Synthetic Aperture Radar Target Research Articles

The Latest Development of Synthetic Aperture Radar: An Overview

Abstract. Synthetic Aperture Radar (SAR) technology enables high-resolution imaging regardless of weather conditions, offering immense potential for target detection and recognition. This paper reviews the latest advancements in SAR technology, highlighting key developments and applications. The limitations of traditional SAR imaging techniques, particularly the trade-off between resolution and swath width, have been addressed by High Resolution Wide Swath (HRWS) SAR, enabling simultaneous acquisition of high-resolution and wide-swath data. Multi-polarimetric SAR provides richer scattering information for improved target recognition accuracy, while deep learning algorithms enhance image quality and recognition performance. Emerging technologies like 3D-ISAR and holographic imaging offer novel approaches to target recognition and tracking. We discuss the development history, performance advantages, and limitations of these technologies, providing a comprehensive overview of their practical applications. We identify current challenges and future trends in SAR target imaging and recognition, including computational efficiency, adversarial attack defense, data acquisition and processing, and feature extraction and classification. Potential research directions, such as integrated multi-mode SAR systems, multi-source data fusion, and adversarial attack defense strategies, are outlined to fully harness the potential of SAR technology in target imaging and recognition.

Attention Heat Map-Based Black-Box Local Adversarial Attack for Synthetic Aperture Radar Target Recognition

Synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNNs) are susceptible to adversarial attacks. In this study, we proposed an SAR black-box local adversarial attack algorithm named attention heat map- based black-box local adversarial attack (AH-BLAA). First, we designed an attention heat map extraction module combined with the layer-wise relevance propagation (LRP) algorithm to obtain the high concerning areas of the SAR-ATR models. Then, to gener- ate SAR adversarial attack examples, we designed a perturbation generator module, introducing the structural dissimilarity (DSSIM) metric in the loss function to limit image distortion and the dif- ferential evolution (DE) algorithm to search for optimal perturba- tions. Experimental results on the MSTAR and FUSAR-Ship datasets showed that compared with existing adversarial attack algorithms, the attack success rate of the AH-BLAA algorithm increased by 0.63% to 33.59% and 1.05% to 17.65%, respectively. Moreover, the low- est perturbation ratios reached 0.23% and 0.13%, respectively.

A Ghost and Attention Mechanism-Based Deep Learning Approach for SAR Small Target Image Detection

Sy nthetic aperture radar (SAR) images play an important role in the task of small target imaging and detection. However, due to its blurred target imaging, complex image background, and challenging feature representation, SAR detection is difficult to get high precision in a complex imaging environment. Therefore, by integrating the ghost model and the Attention-Mechanism (AM), this paper presents a new feature network model based on the improved YOLOv5s for detecting small targets in SAR images efficiently. The key is to design a Ghost_AM module by integrating the ghost bottleneck with the simple AM. This Ghost_AM module is introduced into to the YOLOv5s’ network for improving the YOLOv5s’ extraction performance for small target’s feature from a complex SAR image. Furthermore, to improve the YOLOv5s’ recognition ability for the features extracted from the Ghost_AM, a bidirectional feature pyramid network is use to replace the YOLOv5s’ feature fusion network and get a precise detection. The experiments using some SAR ship image datasets have demonstrated the presented model is better than some traditional SAR target detection algorithms and the original YOLOv5s. Moreover, the presented model’s parameters are reduced by 25.1% compared with the original YOLOv5s, which contributes to its simpler implementation.

