Then, a unique deep convolutional neural network containing a parallel network topology with multiple inputs is adopted. The features of input SAR images from different views will be learned by the proposed network layer by layer; meanwhile, the learned features from the distinct views are fused in different layers progressively. Therefore, the proposed framework is able to achieve a superior recognition performance, and requires only a small number of raw SAR images for network training samples generation. Experimental results have shown the superiority of the proposed framework based on the Moving and Stationary Target Acquisition and Recognition data set.

Multiview synthetic aperture radar (SAR) images could provide much richer information for automatic target recognition (ATR) than from a single-view image. It is desirable to find optimal SAR platform flight paths and acquire a sequence of SAR images from appropriate views, so that multiview SAR ATR can be carried out accurately and efficiently. In this paper, a novel optimization framework for multiview SAR ATR is proposed and implemented. The geometry of the multiview SAR ATR is modeled according to the recognition mission and flight environment. Then, the multiview SAR ATR is abstracted and transformed into a constrained multiobjective optimization problem with objective functions considering the tradeoffs between recognition performance and efficiency and security. A specific approach based on convolutional neural network ensemble and constrained nondominated sorting genetic algorithm II is employed to solve the multiobjective optimization, and optimal flight paths and corresponding imaging viewpoints are obtained. The SAR sensor can thus choose an applicable flight path to acquire the multiview SAR images from different tradeoff solutions according to application requirements. Finally, accurate recognition results can be obtained based on those multiview SAR images. Extensive experiments have shown the validity and superiority of the proposed optimization framework of multiview SAR ATR.

Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals. The technique is formed by initially introducing a coarse grid dictionary constructed from integrating elements, spanning bands of the considered parameter space. After forming estimates of the initially activated bands, these are retained and refined, whereas nonactivated bands are discarded from the further optimization, resulting in a smaller and zoomed dictionary with a finer grid. Implementing this scheme allows for reliable sparse signal reconstruction, at a much lower computational cost as compared to directly forming a larger dictionary spanning the whole parameter space. Simulation and real data processing results demonstrate that the proposed wideband estimator offers significant computational savings, without noticeable loss of performance.

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

This paper describes a new technique for generating bistatic sea clutter returns based on the compound K-distribution model for clutter amplitude statistics. The technique adopts the computational electromagnetic (CEM) method to calculate bistatic sea clutter reflectivity by the given bistatic geometrical relationship, aiming at obtaining the parameters of the distribution. Then the theory of spherically invariant random processes (SIRP) is used to generate the returns of the bistatic sea clutter following compound K-distribution. This study can be used to evaluate the bistatic radar signal model and predict system detection performance in the sea clutter environment. Simulation results verify the proposed technique.

Recognizing the category attributes from the real world targets is one of the most challenging and attractive fields in synthetic aperture radar (SAR) application. It is an important issue to explore the spatial distribution characteristics of multi-aspect angle imagery in synthetic aperture radar automatic target recognition (SAR ATR). In this paper, we will research the spatial structure of multi-aspect angle SAR imagery through a visualization approach with real SAR data. Based on nonlinear dimensionality reduction, the representation of SAR samples is revealed in the low-dimensional Euclidean space, and the the nonlinear manifold distribution of multi-aspect angle SAR imagery is discovered. Besides, the regularity of that spatial distribution is summarized, i.e. the intrinsic structure of SAR images is parameterized by the aspect angles. The results of our research can provide a theoretical basis for SAR image classification and recognition algorithm designing.

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.

Target recognition based on forward-looking imaging has many potential applications. However, the conventional algorithms fail to locate targets accurately due to the low resolution of forward-looking radar images. Meanwhile, the conventional algorithms always suffer from high computational complexity and cannot satisfy the requirement of real-time processing for high-speed platform. This paper proposes a novel target recognition method based on morphological and spatial features. The algorithm comprises of initial matching and dual verification algorithms based on image gray scale and a priori position information. It is demonstrated that the proposed algorithm can work well for the forward-looking radar images with coarse resolution and enjoy higher computational efficiency. Simulation and real data processing validates the superior performance of the proposed algorithm.

