Inverse synthetic aperture radar (ISAR) image quality is significantly affected by the structural characteristics of the observed target, the accuracy of motion compensation and the sparsity of echoes. Because these factors interact in a complicated manner, a standardized and objective method for ISAR image quality evaluation remains underdeveloped. To address this challenge, we propose an intelligent evaluation method using a two-stream attention network to automatically grade image quality without reference images. The network comprises two branches: an image stream with a high-low frequency attention module that captures global perceptual cues, and a saliency stream with a multi-scale attention module leveraging saliency priors to extract local features from multiple scales in both spatial and channel dimensions. A weighted loss function allows the saliency stream to guide and refine the image stream’s learning process. For network training, a dataset comprising ISAR images with varying structural characteristics and quality levels derived from differences in noise, motion compensation accuracy, and echo sparsity is constructed. Experimental validation on simulated electromagnetic data confirms the effectiveness of the proposed method.

Spaceborne Synthetic Aperture Radar (SAR) working mode recognition is crucial for intention perception against SAR, threat level evaluation, and subsequently guiding the electronic jamming system to protect our key regions. However, as spaceborne SAR develops towards flexibility and intelligence, the agility of its waveforms and beams, as well as the overlap of parameters in different working modes, have posed significant challenges for the rapid and high-accuracy recognition of spaceborne SAR working modes. To improve the ability of spaceborne SAR working mode recognition in complex electromagnetic environments, this article proposes a spaceborne SAR working mode recognition method based on the Multi-scale Transformer (MFormer) network assisted by SAR semantic information. First, this method introduces SAR semantic information into the model input to mitigate the influence of outliers on recognition performance. Second, a multi-scale network is designed to sequentially input tokens of different scales into different layers of the Transformer encoder, fusing multi-scale information and enhancing the network’s ability to extract sequence features. Our experiments demonstrate the effectiveness, robustness, and generalization of this method in spaceborne SAR working mode recognition. In the non-ideal case of missing pulses and spurious pulses, our method outperforms other methods in terms of accuracy, macro Precision, macro Recall, and macro F1-score. Our method maintains over 80% recognition accuracy under conditions with 20% spurious and missing pulses.

Automatic modulation recognition (AMR) is essential for ensuring the physical-layer security for Internet of things (IoT) networks. Despite advancements in deep learning, most current AMR methods rely heavily on a large number of labeled samples to achieve high recognition accuracy. However, acquiring labeled samples can be costly and impractical in many real-world scenarios due to privacy concerns and economic constraints. In contrast, unlabeled data is often abundant and readily available. This paper presents a novel semi-supervised AMR framework that addresses the challenge of label scarcity by leveraging semantic mask contrastive learning (SMCL). Through a self-supervised modulation semantic mask contrastive prediction task within IQ sequence, our method learns subtle modulation features directly from unlabeled radio signals. It is important to note that SMCL requires neither data augmentation nor representation domain transformation. Sufficient experiments on public datasets have demonstrated our method outperforms existing semi-supervised and supervised methods when using the same number of labeled samples. SMCL effectively enables the representation learning of unlabeled radio signals, overcoming the limitations posed by the lack of sufficient labeled data and providing a solid technical foundation for the development of signal-based IoT large language models (IoT-LLMs).

Spatial-temporal beam spoofing (STBS) poses a severe security threat to integrated sensing and communication (ISAC) systems by deliberately manipulating signal properties to fabricate deceptive target echoes, thereby undermining the sensing accuracy and evading the current security measures through the spatial masking and asynchronous injection. To combat this intelligent attack in ISAC, we proposes a beam consistency anomaly detection (BCAD) method, which establishes a physics-constrained verification procedure based on inherent propagation properties of legitimate signals. The proposed BCAD method systematically incorporates essential signal consistency requirements, such as array steering vector coherence, Doppler shift linearity, and phase progression consistency, into multivariate polynomial formulations. It then utilizes sum-of-squares relaxation to rigorously verify the global non-negativity of these polynomials across the entire parameter space encompassing angle-of-arrival, delay, and Doppler shift. This verification process confirms the physical legitimacy of the signal, and a negative result reveals violations of the transmission continuity constraint caused by STBS. Numeric results are presented to show the detection performance of the proposed BCAD method.

Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention Network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution.

Inverse synthetic aperture radar (ISAR) and optical image fusion aims to generate a composite image that simultaneously emphasizes the prominent contours of spacecraft from optical images and preserves the rich texture information inherent in ISAR images. However, the limited receptive fields of spatial-domain methods restrict their ability to capture global contextual dependencies among strong scattering points in ISAR images and to effectively integrate complementary optical features. To tackle this challenge, we propose a phase-guided cross-frequency integration module (PGCFIM), which exploits the intrinsic global modeling capability of the frequency domain and the semantic expressiveness of the phase spectrum. Specifically, a deep Fourier transform is employed to establish an image-wide receptive field for intra-domain global modeling. Subsequently, phase components are explicitly aggregated, and a gating mechanism is introduced to guide the integration of inter-domain long-range dependencies, enabling effective learning of complementary cross-modal representations. To eliminate reliance on hand-crafted fusion strategies, we design an end-to-end network, named PGCFINet. By jointly enhancing cross-domain interaction, frequency-domain global awareness, and explicit complementary feature integration, PGCFINet significantly strengthens cross-domain and cross-modal information interaction representation. Furthermore, to mitigate the current lack of ISAR and optical image datasets, we construct a new dataset comprising various spacecraft models, offering an alternative benchmark for evaluation. Extensive experiments demonstrate show that PGCFINet achieves superior performance than state-of-the-art methods in both qualitative and quantitative assessments. Moreover, PGCFINet is extended to infrared and visible image fusion, and the favorable results further validate its robust generalization ability. The codes of our fusion method and the dataset are forthcoming at https://github.com/WangZe0622/PGCFINet.

The primary objective of jamming strategy optimization is to ensure that a jammer timely finds an effective jamming strategy against the multifunction radar (MFR), thereby ensuring the safety of targets. Deep reinforcement learning (DRL) has been widely applied in solving the problem of jamming strategy optimization. However, the process still faces challenges such as low learning efficiency and a heavy memory burden. Therefore, we propose a fast jamming strategy optimization method with imperfect experience. Firstly, we model the radar countermeasure process as a Markov decision process (MDP), and formulate the jamming reward function by combining the jamming effectiveness and the jammer’s operational intent. Secondly, we design a novel hybrid jamming strategy choice module, which uses imperfect experience to improve the optimization efficiency of jamming strategy. Furthermore, to improve sample efficiency and reduce forgetting caused by small replay buffer, we respectively employ a mixed replay buffer strategy and a knowledge consolidation technique. Finally, extensive experiments demonstrate that under the guidance of imperfect experience, our proposed method achieves faster convergence speed and higher strategy accuracy compared with existing DRL-based methods.

