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.

Synthetic aperture radar automatic target recognition (SAR ATR) with limited data has gained significant attention as practical application requirements change. Despite many proposed methods, key problems caused by the limited SAR data remain under-researched, hindering further performance improvement. In this article, we establish an SAR ATR model based on causal theory. It compares the causal effect of SAR ATR between cases with ample and limited data, showing that the negative impact of the confounder, which is blocked with ample data, is introduced with limited data, resulting in poor performance of limited-data SAR ATR. To address this, we propose a limited-data SAR ATR causal method via dual invariance intervention, which first derives the causal interventional solution. This solution is transformed into two optimizable objectives: inner-class feature invariance and the independence of features from the confounder. Subsequently, the dual invariance mechanism is designed to filter SAR outlier samples and noise features under limited-data conditions, accurately obtaining the intraclass invariant feature. It also alleviates the need for ample SAR data when optimizing the independence of features from the confounder, achieving the two objectives. Finally, the proposed method not only unravels the key problem caused by limited data but also derives an effective solution with precise recognition performance. Extensive experiments on three benchmark datasets validate the rationality of the causal SAR ATR (CSA) model, the effectiveness of the solution, and the soundness and recognition performance of the method. The codes and more experimental results will be released athttps://github.com/cwwangSARATR/SARATR_Causal_Dual_Invariance.

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.

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.

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.

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.

Synthetic aperture radar automatic target recognition (SAR ATR) methods often struggle due to inadequate training data. In this letter, we introduce a causal interventional ATR method (CIATR), specifically designed to address the challenges posed by limited synthetic aperture radar (SAR) data. This approach is key in revealing the underlying causal relationships among essential factors in ATR, enabling us to achieve the desired causal effect without altering the imaging conditions (ICs). To address the challenges in SAR ATR with limited data, we developed a structural causal model (SCM) based on causal inference principles. This model helps identify how ICs, as confounders, induce spurious correlations between SAR images and their classifications, which can be solved by standard backdoor adjustment. Our implementation of backdoor adjustment begins with data augmentation, employing a spatial-frequency domain hybrid transformation. This step is crucial in estimating the potential effects of varied ICs on SAR images. Following this, a feature discrimination strategy is introduced to incorporate a hybrid similarity measurement. This technique is essential for assessing and mitigating the impact of changing ICs on the features extracted from SAR images, focusing on both structural and vector angle influences. The CIATR method effectively uncovers the true causal relationships between SAR images and their classes, even with limited data. Tested on MSTAR and OpenSARship datasets, our method shows promising performance in limited data scenarios, achieving 75.05% for ten-way five shots.

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.

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.

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.

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

Marine surveillance radar plays an important role in marine environment monitoring, however, its detection performance is often affected by sea clutter. In this paper, we consider the sea clutter suppression process as the mapping from clutter radar data domain to clutter-free radar data domain, and propose a new sea clutter suppression method based on clutter cancellation generative adversarial network (CCGAN). The proposed CCGAN contains sea clutter suppression generator (SCSG) and clutter-free domain discriminator (CFDD). With the proposed network, the clutter suppression result can be obtained. To ensure the target imformation is not affected while sea clutter is suppressed, the proposed method introduces target consistency loss in addition to adversarial loss during the training process. Experimental results have shown the proposed method can achieve excellent clutter suppression performance.

In the problem of ship detection in complex scenes, in addition to the characteristics of ship targets, there is rich semantic information in the global and local background of the whole scenes, which provides more valuable inference information for ship detection. Therefore, in this paper, we propose a ship detection method in complex scenes considering both global and local information perception for SAR images. Firstly, the proposed method detects the globally stable region and the locally significant region respectively, and then designs a judgment method combining the two to eliminate false alarms, so as to ensure that the detected target has both globally stable characteristics and locally significant characteristics. The detection performance of the proposed method is verified by the spaceborne SAR images covering the coastal areas. The result shows that the proposed method can effectively detect ships in complex scenes, especially eliminating most false alarms in land areas.

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

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.