Enhanced Prototypical Network with Customized Region-Aware Convolution for Few-Shot SAR ATR

With the prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning-based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning-based methods can achieve outstanding recognition performance on the condition that an abundance of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Therefore, SAR target recognition in the situation of few-shot, where only a scarce few labeled samples are available, is a fundamental problem that needs to be solved. In this paper, a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) is put forward to specially tackle the few-shot SAR ATR tasks. To be specific, a feature-extraction network based on a customized and region-aware convolution is first developed. This network can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, an enhanced prototypical network is proposed. This network can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset, the OpenSARShip dataset, and the SAMPLE+ dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition

Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, thereby necessitating an open set recognition (OSR) challenge for SAR-ATR. The crux of OSR lies in establishing distinct decision boundaries between known and unknown classes to mitigate confusion among different classes. To address this issue, we introduce a novel framework termed reinforced class separability for SAR target open set recognition (RCS-OSR), which focuses on optimizing prototype distribution and enhancing the discriminability of features. First, to capture discriminative features, a cross-modal causal features enhancement module (CMCFE) is proposed to strengthen the expression of causal regions. Subsequently, regularized intra-class compactness loss (RIC-Loss) and intra-class relationship aware consistency loss (IRC-Loss) are devised to optimize the embedding space. In conjunction with joint supervised training using cross-entropy loss, RCS-OSR can effectively reduce empirical classification risk and open space risk simultaneously. Moreover, a class-aware OSR classifier with adaptive thresholding is designed to leverage the differences between different classes. Consequently, our method can construct distinct decision boundaries between known and unknown classes to simultaneously classify known classes and identify unknown classes in open scenarios. Extensive experiments conducted on the MSTAR dataset demonstrate the effectiveness and superiority of our method in various OSR tasks.

Open-Set Recognition Model for SAR Target Based on Capsule Network with the KLD

Synthetic aperture radar (SAR) automatic target recognition (ATR) technology has seen significant advancements. Despite these advancements, the majority of research still operates under the closed-set assumption, wherein all test samples belong to classes seen during the training phase. In real-world applications, however, it is common to encounter targets not previously seen during training, posing a significant challenge to the existing methods. Ideally, an ATR system should not only accurately identify known target classes but also effectively reject those belonging to unknown classes, giving rise to the concept of open set recognition (OSR). To address this challenge, we propose a novel approach that leverages the unique capabilities of the Capsule Network and the Kullback-Leibler divergence (KLD) to distinguish unknown classes. This method begins by deeply mining the features of SAR targets using the Capsule Network and enhancing the separability between different features through a specially designed loss function. Subsequently, the KLD of features between a testing sample and the center of each known class is calculated. If the testing sample exhibits a significantly larger KLD compared to all known classes, it is classified as an unknown target. The experimental results of the SAR-ACD dataset demonstrate that our method can maintain a correct identification rate of over 95% for known classes while effectively recognizing unknown classes. Compared to existing techniques, our method exhibits significant improvements.

Attribute Feature Perturbation-Based Augmentation of SAR Target Data.

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.

SAR vehicle image generation with integrated deep imaging geometric information

Deep learning is widely applied in synthetic aperture radar (SAR) target recognition. However, the high cost of collecting real SAR target data leads to insufficient data volume and diversity of SAR target datasets, making it challenging to support deep learning models. Therefore, it is necessary to expand SAR target datasets through data generation. However, existing SAR target data generation methods do not fully utilize the SAR imaging geometric information of targets. This results in generating image data that does not conform to SAR physical imaging laws. To address this problem, this paper proposes a SAR vehicle data generation method that integrates deep imaging geometric information. Compared to SAR, optical data, which contains rich geometric structural information of targets, is relatively easy to collect. Therefore, the proposed method computes the dominant scattering edge (DSE) of targets under different radar incident angles with optical data. Then, based on generative adversarial networks (GAN), a SAR vehicle target data generation model is constructed. To guide the model in learning SAR imaging geometric laws of targets, the computed DSE is integrated into the generation network with multi-scale attention mechanism modules. Compared to existing SAR target data generation methods, our proposed method can generate SAR vehicle target data with distinct dominant scattering edges, and effectively addresses the issue of generated target data not conforming to SAR imaging physical laws. Metrics such as Fréchet Inception Distance (FID), scattering intensity difference, and Structural Similarity Index (SSIM) are adopted for evaluation. The SSIM between the generated and the real SAR target data reaches 0.834, the FID minimum of 96.98, and the scattering intensity difference minimum of 0.37 dB, all indicating that the SAR target data generated by the proposed method is similar to real ones in terms of deep feature, scattering intensity, and image structure. Finally, the SAR target data generated by the proposed method is verified through target recognition experiments with VGG16 and ResNet50 as backbone network. The average recognition accuracy increases by 2.80% and 2.14%, respectively. The target recognition results demonstrate that the generated SAR data can help improve target recognition accuracy, thereby validating the effectiveness of data generated by the proposed method.