In this paper, we explore the vulnerability of the physical uplink shared channel (PUSCH) to a new smart jamming attack in fifth generation (5G) new radio (NR), where an intelligent adversary first executes its attack by sniffing the downlink control information (DCI)-indicated resource scheduling information and then disrupts the PUSCH data transmission effectively and covertly by the precise jamming. To combat such kind of DCI sniffing based smart jamming (DCIS-SJ), we propose a novel method for effective DCIS-SJ suppression leveraging the DCI-scheduled subset identification and the PUSCH resource reconstruction. Our method fundamentally relies on the differences in the spatial domain feature under available control channel elements and resource block group granularities between legitimate users and the DCIS-SJ attacker, to selectively exclude unwanted elements while safeguarding the authenticity of the targeted transmissions. Numerical results evaluate and confirm the effectiveness of our method.

Class incremental learning (CIL) is the key to achieving adaptive vision intelligence, and one of the main streams for CIL is network expansion (NE). However, state-of-the-art (SOTA) methods usually suffer from feature diffusion, growing parameters, feature confusion, and classifier bias. In view of this, a novel dynamic structure dubbed as recurrent NE (RNE) is proposed by establishing connections among task experts. Specifically, the previous task experts transfer features sequentially through a shared module and the new task expert makes adjustments based on received features rather than reextracted ones, thereby focusing more on the key area and avoiding feature diffusion. Furthermore, the RNE is compressed by replacing additional task experts with lightened ones, in order to significantly reduce the number of parameters while keeping the performance almost unaltered. In addition, feature confusion is alleviated by a decoupled classifier and classifier bias is corrected by pseudo-feature generation. Extensive experiments on four widely adopted benchmark datasets, i.e., CIFAR-100, ImageNet-100, Food-101, and ImageNet-1K, have demonstrated that RNE achieves SOTA performance in both ordinary and challenging CIL settings.

Current remote sensing semantic segmentation methods are mostly built on the close-set assumption, meaning that the model can only recognize pre-defined categories that exist in the training set. However, in practical Earth observation, there are countless new categories, and manual annotation is impractical. To address this challenge, we first attempt to introduce training-free1open-vocabulary semantic segmentation (OVSS) into the remote sensing context. However, due to the sensitivity of remote sensing images to low-resolution features, distorted target shapes and ill-fitting boundaries are exhibited in the prediction mask. To tackle these issues, we propose a simple and universal upsampler, i.e. SimFeatUp, to restore lost spatial information of deep features. Specifically, SimFeatUp only needs to learn from a few unlabeled images, and can upsample arbitrary remote sensing image features. Furthermore, based on the observation of the abnormal response

Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting road graphs remains a persistent challenge, primarily due to the severe scarcity of labeled data. To address this limitation, we collect a global-scale satellite road graph extraction dataset, i.e. Global-Scale dataset. Specifically, the Global-Scale dataset is ∼ 20× larger than the largest existing public road extraction dataset and spans over 13,800 km2globally. Additionally, we develop a novel road graph extraction model, i.e. SAM-Road++, which adopts a node-guided resampling method to alleviate the mismatch issue between training and inference in SAM-Road [17], a pioneering state-of-the-art road graph extraction model. Furthermore, we propose a simple yet effective "extended-line" strategy in SAM-Road++ to mitigate the occlusion issue on the road. Extensive experiments demonstrate the validity of the collected Global-Scale dataset and the proposed SAM-Road++ method, particularly highlighting its superior predictive power in unseen regions. The dataset and code are available at https://github.com/earth-insights/samroadplus.

Automatic modulation recognition (AMR) is critical in modern wireless communication systems and cognitive radio applications. Deep learning (DL) methods have become main-stream for AMR, achieving remarkable performance on balanced datasets. However, practical scenarios commonly exhibit long-tailed distributions, where abundant samples exist for head modulation types but middle and tail types suffer from severe scarcity. Such imbalance biases standard DL models towards the head classes, significantly compromising their effectiveness on tail classes. Current signal balance strategies are limited by domain-specific knowledge and insufficient sample diversity. To address these challenges, this paper proposes DiffuMLR, a novel class-dependent label information attenuation diffusion model for long-tailed modulation recognition. Specifically, we propose a label decay mechanism within a diffusion probabilistic model, dynamically adjusting label contributions during the reverse denoising process. DiffuMLR ensures semantic invariance of tail class samples while enhancing sample diversity. Extensive experiments conducted on real-world and public datasets with varying imbalance factors demonstrate significant improvements in recognition accuracy under long-tailed conditions. DiffuMLR can seamlessly integrate with existing AMR architectures and enhance AMR model robustness in realistic wireless communication scenarios.

Existing deep learning (DL)-based AMR methods achieve impressive performance with abundant labeled signals. However, in non-cooperative scenarios, acquiring labeled signals is challenging, leaving a vast amount of unlabeled data underutilized by current DL based automatic modulation recognition (AMR) approaches. To address this challenge, we propose a semi-supervised AMR framework based on a modulation signal diffusion generative model (MSDGM). This framework follows a two-stage training strategy. In the first stage, unlike existing unsupervised paradigms that rely on proxy tasks or pseudo-labels, MSDGM learns the data distribution from unlabeled signals. In the second stage, MSDGM is frozen as a feature extractor, and a classifier is trained on a small set of labeled signals to achieve effective AMR. This decoupled training paradigm significantly reduces the dependence on label quantity, ensuring rapid generalization and robust recognition even with minimal labeled signals. Extensive experimental results demonstrate the superior performance of the proposed MSDGM-based framework under few labeled signal scenarios. Notably, at SNR>0dB with only 2 labeled signals for each class, the proposed method achieves accuracies exceeding 72% and 76% on the 11-modulation-type recognition tasks of RML2016.10A and RML2022, respectively, significantly outperforming existing supervised and semi-supervised AMR methods.