Automatic target recognition (ATR) is the key of synthetic aperture radar (SAR) image interpretation. Due to the superior feature extraction and target classification capabilities, deep learning has been widely used in SAR ATR fields. Most of state-of-the-art SAR ATR methods are proposed for single-view input, however, multi-view SAR images include more abundant classification features. In order to improve the SAR ATR performance, it is necessary to carry out an effective method to extract and discriminate useful features from multi-view SAR images. In this paper, we propose a new SAR ATR method based on multi-view feature extraction and discrimination network, which includes two main components: multi-view feature extraction and multi-view feature discrimination. Multi-view features can be effectively extracted from input SAR images with the feature extraction component. After that, the extracted features are fed into the multi-view feature discrimination component, which aims to gather the features of the same class and separate the features of different classes. Therefore, the proposed method can achieve good multi-view SAR target recognition performance. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the effectiveness of our 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.

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

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.

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.

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.

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.

In the field of modern electronic reconnaissance, due to the complex electromagnetic environment and the denser pulse stream, multiple radar signals will be received simultaneously. These radar signals overlap in the time domain and frequency domain, which makes modulation recognition difficult, especially under low signal-to-noise ratio (SNR). This paper proposes a Squeeze-and-Excitation InceptionNet with an adaptive threshold (SE-IncepAtNet) to deal with the above problem. The network includes an Inception block to extract features of different receptive fields and reduces the influence of noise through a Squeeze-and-Excitation (SE) block. An adaptive threshold block is used to provide adaptive thresholds, avoiding the difficulty of threshold selection in multi-classification tasks. The simulation of five typical radar signals shows that the proposed method is robust and effective.

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.

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.

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.

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

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

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

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

Multistatic synthetic aperture radar (SAR) has the characteristics of all-day, all-weather and high-resolution imaging. It can observe a target from different directions simultaneously to obtain multi-angle observation information. However, jamming signals can affect multistatic SAR. When multiple range-deception jammers exist in the environment, multiple false targets are generated in the multistatic SAR image simultaneously, which can impact the readability of the information contained in multistatic SAR images. The locations of false targets are related to the configuration of multistatic SAR, it provides the potential for jamming suppression by adjusting the configuration. Thus, in this paper, we propose a jammer localization and jamming suppression method for multistatic SAR in a multi-jammer environment via configuration optimization. Firstly, a target detection algorithm and a discriminant algorithm are combined to detect and identify false targets. Then, the distribution law of false targets is analyzed, and false targets are classified into two categories according to the types of jammers. Combined with the multistatic SAR configuration and false target information, localization methods for range-deception jammers with different time delays are proposed. Finally, we model the configuration optimization problem as a multi-objective optimization problem (MOP), and the nondominated sorting genetic algorithm II is employed to solve the MOP. As a result, the configuration distribution of multistatic SAR can be altered to exclude false targets from the region of interest, thereby obtaining a multistatic SAR image without false targets. Simulation results demonstrate that the proposed method is effective.

Multistatic SAR is able to observe targets from different angles simultaneously, which enhances the information acquiring capability. However, multistatic SAR can still be affected by electromagnetic jamming, resulting in the misinterpretation of multistatic SAR images. This article proposes a method to locate multiple range-deception jammers and suppress jamming signals. First, the echo model of multistatic SAR under a multijammer environment is established. Second, the detection of interested targets in multistatic SAR images can be achieved through visual saliency detection methods based on spectral residual. Third, location distribution features of false targets in multistatic SAR images are analyzed, and the Euclidean distance criteria are used to effectively distinguish false targets. Accurate localization is then achieved by combing multistatic SAR configuration information. Finally, using a linear constrained minimum variance beamforming algorithm to suppress jamming signals, multistatic SAR images without jamming signals can be obtained. Simulation results validate the effectiveness of the proposed method in this article.

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).

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.

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.

Multistatic SAR can obtain information from different angles simultaneously, it has application potential in many fields. However, it still be affected by jammers. When deceptive jammers jamming the SAR system, the false target is generated in the SAR image. In this paper, when the false target is generated on the sea by jammer which located at the coast, the anti-jamming method is proposed. Firstly, echo model of multistatic SAR in deceptive jamming environment is established. Then, false targets are detected and recognized. Next, the jammer localization method is proposed according to relationship between the jammer and false targets. Finally, the jammer location is obtained and jamming signal suppression is achieved. Simulation results validate the effectiveness of the proposed method in this paper.

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.

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.