GLRT‐based detection of targets composed of distributed scattering centres

AbstractAttributed scattering problems have been found to be helpful in inverse synthetic aperture radar (ISAR) imaging and target recognition problems. In this model, the scattering centres are divided into two categories: localised and distributed. Localised scattering centres are those that are concentrated in a small area, while distributed scattering centres are spread out over a larger area. Several methods have been proposed to estimate the scattering centres which aim to accurately identify the location and characteristics of the scattering centres. However, detecting a distributed scattering centre remains a challenging task. A novel technique is proposed based on sparse signals to improve the detection of distributed scattering centres from localised ones. This technique takes advantage of the sparsity of the signals to accurately identify the location of the distributed scattering centres. Experimental results demonstrate the superiority of algorithm in detecting distributed scattering centres. This improved detection capability has significant implications for ISAR imaging and target recognition problems.

Unveiling SAR target recognition networks: Adaptive Perturbation Interpretation for enhanced understanding

Deep neural networks (DNNs) have obtained remarkable achievements in various vision tasks. However, DNNs’ mechanism remains obscure, especially in synthetic aperture radar (SAR) target recognition. To clearly understand DNNs’ mechanism, some interpretation algorithms are proposed to provide a saliency map to visualize DNN’s decisions, which can be categorized into (1) propagation-based methods, (2) activation-based methods, and (3) perturbation-based methods. Unfortunately, each of them has some imperfection in producing saliency maps. The saliency maps produced by propagation-based methods are sensitive to SAR speckles. Activation-based methods have difficulty producing a saliency map where the highlighted region covers the entire feature. Perturbation-based methods tend to forfeit intricate details of the target in saliency map during optimization. To mitigate the above limitations, this paper proposed Adaptive Perturbation Interpretation (API), with two aims: (1) to improve the interpretability of SAR recognition networks and (2) to recover the fine-grained lost during the optimization procedures of perturbation-based methods. Specifically, we first introduce a Multi-information Perturbation Optimization Module (MPOM) to retain the target’s local characteristics while constraining saliency map’s sparsity by configuring regularization terms. Additionally, we further introduce a Feature Compensation Module (FCM) to recover the fine-grained feature lost during the optimization process in MPOM. Extensive experiments are implemented on SAR interpretation benchmark datasets, MSTAR and SARAIRcraft1.0, the results demonstrate the superiority of API to other state-of-the-art methods.

Integrated Multi-Scale Aircraft Detection and Recognition with Scattering Point Intensity Adaptiveness in Complex Background Clutter SAR Images

Detecting aircraft targets in Synthetic Aperture Radar (SAR) images is critical for military and civilian applications. However, due to SAR’s special imaging mechanism, aircraft targets often consist of scattering points with large fluctuations in intensity. This often leads to the detector failing to detect weak scattering points. Not only that, previous SAR image aircraft-object-detection models have focused more on detecting and locating targets, with little emphasis on target recognition. This paper proposes a scattering-point-intensity-adaptive detection and recognition network (SADRN). In order to correctly detect the target area, we propose a Self-adaptive Bell-shaped Kernel (SBK) within the detector, which constructs a bell-shaped two-dimensional distribution centered on the target center, making the detection “threshold” for the target decrease from the center towards the periphery, reducing the missed alarms of weak scattering points at the edges of the target. To help the model adapt to multi-scale targets, we propose the FADLA-34 backbone network, aggregating information from feature maps across different scales. We also embed CBAM into the detector, which enhances the attention to the target area in the spatial dimension and strengthens the extraction of useful features in the channel dimension, reducing interference from the complex background clutter on object detection. Furthermore, to integrate detection and recognition, we introduce the multi-task head, which utilizes the three feature maps from the backbone network to generate the detection boxes and categories of the targets. Finally, the SADRN achieves superior detection and recognition performance on the SAR-AIRcraft-1.0, exceeding other mainstream methods. Visualization and analysis further confirm the effectiveness and superiority of the SADRN.