Because of the constraints of observation conditions, it is difficult to obtain a large amount of measured data for real targets in the inverse synthetic aperture radar (ISAR) system. Existing deep networks usually use the simulated data of random points for training, which will lead to the degradation of the imaging performance of measured data when the distribution of measured data is different from that of simulated data, i.e., poor generalization performance. A high-resolution ISAR imaging method based on Swin Transformer-based deep reinforcement learning (SwinRL) is proposed to address this problem. The 2-D alternating direction method of multipliers (ADMM) is modeled as a sequential decision problem in this method. The internal adjustable parameters are modeled as actions, and the Swin Transformer is used as the backbone network of the policy network and value network. The optimization of the actions, i.e., the internal adjustable parameters of the 2-D ADMM algorithm, is then guided through network training in a reinforcement learning framework. After that, the trained agent can automatically give optimal internal parameters according to different input data, and then well-focused imaging results can be obtained by executing a 2-D ADMM algorithm with optimal parameters. Finally, experimental results based on simulated and measured data show the performance priority of the proposed method compared to existing deep unrolling networks with fixed parameters.

Inverse synthetic aperture radar (ISAR) images and optical images exhibit a certain degree of complementarity due to their imaging in different microwave frequency bands. To enhance the representation capability of spacecraft structures and details, we propose a novel ISAR-and-optical image fusion method based on the features of multisubbands. First, given the different imaging planes of spacecraft in ISAR and optical images, we propose an ISAR-and-optical image registration method that combines keypoint detection and homography transformation, based on the obvious geometric features of the spacecraft. Second, the multiscale decomposition of the source images is achieved by the nonsubsampled shearlet transform (NSST). Subsequently, we propose a novel activity level measurement function based on brightness, contours, and textures to achieve the fusion of low-pass subbands. Simultaneously, the texture of high-pass subbands is effectively fused based on the parameter-adaptive dual-channel pulse-coupled neural network (PADCPCNN). Finally, the fused image is obtained by the inverse-NSST. Compared to the existing state-of-the-art fusion methods, the proposed method has a better performance in both qualitative and quantitative evaluations across multiple imaging instants for different satellite models.

The collaborated observation of multi-static ISAR can provide comprehensive information for air/space target classification. However, under limited sample conditions, the existing methods is challenged by effective information fusion and distinguishable features extraction. To tackle these issues, a few-shot multi-static ISAR target classification method, dubbed multi-level feature reconstruction (MLRF), is proposed in this article, which comprises two key components: inter-station feature reconstruction (ISFR) and inter-class feature reconstruction (ICFR). Specifically, ISFR facilitates complementary information mining and redundant information suppression by multi-static feature alignment and reconstruction, while ICFR enhances inter-class separability and intra-class compactness via multi-class feature interaction and reconstruction. Additionally, a specialized hybrid loss is designed to ensure desired outputs of the related modules. Experimental results on multi-static ISAR dataset of satellite targets demonstrate that the proposed method significantly improves the few-shot classification accuracy, and ablation studies and visual analysis further validate the effectiveness of each module.

Unknown image deformation and few-shot issues have posed significant challenges to inverse synthetic aperture radar (ISAR) target classification. To achieve robust feature representation and precise correlation modeling, this article proposes a novel two-stage few-shot ISAR classification network, dubbed as robust embedding and manifold inference (REMI). In the robust embedding stage, a multihead spatial transformation network (MH-STN) is designed to adjust unknown image deformations from multiple perspectives. Then, the grouped embedding network (GEN) integrates and compresses diverse information by grouped feature extraction, intermediate feature fusion, and global feature embedding. In the manifold inference stage, a masked Gaussian graph attention network (MG-GAT) is devised to capture the irregular manifold of samples in the embedding space. In particular, the node features are described by Gaussian distributions, with interactions guided by the masked attention mechanism. Experimental results on two ISAR datasets demonstrate that REMI significantly improves the performance of few-shot classification and exhibits robustness in various scenarios.

The Pulse Repetition Interval (PRI) parameter of a radar is closely related to the radar waveform, working mode, and system resource scheduling, et. al. Correctly identifying the PRI modulation type is of great significance for inferring the technical system, working platform, and behavioral state of the radar emitters. This paper proposes a PRI modulation recognition algorithm based on GAF-SE-CNN to address the problem of low recognition accuracy of existing algorithms under non-ideal conditions with high proportions of lost pulses and spurious pulses. This method first uses the gramian angle field (GAF) algorithm to process PRI sequences of different categories into two-dimensional images with greater feature differences. Secondly, the squeeze and excitation convolutional neural network (SE-CNN) model is constructed to make important features play a leading role in PRI modulation identification. Finally, the processed 2D images are input into the SE-CNN model for training and testing. The experimental results show that the proposed method has good robustness; even with lost pulse and spurious pulse ratios as high as 60%, the recognition accuracy is still very high.

In the passive bistatic radar (PBR) system, there exists methods used to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delayDoppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model results in a reduction of the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results using digital video broadcasting-terrestrial (DVB-T) signal indicate that the proposed approach enables fast and stable detection of weak targets.

Automatic Modulation Recognition (AMR) plays a pivotal role in modern mobile communications and the advancement of B5G and 6G technologies. However, the progressively intricate and hostile electromagnetic environments pose challenges to modulation recognition. While deep learning can solve complex problems, Digital Signal Processing (DSP) is interpretable and can be more computationally efficient. To combine both, we propose a Wavelet-based Adaptive modulation recognition Network (WAN) specifically designed for low SNR conditions. Diverging from traditional methods that preprocess signals prior to neural network input, our novel approach facilitates mutual synergy between DSP and the neural network during the training phase. We introduce two sub-blocks [Wavelet Threshold Estimate Block (WTEB), Selective Multi-scale Feature Extraction Block (SMFB)], which enable adaptive wavelet transform utilization for extracting multi-scale modulation features from recovery signals. WAN significantly enhances modulation recognition accuracy in low SNR while concurrently augmenting the interpretability of the neural network. Experimental results demonstrate that WAN outperforms SOTA methods in recognition accuracy.

Automatic modulation classification (AMC), as a key technology of cognitive radio, has become a focal point of research. However, most deep learning-based AMC methods require an extensive number of labeled signals to acquire a comprehensive understanding of modulation types, placing substantial pressure on signal acquisition and labeling. To solve this issue, we propose a few-shot AMC (FSAMC) method to facilitate rapid generalization and recognition with limited data, namely multi-scale feature fusion and distribution similarity network (MS2F-DS). Firstly, we design a multi-scale feature fusion (MS2F) model, which aims to extract features with varying fields of view and boost feature fusion, enabling the derivation of contextual information from the signal. Furthermore, we introduce a distribution similarity (DS) classifier to address the insufficient measurement of current similarity measurement functions by considering both micro and macro perspectives of vectors, further increasing intra-class compactness and inter-class separability. Finally, extensive experiments were conducted on 3-way 1, 3, and 5-shot FSAMC tasks using public datasets RML2016.10a and RML2016.10b, and the results demonstrated the effectiveness of our method.