Synthetic aperture radar (SAR) has always received wide attention for its developing performance in military and civil applications. SAR automatic target recognition (ATR) is an important research field of the SAR application with the growing number and resolution of the SAR images. SAR images will be greatly influenced by the imaging azimuth, which could also be utilized to extract the correlation features between the adjacent azimuths. In this paper, we proposed a multi-view convolutional neural network and long short term memory (CNN-LSTM) network to extract and fuse the feature extracted from different adjacent azimuths. It adopts the structure of convolutional neural network to extract the optimal feature from the SAR images. Then, the structure of multiple layers of the long short term memory is adopted to fuse the optimal features of adjacent azimuths. Finally, a softmax is employed as the classifier to get the recognition results. Experimental results based on the MSTAR data set have shown the effectiveness and accuracy of 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.

In SAR image target detection, harbor detection can help the detection of harbor targets and maritime traffic planning. In this paper, we propose a harbor detection method of SAR images based on multidirectional one-dimensional scanning. Take the candidate points along the coastline and the multidirectional one-dimensional scanning is performed. Using the distribution characteristics of land, sea and dock in the one-dimensional vector, training a convolutional neural network to classify the candidate points into harbor and non-harbor feature points. Then we get the harbor feature points map reflecting the distribution of harbors. The Sentinel-1 spaceborne SAR images covering a coastal region are used to verify the proposed method. The experimental results show the effectiveness and accuracy 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.

Finding a suitable parking position often leads to much traffic pressure and time consumption in a busy parking lot. An auxiliary parking method based on automotive millimeter wave SAR is proposed in this paper. Firstly, Maximally Stable Extremal Region (MSER) method is utilized to extract the candidate regions occupied by parked vehicles from the millimeter wave SAR images. Then, in order to eliminate the false alarm candidate regions, we employ the morphological filter and utilize the centroid position to further refine the candidate regions. Thirdly, the difference in width-to-height ratio of the candidate regions is exploited to distinguish the parking directions of the cars. After that, the available parking spaces are located according to the parking direction. Finally, further remove the spaces occupied by obstacles, and plan reasonable parking routes. Experimental results based on measured data show that the proposed method has outstanding detection and parking route planning performance in different scenes.

Multistatic synthetic aperture radar (SAR) have achieved significant performance in the field of anti-jamming for its flexible configuration. Hence, its capacity of anti-jamming is very important in electronic warfare. In addition, it is difficult to remove the false targets caused by the deceptive jammer from SAR echoes. We propose a novel anti-deceptive jamming method for multistatic SAR in this paper which is able to locate the deceptive jammer and eliminate its influence. We first apply maximally stable extremal region (MSER) and European-distances-based method to detect the false targets. The position of the jammer can then be obtained by exploiting the position information of the false targets and the SAR transmitters/receivers. At last, beamforming is applied to achieve the anti-deceptive jamming. Experimental results demonstrate the effectiveness of our proposed method.

SAR ship detection is essential for marine monitoring. Due to the high similarity between the harbor and the ship body on gray and texture features, the traditional methods are unable to achieve effective inshore ship detection. An improved Faster R-CNN based on MSER decision criterion for SAR ship detection in harbor is proposed in this paper. It is a ship detection method based on the combination of feature-based method and pixel-based method. Firstly, Faster R-CNN is used to generate region proposals. Then, replace the threshold decision criterion of Faster R-CNN with the maximum stability extremal region (MSER) method to reassess the generated region proposals with higher scores, aiming at improving the detection rate and reducing the false alarm rate simultaneously. Experimental results based on satellite-borne SAR data illustrate that the proposed method obtains excellent detection performance and low false alarm rate.

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.

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.

Oil spill will cause severe ecological disasters and enormous marine environment damages. we consider a robust and fast oil spill candidate detection problem for oil spill recognition systems for synthetic aperture radar (SAR) imagery. In this paper, we propose a automatic detection method called thresholding-guided maximally stable extremal regions (T-GMSERs) algorithm. First, thresholding approach is utlized to learn model parameters automatically. Candidate regions are extracted by using the maximally stable extremal region (MSER) detector. Then, we label each candidate region to obtain a binary potential target pixel map. Finally, the detection results are acquired by maximal stable criteria from the corresponding region map. Simulation based on satellite-borne data illustrates that the proposed algorithm obtains more precise detection performance without increasing the computational complexity.