CFAR Target Detector in Synthetic Aperture Radar

Introduction. Constant false alarm rate (CFAR) detectors have found application in synthetic aperture radar (SAR) systems. The operating principle of a classic cell averaging detector (CA-CFAR detector) is based on comparing the decision statistics in the test resolution element with an adaptive threshold, which is calculated from signals in the reference cells. The decision statistic is an estimate of the signal power. Therefore, target signal detection is based on the brightness contrast of the test and reference resolution cells. Such a detector is optimal provided that the noise background is homogeneous. In cases where the background homogeneity is violated, the quality of detection deteriorates. There are several known methods for improving the quality of detection (GO-CFAR, SO-CFAR, OS-CFAR, etc.). However, the precise principle of detection by brightness contrast in such CFAR detectors remains unchanged.Aim. To synthesize a CFAR detector that uses not only the brightness contrast between the test and reference resolution cells, but also the spectral differences of the signals.Materials and methods. The proposed CFAR detector uses estimates of the algebraic moments of the power spectral density of signals in range cells, based on which three decision statistics are calculated containing information about the power, the position of the energy center, and the width of the signal spectrum. The decision about the presence of a target in the test cell is carried out according to the 2/3 rule (2 threshold overshoots out of 3 comparisons).Results. A comparison of the proposed detector with the SO-CFAR detector, performed by computer simulation, showed that, under a signal-to-clutter ratio of -6 dB and a false alarm probability of 10-4, the detection probability of the proposed detector was 0.933 versus 0.708 for the SO-CFAR detector.Conclusion. The article proposes a three-parameter CFAR detector for a synthetic aperture radar system, in which the decision on the presence of a target in the test cell is made via estimation of the first three algebraic moments of the signal spectrum. The synthesized detection algorithm can also be used when detecting moving targets in SAR.

Spaceborne Synthetic Aperture Radar Aerial Moving Target Detection Based on Two-Dimensional Velocity Search

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified.

Application of adaptive fusion to attributed scattering centre-based reconstruction for SAR target recognition

ABSTRACT Synthetic aperture radar (SAR) measures backscattering characteristics of man-made targets and has the advantage of not being affected by weather and time. For target recognition in SAR images, the target reconstruction results based on attribute scattering centres are introduced for decision fusion. In comparison with original images, the reconstructed targets better relieve corruptions caused by noises, clutters, etc. A deep learning model, i.e. ResNet, is adopted as a basic classifier to classify both original and reconstructed images. According to the energy relationship between the reconstruction target and residual, the noise level of the original SAR image is defined. Then, the adaptive weights of original and reconstructed images are determined and a weighted decision fusion process is conducted to combine decisions from both images to confirm the target label. The proposed method is tested based on MSTAR dataset, and experimental results show its effectiveness.

SAR target recognition through adaptive kernel sparse representation model based on local contrast perception

Sparse representation-based methods have shown promising prospects in synthetic aperture radar (SAR) target recognition due to their robustness under complex application conditions. However, these methods are still susceptible to the feature representation of SAR images. In this paper, we propose a new SAR target recognition method using an adaptive kernel sparse representation model based on local contrast perception (LCP). The LCP method combines a specially designed random projection matrix with two-dimensional filtering to capture extensive intensity contrasts between local regions in SAR images. A response map reconstruction technique shows that LCP can obtain discriminative properties between target and background regions and improve feature representation. The LCP features are then transformed into the kernel representation to improve linear separability by defining a reproducing kernel Hilbert space (RKHS) and using a nonlinear kernel function. The kernel representation is utilized to construct a new kernel sparse representation model for classification. Furthermore, neighborhood analysis (NA) is proposed to establish the relationship between the parameter and feature distribution, with an adaptive regularization parameter selection strategy to mitigate the parameter sensitivity. Experiments conducted on the moving and stationary target acquisition and recognition dataset demonstrate the superiority and robustness of the proposed method under different application conditions.