Recently, significant progress has been made in deep learning, which has been widely applied in automatic modulation classification (AMC) with remarkable outcomes. However, current deep learning based AMC (DL-AMC) algorithms show limitations in their ability to accommodate dynamically changing communication scenarios. With the increasing number of modulation types, most DL-AMC algorithms often need to be re-trained, making it hard to transfer previous knowledge to the new models. Also, modulation classification faces the difficulty of acquiring and annotating a large number of signals. To address these challenges, we have modeled a few-shot class-incremental AMC (FSCI-AMC) task and proposed a pseudo classes and stochastic classifiers-based network (PASS-Net) to accomplish it. Firstly, the pseudo classes are generated to reserve space for new types, enhancing the model’s continuous learning capability. Additionally, stochastic classifiers ensure the reliability of generated pseudo classes. Finally, in the incremental session, both real and pseudo classes are used for modulation classification. To evaluate the proposed approach, experiments were conducted on 7 modulation types as base classes and another 7 modulation types as incremental classes. The results show superior performance in 7 sessions of 1-way 1-shot and 1-way 5-shot class-incremental experiments compared to other competitive methods.

With the emergence of novel and complex jamming types, jamming recognition as the primary step in radar anti-jamming is facing tremendous challenges. However, traditional methods experience significant difficulties in identifying increasingly complicated jamming types due to excessive manual dependence and inferior generalization performance. To alleviate the above challenges, we propose a novel recognition framework called Residual Attention Network (RA-Net). Specifically, we integrate channel and spatial attention to learn refined feature representations, which benefits the final recognition accuracy. To further optimize our proposed method, we introduce a polynomial loss to learn a robust feature space. Experimental results on simulated datasets with 19 types of jamming have demonstrated improvement of our proposed RA-Net over traditional methods.

The existence of Comb Spectrum Modulation Jamming (CSMJ) degrades the imaging quality severely, which hinders the performance of Synthetic Aperture Radar (SAR). The frequency domain-notched filtering is applicable in dealing with CSMJ but would introduce severe signal loss. In this paper, we propose a novel method for CSMJ suppression based on a dual-path residual network with the attention mechanism (DPRA-Net). We use the attention mechanism to build inter-dependencies among local and global features in the frequency domain for improving the suppression performance of DPAR-Net. Consequently, the CSMJ suppression problem is transformed into an end-to-end mapping problem, which minimizes signal loss. The validity of our algorithm has been verified on the measured data collected by Sentinel-1.

Narrowband and wideband waveforms are usually adopted simultaneously during the observation of micro-motion space targets by inverse synthetic aperture radar (ISAR), which can collect rich multimodal information in the time-Doppler, time-range, and range-instantaneous-Doppler domains. In order to exploit the electromagnetic scattering, shape, structure, and motion characteristics, this article proposes an attention-augmented cross-modal feature fusion recognition network, namely ACM-FR Net. Firstly, the ACM-FR Net adopts convolution neural network (CNN) to extract initial feature vectors from joint time-frequency (JTF) image, high resolution range profiles (HRRPs), and range-instantaneous-Doppler (RID) image, respectively. Then, it transforms the feature vectors of the three modalities into feature sequences. Finally, it achieves interactive feature fusion by implementing attention-augmented cross-modal feature fusion. In the four-category micro-motion space targets recognition experiments, the proposed ACM-FR Net has demonstrated high accuracy and noise robustness.

It is difficult to realize effective synthetic aperture radar (SAR) automatic target recognition (ATR) in open scenarios because the ATR model cannot continuously learn from new classes with limited training samples. When adding new classes to the previously trained model, the capability of recognizing old classes may lose due to severe overfitting. To tackle this problem, a few-shot class-incremental SAR ATR method, namely, hierarchical embedding and incremental evolutionary network (HEIEN), is proposed in this article. First, a hierarchical embedding network and a hybrid distance-based classifier are constructed for basic feature extraction and classification. Then, in order to obtain more accurate decision boundaries, an adaptive class-incremental learning (ACIL) module is designed to adjust the weights of classifiers in all tasks by collecting context information from the past to the present. Finally, a pseudo-incremental training strategy is designed to enable effective model training with only a few samples. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) benchmark data set have illustrated that HEIEN performs well with remarkable advantages in few-shot class-incremental SAR ATR tasks.

Restricted by observation conditions, some scarce targets in the synthetic aperture radar (SAR) image only have a few samples, making effective classification a challenging task. Although few-shot SAR target classification methods originated from meta-learning have made great breakthroughs recently, they only focus on object-level (global) feature extraction while ignoring part-level (local) features, resulting in degraded performance in fine-grained classification. To tackle this issue, a novel few-shot fine-grained classification framework, dubbed as HENC, is proposed in this article. In HENC, the hierarchical embedding network (HEN) is designed for the extraction of multi-scale features from both object-level and part-level. In addition, scale-channels are constructed to realize joint inference of multi-scale features. Moreover, it is observed that the existing meta-learning-based method only implicitly utilize the information of multiple base categories to construct the feature space of novel categories, resulting in scattered feature distribution and large deviation during novel center estimation. In view of this, the center calibration algorithm is proposed to explore the center information of base categories and explicitly calibrate the novel centers by dragging them closer to the real ones. Experimental results on two open benchmark datasets demonstrate that the HENC significantly improves the classification accuracy for SAR targets.

In the complex electromagnetic environment, the imaging quality of the synthetic aperture radar (SAR) will be severely degraded by the wideband interference (WBI). It is difficult to mitigate the WBI due to its wide frequency band. To address this problem, we propose a method based on synchroextracting transform (SET). First, a WBI-corrupted SAR echo is transformed into the time-frequency (TF) domain by the short-time Fourier transform (STFT). Then the WBI is detected in the TF domain by thresholding and a TF mask for the WBI is generated. Next, the synchroextracting operator (SEO) is calculated and the SET result is obtained. Finally, the WBI is reconstructed based on the SET result and is subtracted from the WBI-corrupted SAR echo. Experimental results with the measured WBI-corrupted SAR data demonstrate the effectiveness of the proposed method.