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

Ship detection in sea clutter environment using scanning radar is of vital importance, but with challenges due to low angular resolution. To solve the problem, an angular superresolution algorithm for radar imaging based on Bayesian deconvolution theory is proposed. Firstly, the statistic characteristics of the sea clutter are modeled using compound K-distribution. Then the signal model of radar echo in sea environment is formulated as the convolution of the antenna pattern and the reflectivity of the original scene plus the reflectivity of sea clutter. The ship detection task in sea clutter environment using the deconvolution method is converted into an equivalent maximum a posteriori estimation problem, which is solved using the optimization method in this paper. Simulation results demonstrate the validity of the proposed method in terms of ship detection in sea clutter environment.

This paper presents a deconvolution algorithm based on the Maximum likelihood (ML) criterion to realize azimuth angular superresolution of sea-surface target in the background of sea clutter. Firstly, the received signal of real-beam image in azimuth dimension was modeled as the convolution of antenna pattern and target scattering. Then the ML objective function was built according to the assumption that the sea clutter obeys Rayleigh distribution. Finally, the iterative expression was derived to recover the original scattering of sea-surface target. Compared to Poisson-based ML deconvolution method, the assumption of clutter distribution is more reasonable for the practical background. Simulations are given to verify the effectiveness of the algorithm.

This paper presents a sparse deconvolution algorithm based on the Maximum a Posterior (MAP) criterion to improve the azimuth angular resolution of scanning radar. For high resolution of few targets in large imaging scene, the sparse property was considered as the prior information to combine with the classic Richardson-Lucy (R-L) iterative algorithm to obtain sparse solution. Besides, the sparsity constraints also help to suppress the amplification of false target and noise of R-L algorithm. Simulations are given to verify the effectiveness of our algorithm.

Radar image resolution is a controlling factor in the radar imaging application. In this paper, we propose an approach to radar angular super-resolution through sparse deconvolution, which is able to increase the resolution of radar image beyond the limitation of system parameters. It relies on the optimization approach that enables to incorporate the prior information about the system and the statistical characteristics of scene. We first formulate the radar angular super-resolution problem as a constrained optimization problem and then convert it to an equivalent unconstrained optimization task using augmented Lagrangian method. We then solve the unconstrained optimization problem in the convex optimization framework using iterative method. Numerical experiments with real data demonstrate that the validity of the proposed method.

Angular super-resolution performance is the key problem in the field of radar imaging. In this paper, we propose an approach to radar angular super-resolution through deconvolution, which is able to increase the resolution of radar image beyond the limitation of system parameters. It relies on the Bayesian formulation approach that enables to incorporate the prior information about the system and the statistical characteristics of scene. We first formulate the radar angular superresolution problem as an linear inverse problem and then convert it to a maximum a posterior (MAP) task using Bayesian theory. We then solve the MAP problem in a convex optimization framework using a shrinkage based iterative procedure, leading to algorithm that guarantees the solution to converge the global maximizer of an associated MAP criterion. Numerical experiments with synthetic data demonstrate the performance of proposed angular super-resolution algorithm.

This paper present a novel superresolution algorithm for real-beam scanning radar based on the iterative adaptive strategy. Firstly, we establish the objective function based on the minimum mean-square error (MMSE) criterion, then we build the iterative expression by update the covariance matrix. This algorithm has better superresolution performance than traditional deconvolution method. Simulation results verified the analysis before.

This paper present a superresolution algorithm for forward-looking imaging of scanning radar based on weighted least squares method. This algorithm utilized the weighted vectors to structure the objective function, and introduced the diagonal loading technique to obtain more robust superresolution result. Simulation results verified the performance of our algorithm.

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

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