SAR Target Recognition Method based on Adaptive Weighted Decision Fusion of Deep Features

Background: This paper proposes a synthetic aperture radar (SAR) target recognition method based on adaptive weighted decision fusion of multi-level deep features. Methods: The trained ResNet-18 is employed to extract multi-level deep features from SAR images. Afterwards, based on the joint sparse representation (JSR) model, the multi-level deep features are represented to obtain the corresponding reconstruction error vectors. Considering the differences in the abilities of different levels of features to distinguish the target, the reconstruction error vectors are analyzed based on entropy theory, and their corresponding weights are adaptively obtained. Finally, the fused reconstruction error result is obtained through adaptively weighted fusion, and the target label is determined accordingly. Results: Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under different conditions, and the proposed method is compared with published methods, including multi-feature decision fusion, JSR-based decision fusion and other types of ResNets. Conclusion: The experimental results under standard operating condition (SOC) and extended operating conditions (EOCs) including depression angle variance and noise corruption validate the advantages of the proposed method.

A Broadband Information Metasurface-Assisted Target Jamming System for Synthetic Aperture Radar

In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system’s ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas.

Recognition for SAR deformation military target from a new MiniSAR dataset using multi-view joint transformer approach

Accurately detecting ground armored weapons is crucial for achieving initiative advantages in military operations. Generally, satellite or airborne synthetic aperture radar (SAR) systems face limitations due to their revisit cycles and fixed flight trajectories, resulting in single-view imaging of targets, thereby hampering the recognition of small SAR ground targets. In contrast, MiniSAR possesses the capability to capture the multi-view of a target by acquiring images from different azimuth angles. In this research, our team utilizes a self-developed MiniSAR system to generate multi-view SAR images of real ground armored targets and recognize targets. However, the recognition of small targets in SAR images encounters two significant difficulties. First, small targets in SAR images are prone to interference from background noise. Second, SAR target deformation arises from variations in depression angles and imaging processes. To tackle these difficulties, this paper proposes a novel SAR ground deformation target recognition approach based on a joint multi-view transformer model. The method first preprocesses SAR images based on a low-frequency priori SAR image denoising method. Next, it obtains multi-view joint information through a self-attentive mechanism, inputs joint features to the transformer structure. The outputs are jointly updated by a multi-way averaging adaptive loss function to improve the recognition accuracy of deformed targets. The experimental results demonstrate the superiority of the proposed method in SAR ground deformation target recognition, outperforming other representative approaches such as information fusion of target and shadow (IFTS) and Vision Transformer (ViT). It is concluded that the proposed method has high recognition accuracies of 98.37% and 93.86 % on the moving and stationary target acquisition and recognition (Mstar) and our SAR images dataset, respectively, in the field of SAR ground deformation target recognition. We have included links to the code and data in the abstract of this paper for ease of access. The source code and sample dataset are available at https://github.com/Lvjiming/MJT.

Synthetic aperture radar target recognition based on adaptive decision fusion of multiple views

Synthetic aperture radar target recognition based on adaptive decision fusion of multiple views

A 3-D scattering centre model-based SAR target recognition method using multi-level region matching

ABSTRACT A 3-D scattering centre model-based synthetic aperture radar (SAR) automatic target recognition (ATR) method is proposed. Multi-level dominant scattering areas (DSAs) are generated from the test sample and model predicted target images, which can describe the target region and scattering centres distribution from coarse to fine. The DSA matching is conducted at each level using the morphological erosion operation and Euclidean distance transform. Finally, the energy of the region residuals is taken as the distance measure between the test sample and different target models so as to confirm the target label. The 3-D scattering centre model can provide very complete descriptions of the target. In addition, the designed distance measure comprehensively considers the possible nonlinear transformations between the model and test image using the morphological erosion operation and Euclidean distance transform. Experiments are performed on the electromagnetic (EM) simulated data of three military targets.