Scatterer trajectory association is a critical step of 3-D target reconstruction from the inverse synthetic aperture radar (ISAR) image sequence. To cope with the complex scatterer trajectory association of the noncooperative target, a novel method based on multiple hypothesis tracking (MHT) algorithm is proposed. First, the scatterer trajectory association situation is modeled as a multiple hypothesis tree, in which each branch represents a possible association. Then, to generate the hypothesis in each specific branch, a general trajectory motion model is constructed and the parameters are estimated based on the current trajectory association situation. The parameter estimation precision will increase with the growth of the trajectory length. Besides, to eliminate the influence of inaccurate association initialization and direction selection, a fusion algorithm is proposed to merge the forward and backward associated trajectories. Finally, experimental results based on the simulated data and electromagnetic data verify the effectiveness and robustness of the proposed method.

Estimation of the instantaneous attitude of spacecraft plays significant roles in space situation awareness activities, such as on-orbit status monitoring and collision warning. With the rapid development of both optical sensors and inverse synthetic aperture radar (ISAR), it becomes possible to achieve accurate instantaneous attitude estimation of spacecraft by multistatic optical-and-ISAR joint observation. In view of this, this article proposes a novel spacecraft attitude estimation method based on a joint optical-and-ISAR observation system, which includes one optical sensor and two ISARs. Specifically, the proposed method first estimates the orientation and the length of typical components utilizing optical and ISAR images with the same observation instant. Then, it solves the target instantaneous rotation vector from the orientation, length, and Doppler of typical components, and finally, it obtains the instantaneous attitude of the spacecraft. Experimental results have verified the effectiveness of the proposed method.

In complex electromagnetic environments, wideband interference (WBI) may severely affect the imaging quality of synthetic aperture radar (SAR). Because it occupies a large bandwidth, which overlaps with target echoes, the WBI is difficult to mitigate. The existing WBI suppression methods based on filtering or transform-domain analysis usually suffer from a model mismatch. To tackle this problem, a method combining instantaneous frequency (IF) estimation and regularized time-frequency filtering (RTFF) is proposed for WBI suppression and individual components extraction. First, the WBI-corrupted SAR echo is characterized in the time-frequency domain by short-time Fourier transform (STFT) with adaptive window width, determined by the proposed window width optimization method. Then, the IFs of the WBI components are estimated by ridge path detection and regrouping. Finally, the WBI is extracted by RTFF. Experimental results of measured SAR data corrupted by simulated and real WBIs have demonstrated the effectiveness and practicability of the proposed method.

In the current complex electromagnetic environment, the imaging quality of synthetic aperture radar (SAR) may be severely degraded by wideband interference (WBI), which occupies a wide frequency band overlapping with the frequency band of the SAR system, leading to great difficulties in WBI suppression. To address this problem, we propose a method based on the masked second-order multisynchrosqueezing transform (MSST2) to effectively extract WBI from corrupted SAR data. First, the SAR echo is characterized in the time-frequency (TF) domain by the short-time Fourier transform (STFT) with adaptive window width, and a TF mask for the WBI is generated. Then, the energy of the WBI is concentrated with high resolution by MSST2, and the TF mask is refined according to the MSST2 result. Finally, the WBI is accurately reconstructed from the MSST2 result masked by the refined TF mask and is subtracted from the SAR echo. Abundant experimental results for measured SAR data with simulated and real WBI demonstrate the superior performance and practicability of the proposed method.

To make full use of the sequential information obtained by continuous inverse synthetic aperture radar (ISAR) imaging, this article proposes a sequential ISAR target classification network based on hybrid transformer (HT). First, a temporal–spatial encoder based on the attention mechanism is designed to extract long-term and global features from sequential images. Meanwhile, a local feature encoder based on the 3-D convolution neural network is designed to extract short-term and local features. Then, the above two features are fused and the classification labels are obtained by a channel encoder–decoder. In 4-satellite target classification experiments, the proposed HT shows high accuracy and robustness to the unknown image scaling, rotation, and combined deformations.

To effectively classify inverse synthetic aperture radar (ISAR) sequential image with unknown deformation, a sequential adjustment ISAR image classification network (SAISAR-Net) is proposed, which first performs global and local image adjustments for each image frame and obtains deformation robust feature sequence. Then, the time-varying features are extracted by attention augmented bidirectional long short-term memory (Bi-LSTM), the output of which is weighted and fused to give a classification label. Compared with the existing deep learning methods, the proposed network significantly improves the classification accuracy and exhibits robustness in scenarios of scaled, rotated, combined transformation, and practical satellite orbit tests.

Restricted by the observation condition, synthetic aperture radar (SAR) automatic target classification based on deep learning usually suffers from insufficient training samples. To tackle this problem, a novel few-shot learning (FSL) framework for SAR target classification, i.e., the mixed loss graph attention network (MGA-Net), is proposed. The classification procedure of the MGA-Net consists of three main stages. In the first stage, the task set is expanded by the data augmentation module to increase diversity. In the second stage, the embedding network is designed to map samples to the embedding space with strong intra-class similarity and inter-class divergence. In the third stage, the multilayer graph attention network (GAT) is constructed and updated according to a novel mixed loss to obtain the classification result. In particular, the data augmentation module alleviates the desire of training samples under large model capacity and enhances the robustness to noise and viewing angle variation; the multilayer GAT accurately captures relations between samples by the attention mechanism; and the mixed loss increases the inter-class separability and accelerates convergence. Experimental results under various few-shot observation settings of the MSTAR and the OpenSARShip benchmark datasets demonstrate that the MGA-Net obtains higher accuracy than typical FSL methods and exhibits robustness to large depression angle variation.

In this paper, an end-to-end classification method for polarimetric synthetic aperture radar (PoISAR) images through complex-valued residual attention enhanced U-Net is proposed, which incorporates complex-valued operation to utilize phase information and residual attention modules to enhance discriminate features in multiple resolutions. Besides, the deep supervision strategy can not only enhance the ability to learn an effective representation for each scale, but also speed up and stabilize the training process. The experiments clearly demonstrate that our proposed method can achieve state-of-the-art performance compared with recently proposed approaches based on deep belief networks, autoencoders and convolutional neural networks.

We address the problem of high-resolution radar imaging in low signal-to-noise ratio (SNR) environments in an approximate Bayesian inference framework. First, the probabilistic graphical model is constructed by imposing the sparsity-promoting spike-and-slab prior to the distribution of scattering centers. Then, the model parameters and phase errors are estimated iteratively by expectation propagation (EP) and maximum likelihood (ML) estimation. Compared with the available imaging methods based on the numerical optimization and Bayesian inference, the proposed method has exhibited more flexibility in data representation and better performance in parameter estimation, particularly in sparse-aperture and low SNR scenarios.