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

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.

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.

Synthetic aperture radar (SAR) has been widely used in remote sensing. Feature extraction is a crucial step in SAR automatic target recognition (ATR). In this paper, Kernel Marginal Sample Discriminant Embedding (KMSDE) is proposed, which is based on kernel trick and manifold learning theory. In feature extraction via KMSDE, the original dataset is mapped to high dimensional space and manifold learning theory is introduced for dimensional reduction. KMSDE preserves local and class information of the original dataset, as well as gathers the with-class samples and separates between-class samples in the low-dimensional space. In addition, it employs information related to each sample's location in the dataset, which enhances the discriminative capability of the method. Compared to other SAR imagery feature extraction methods, the experiments based on MSTAR database show that the proposed method improves the recognition performance.

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.

Target image database is of great significance in SAR automatic target recognition (ATR). Recently, some convenient and low cost approaches of database simulation were proposed. However, the similarity between virtual SAR images obtained by these simulation approaches and real SAR images is still under study. To solve this problem, we will model the virtual target with three-dimensional (3D) modeling methods, and acquire SAR image via the simulated RCS data which is generated by computational electromagnetic software. Then, we propose a method to measure the similarity between the virtual and real SAR images, which provided better support for data training and recognition of virtual target. Experiment results demonstrate the formation of the virtual SAR images and validate the effectiveness of our proposed method.

Geosynchronous synthetic aperture radar (GEO-SAR) offers new opportunities for continuous Earth observation missions with large coverage and short revisit cycle. The unique features of GEO-SAR present huge potentials for bistatic observation applications. In this paper, the concept and advantages of GEO bistatic SAR (GEO-BiSAR) are first investigated. The system consists of a GEO illuminator and an airborne receiver, such as an airplane or a near-space vehicle. Compared with a monostatic GEO-SAR system, the bistatic configuration can provide finer spatial resolution and higher signal-to-noise ration (SNR) with less system complexity. The spatial resolution characteristics are then analyzed based on generalized ambiguity function, where the time-varying GEO velocity, Earth rotation, and ellipsoid Earth surface are taken into consideration. Meanwhile, the bistatic SNR is analyzed using the integration equation model. In this paper, the mission design for GEO-BiSAR aims at identifying a set of receiver flight parameters and bistatic configurations to obtain the desired spatial resolution and SNR. Based on the desired imaging performance of a specific application background, the mission design process is modeled as a nonlinear equation system (NES). Finally, a mission design method based on fast nondominated sorting genetic algorithm is proposed to solve the NES and obtain multiple optimal solutions to guide the receiver flight missions. Examples of the mission design process are given to validate the effectiveness of the proposed method. The results of the mission design can be conveniently used to guide the receiver flight mission for the desired imaging performance, which is highly desirable in practical applications.

Feature extraction is a key step in synthetic-aperture-radar automatic target recognition. In this letter, we propose a novel feature extraction method named sample discriminant analysis (SDA) that is based on the manifold learning theory. The method directly extracts features from 2-D image matrices rather than vectors. Furthermore, SDA preserves the neighborhood information of the original data in dimension reduction. It also makes within-class samples closer and makes between-class samples father away in a low-dimensional space. Meanwhile, a sample discriminant coefficient is employed in the method to give each sample a weight related to its location and similarity to neighboring samples. Thus, the discriminative ability of the method is improved. Experimental results based on the moving and stationary target acquisition and recognition database show that the proposed method can improve recognition performance.

Feature extraction is a key step in synthetic aperture radar (SAR) automatic target recognition (ATR). In this paper, we propose a feature extraction algorithm based on manifold learning theory, the algorithm is named Two-dimensional Principal Component Analysis-based Two-dimensional Marginal Sample Discriminant Embedding (2DPCA-based 2DMSDE). Above all, the original SAR images are projected by 2DPCA which is effective for feature representation, the dimension of SAR images is reduced in horizontal direction and global information of the original dataset is preserved. Furthermore, 2DMSDE is employed to reduce dimension in vertical direction , preserve local information of the dataset and enhance discriminative ability. Therefore, 2DPCA-based 2DMSDE not only further compresses the dimensions of original images, but also achieves better recognition performance. Experimental results demonstrate the effectiveness of 2DPCA-based 2DMSDE.