This article addresses the problem of joint time–frequency (JTF) analysis of micromotion targets in complex environments in an approximate Bayesian inference framework. First, the sparse observation model is constructed, which is then decomposed into a series of single-window-JTF (SW-JTF) analysis problems to tackle the high dimension of the over-complete dictionary. On this basis, the probabilistic graphical model is constructed by imposing the Gamma-complex Gaussian prior to the JTF distribution. Finally, the model parameters are solved effectively by single-window variational inference (SWVI). Compared with the available methods, the proposed method could obtain better-focused JTF signature for narrowband data and higher quality range-instantaneous Doppler (RID) image for wideband data, especially in low signal-to-noise ratio (SNR) and data corruption scenarios.

Synthetic aperture radar (SAR) automatic target recognition (ATR) plays an important role in SAR image interpretation. However, at least hundreds of training samples are usually required for each target type in the existing SAR ATR algorithms. In this article, a novel few-shot learning framework named hybrid inference network (HIN) is proposed to tackle the problem of SAR target recognition with only a few training samples. The recognition procedure of HIN consists of two main stages. In the first stage, an embedding network is utilized to map the SAR images into an embedding space. In the second stage, a hybrid inference strategy that combines the inductive inference and the transductive inference is adopted to classify the samples in the embedding space. In the inductive inference section, each sample is recognized independently according to a metric based on Euclidean distance. In the transductive inference section, all samples are recognized as a whole according to their manifold structures by label propagation. Finally, in the hybrid inference section, the classification result is obtained by combining the above two inference methods. To train the framework more effectively, a novel loss function named enhanced hybrid loss is proposed to constrain samples to gain better interclass separability in the embedding space. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) benchmark data set illustrate that HIN performs well in few-shot SAR image classification.

Although numerous methods based on sequence image classification have improved the accuracy of synthetic-aperture radar (SAR) automatic target recognition, most of them only concentrate on the fusion of spatial features of multiple images and fail to fully utilize the temporal-varying features. In order to exploit the spatial and temporal features contained in the SAR image sequence simultaneously, this article proposes a sequence SAR target classification method based on the spatial-temporal ensemble convolutional network (STEC-Net). In the STEC-Net, the dilated 3-D convolution is first applied to extract the spatial-temporal features. Then, the features are gradually integrated hierarchically from local to global and represented as the united tensors. Finally, a compact connection is applied to obtain a lightweight classification network. Compared with the available methods, the STEC-Net achieves a higher accuracy (99.93%) in the moving and stationary target acquisition and recognition (MSTAR) data set and exhibits robustness to depression angle, configuration, and version variants.

To obtain high-resolution imaging while reducing the radar operating bandwidth under a low signal-to-noise ratio (SNR), this article proposes a genetic method for accurate residual radial motion estimation and well-focused imaging of the sparse stepped-frequency chirp signal (SSFCS). First, the signal model is constructed by incorporating the residual radial motion parameters into the dictionary. Then, high-quality high-resolution range profiles (HRRPs) are synthesized by Beta process regression (BPR), which has enhanced flexibility in data description and superior performance in parameter estimation. In addition, the genetic method updates the population, i.e., the candidates for the residual radial motion parameters, iteratively according to the image entropy to meet the required precision for well-focused imaging. Experimental results of Monte Carlo simulations and imaging results of simulated and measured data have demonstrated that the proposed method achieves more accurate estimation in residual radial motion parameters and better-focused imaging than the available methods.

This paper considers the anti-jamming optimization problem for tracking multiple moving target flight vehicles in the presence of deception jammers. Since the radar is not able to separate the real target vehicles from a large number of deceptive vehicles, we promote the existing non-anti-jamming tracking model to the anti-jamming partially observable Markov decision process-based (POMDP-based) game tracking model by establishing a new anti-jamming Bayesian tracker. The proposed tracker is able to separate the hidden real target vehicles and establish their accurate trajectories, but the limited radar resources will decrease the accuracy. In order to effectively utilize the limited resources to guarantee the anti-jamming performance, this work deduces the anti-jamming performance gradients with respect to the resource management policy, which can be estimated with the asymptotically vanished biases. With the gradient estimates, the optimal anti-jamming resource management policy can be found with the tolerable complexity. The convergence analysis shows that the algorithm converges to a Nash equilibrium solution with probability 1. Numerical results show that the proposed algorithm can obtain the accurate target trajectories in the presence of jammers.

In this paper, a deceptive jamming template generative adversarial network (DJTGAN) is proposed, which can intelligently generate high-fidelity deceptive jamming template matched with the practical SAR scenario. The DJTGAN consists of a deceptive jamming template generative network and a discriminative network. The generative network combines low-frequency content and high-frequency details of the target, and the discriminative network adopts PatchGAN architecture to capture local texture statistics to improve the fidelity of the deceptive jamming template. The MSTAR dataset is utilized to verify the effectiveness of the proposed DJTGAN. Moreover, the strip SAR deceptive jamming experiment based on the deceptive jamming templates generated by DJTGAN is done to further validate the effectiveness of the DJTGAN.

In this paper, a semi-supervised classification method for polarimetric synthetic aperture radar (PolSAR) images through multi-scale weighted graph convolutional network (MWGCN) is proposed, which utilizes the symmetric revised Wishart distance for weighted graph representation, graph integration module to incorporate the multi-scale information embedded in different hops, and superpixels to reduce the memory requirements and computation burden. The experiments clearly demonstrate that the proposed graph convolutional network (GCN) based method is suitable for PolSAR image interpretation in non-Euclidean domain and can achieve state-of-the-art classification results compared with recently proposed auto-encoder (AE) based and convolutional neural network (CNN) based methods.

By analyzing the motion characteristics and the radar observation model of triaxial stabilized space targets, a new 3-D geometry reconstruction method is proposed based on the energy accumulation of inverse synthetic aperture radar (ISAR) image sequence. According to the radar line of sight (LOS), we first construct the projection vectors of the 3-D geometry of a space target on the imaging planes. Then, by projecting the 3-D scatterer candidates on each imaging plane, we can accumulate the scattering energy of the corresponding 2-D projection position in each image. The 3-D scatterer candidates occupying the larger accumulated energy will be reserved as the real scatterers. To improve the efficiency, the real 3-D scatterers will be searched by using the particle swarm optimization (PSO) algorithm one by one. Compared with traditional 3-D geometry reconstruction methods, the proposed one never needs the 2-D scatterer extraction and trajectory association, which remains the challenges in ISAR image processing. Experimental results based on the simulated point target and electromagnetic data are presented to validate the effectiveness and robustness of the proposed method.

Radar echoes simulation has played a significant role in human detection and classification in the scenarios, e.g., antiterrorism, rescue after a disaster and medical, where the real-measured data are generally unavailable and limited. Therefore, a novel radar echoes simulation method of human movements is proposed based on motion capture (MOCAP) and electromagnetic (EM) calculation. First, we generate the trajectories of body segments from the true shape and MOCAP data of a human body. On the basis of that, the radar echoes are simulated by calculating the EM scattering characteristics, i.e., radar cross sections (RCSs) of all the gestures of each body segment's trajectory. Meanwhile, the micro-Doppler characteristics induced by the micromotion of human body segments are modulated in simulated radar echoes. Finally, comparisons between the simulated radar echoes and measured ones prove the validity of the proposed method. Some refinement for RCS calculation of the human body will be investigated in our future work.

This paper addresses the problem of radar-based human gait recognition based on the dual-channel deep convolutional neural network (DC-DCNN). To enrich the limited radar data set of human gaits and provide a benchmark for classifier training, evaluation, and comparison, it proposes an effective method for radar echo generation from the infrared, publicly accessible motion capture (MOCAP) data set. According to the different nonstationary characteristics of micro-Doppler (m-D) for the torso and limbs, it enhances their distinguishable joint time-frequency (JTF) features by applying the short-time Fourier transforms (SFTFs) with varying sliding window length and then designs the DC-DCNN structure to achieve refined human gait recognition by separate feature extraction and fusion. Experiments have shown that compared with the traditional single-channel deep convolutional neural network (SC-DCNN), the proposed method achieves higher recognition accuracy in refined human gait classification without incurring additional radar resources and could be readily extended to refined recognition of other human activities.

Although the deep convolutional neural network (DCNN) has been successfully applied to target classification of military vehicles based on synthetic aperture radar (SAR), most of the available methods do not fully exploit the characteristics of continuous SAR imaging and only utilize single image for recognition. To extract significant identification features contained in the image sequence, this paper proposes a sequence of SAR target classification method based on bidirectional convolution-recurrent network. In this network, we extract spatial features of each image through DCNNs without the fully connected layer, and then learn sequence features by bidirectional long short-term memory networks. Finally, we design the average softmax classifier to obtain the classification results. Compared with the available methods, the proposed network takes advantage of the significant information in the image sequence and achieves higher classification accuracy in the moving and stationary target acquisition and recognition data set. In addition, it has shown robustness to large depression angle variants, configuration variants, and version variants.

We address the problem of high-resolution radar imaging in complex environments in a Bayesian framework. We perform model order selection and sparse weights estimation via the maximum a posterior-expectation maximization technique in a statistical model, where the noise obeys Gaussian mixture distribution; and the weights are governed by the sparsity-promoting Gamma-Gaussian hierarchical prior. The proposed method has closed-form solution and can be implemented efficiently by matrix operation. Experiments has shown that accounting for the noise with Gaussian mixture model instead of single Gaussian greatly improves the performance, and the proposed method provides an effective way of high-resolution radar imaging in complex environments such as barrage jamming and micro-Doppler interference.

Although the deep convolutional neural network (DCNN) has been successfully applied to automatic target recognition (ATR) of ground vehicles based on synthetic aperture radar (SAR), most of the available techniques are not suitable for inverse synthetic aperture radar (ISAR) because they cannot tackle the inherent unknown deformation (e.g., translation, scaling, and rotation) among the training and test samples. To achieve robust polarimetric-ISAR (Pol-ISAR) ATR, this paper proposes the spatial transformer-multi-channel-deep convolutional neural network, i.e., ST-MC-DCNN. In this structure, we adopt the double-layer spatial transformer network (STN) module to adjust the image deformation of each polarimetric channel and then perform a robust hierarchical feature extraction by MC-DCNN. Finally, we carry out feature fusion in the concatenation layer and output the recognition result by the softmax classifier. The proposed network is end-to-end trainable and could learn the optimal deformation parameters automatically from training samples. For the fully Pol-ISAR image database generated from electromagnetic (EM) echoes of four satellites, the proposed structure achieves higher recognition accuracy than traditional DCNN and MC-DCNN. Additionally, it has shown robustness to image scaling, rotation, and combined deformation.

In this paper, we proposed a ship detection method based on deep convolutional neural networks for PolSAR images. The proposed ship detector firstly segments PolSAR images into sub-samples using a sliding window of fixed size to effectively extract translational-invariant spatial features. Further, the modified faster region based convolutional neural network (Faster-RCNN) method is utilized to realize ship detection for ships with different sizes and fusion the detection result. Finally, the proposed method was validated using real measured NASAlJPL AIRSAR datasets by comparing the performance with the modified constant false alarm rate (CFAR) detector. The comparison results demonstrate the validity and generality of the proposed detection algorithm.

Space debris damage orbiting spacecraft and astronauts and ISAR imaging is an important method to recognize and classify debris. Compared with 2-D imaging, 3-D imaging is able to provide more information. However, debris with rapid spinning have great migration through range-cells, so common methods are unproductive. A novel method of ISAR 3-D imaging based on nonparametric Bayesian model is proposed aimed at debris with spinning. Firstly, a motion model and a signal model are proposed. Secondly, PSO algorithm is utilized to preprocess the data and obtain the height of target. Finally, Nonparametric Bayesian model is imposed to elaborately reconstruct the target in range and cross-range. For monostatic radar, point-target simulation data and electromagnetism data confirm that the method will obtain refined 3-D imaging results. Meanwhile, this method is capable to surmount the obstacle of Doppler aliasing and data missing caused by rapid spinning.

To improve the performance of expectation maximization (EM) in retrieving the missing data of inverse synthetic aperture radar (ISAR) scatterer trajectory matrix, a novel initialization method is proposed. Firstly, we derive the ellipse motion dynamics of the projected scatterer trajectory. Then, based on estimated ellipse parameters using known data of each scatterer trajectory, we propose the bidirectional Kalman filter to initialize the missing data. Finally, EM algorithm is applied to estimate the missing data and factorization method is performed on the complete trajectory matrix to obtain the three-dimensional geometry of scatterers. Experimental results using simulated data verify the effectiveness of the proposed initialization method.

As tracked vehicles play significant roles in a battlefield, effective jamming measures is necessary to protect them from being perceived by hostile radar. Moving tracked vehicles exhibit strong Doppler and micro-Doppler signatures. Therefore, the jamming signal should include micro-Doppler modulation generated by metallic caterpillars for successful deception jamming. Based on detailed analysis of kinetic characteristics of tracked vehicles, this paper proposes a new deception jamming method for tracked vehicles against continuous-wave ground surveillance radar. To obtain precise deception jamming effect, this method achieves both translational modulation for rigid parts and micro-Doppler modulation for the caterpillars. Finally, simulation results have proven the effectiveness of the proposed method.

Terrain observation by progressive scans synthetic aperture radar (TOPSAR) is a novel two dimensional imaging mode with wide swath coverage. However, it will cause problems when our regions of interest are being detected by hostile TOPSAR. To prevent this threaten from TOPSAR, this paper presents a corresponding deceptive jamming method by taken the effect of changing relative position between jammer and TOPSAR platform into consideration. Meanwhile, our approach decomposes the jamming problem against TOPSAR into simpler subproblems of the same type. Therefore, a parallel processing scheme could be carried out to implement effectively jamming against TOPSAR.

In recent studies, synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms based on convolutional neural network (CNN) have achieved high recognition rates in the moving and stationary target acquisition and recognition (MSTAR) data set. However, the correlation between clutter in the training and test data sets is ignored in these algorithms, although most of them used only the center part of the images by removing a lot of the clutter but not everything, which may result in better performance than what would be achieved in the operational scenarios. To tackle this problem, we propose a target segmentation method based on morphological operations to generate data sets without clutter. Then, we design the large-margin softmax (LM-softmax) batch-normalization CNN (LM-BN-CNN) structure, which utilizes the LM-softmax classifier in the last layer to increase the separability of samples after clutter removal. In addition, this structure performs BN with constant mean and variance to increase the convergence speed and reduce overfitting. Experiments on the MSTAR data set have shown that LM-BN-CNN obtains better performance than the available CNNs designed for SAR ATR of ground vehicles, and it has robustness to large depression angle variation, configuration variants, and version variants.

The anisotropy of radar cross section of scatterers makes the scatterer trajectory matrix incomplete in sequential inverse synthetic aperture radar images. As a result, factorization methods cannot be directly applied to reconstruct the 3-D geometry of scatterers without additional consideration. We propose a modified expectation-maximization (EM) algorithm to retrieve the complete scatterer trajectory matrix. First, we derive the motion dynamics of the projected scatterer, which approximates an ellipse. Then, based on the estimated ellipse parameters using the known data of each scatterer trajectory, we use the Kalman filter to initialize the missing data. To address the limitations of a traditional EM, which only considers the rank-deficient characteristics of the scatterer trajectory matrix, we propose to augment EM by using both the known rank-deficient and elliptical motion characteristics. Experimental results on simulated data verify the effectiveness of the proposed method.

To obtain higher range resolution without incurring significant hardware costs, this paper proposes a novel method for high-resolution sparse subband imaging based on Bayesian learning. The signal model is derived and a probabilistic model is constructed. In particular, hierarchical sparse-promoting priors are imposed on the distribution of scattering centers, which is conjugate to the likelihood function. Then, a closed-form solution is derived based on the MAP-expectation-maximization framework. A multilevel dictionary which automatically adjusts the distance between adjacent atoms is adopted to achieve refined estimation with moderate computational burden. Finally, a coherent processing method is addressed. Experimental results have demonstrated the effectiveness of the proposed method in low signal-to-noise ratio and complex target scenarios.

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent and update the class labels of all pixel vectors using -expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.

Multiple informative polarimetric descriptors can be computed from direct measurements of polarimetric covariance matrix and target decomposition theorems. Under the tensor algebra framework, each pixel is modeled as a third-order tensor object by combining multi-features and incorporating neighborhood spatial information together. Typically, the tensor object is of high correlation and redundancy in both the spatial and feature dimensions. In this paper, we propose a feature extraction method using the multilinear principal component analysis to facilitate the classification process. Experimental results in comparison with principal component analysis, independent component analysis and linear discriminate analysis demonstrate that the classification accuracy is significantly improved since the extracted features by the proposed method are more discriminative.

This paper presents an automatic ship detection algorithm for polarimetric synthetic aperture radar (PolSAR) data. Based on the non-Gaussian K-Wishart distribution model for complex backscattering coefficients, the PolSAR image is clustered automatically by a modified expectation maximization algorithm. A goodness-of-fit test is incorporated to improve the model fitness of the cluster iteratively. Then, the SPAN of ship cluster center is used to detect ships. Finally, the experimental results of a real measured UAVSAR dataset show that the proposed algorithm could improve the ability of weak target detection while reduces the rate of false alarm and miss detections.

Radio frequency interference is a major issue for synthetic aperture radar (SAR) imaging. Especially with the presence of wideband interference (WBI), the signal-to-interference ratio (SIR) of the measurements is greatly degraded, thus making it difficult to produce a high-quality SAR image. Compared with narrow-band interference (NBI), WBI occupies a larger bandwidth and is more complicated to deal with. This paper addresses the detection and mitigation of WBI in high-resolution airborne SAR data. First, a WBI-corrupted echo is characterized in the time-frequency representation by utilizing the short-time Fourier transform. In this way, the original range-spectrum WBI mitigation problem can be simplified into a series of instantaneous-spectrum NBI mitigation problems. For each instantaneous spectrum, the existence of interference signal can be identified according to the negentropy-based statistical test. Furthermore, the interference signal is mitigated by notch filtering or eigensubspace filtering. The experimental results of the simulated data, as well as real measured data sets, show that the proposed scheme is effective in suppressing the interference signal and in obtaining a high-quality image.

Traditionally, the factorization method is applied to reconstruct the 3D geometry of a target from its sequential inverse synthetic aperture radar images. However, this method requires performing cross-range scaling to all the sub-images and thus has a large computational burden. To tackle this problem, this paper proposes a novel method for joint cross-range scaling and 3D geometry reconstruction of steadily moving targets. In this method, we model the equivalent rotational angular velocity (RAV) by a linear polynomial with time, and set its coefficients randomly to perform sub-image cross-range scaling. Then, we generate the initial trajectory matrix of the scattering centers, and solve the 3D geometry and projection vectors by the factorization method with relaxed constraints. After that, the coefficients of the polynomial are estimated from the projection vectors to obtain the RAV. Finally, the trajectory matrix is re-scaled using the estimated rotational angle, and accurate 3D geometry is reconstructed. The two major steps, i.e., the cross-range scaling and the factorization, are performed repeatedly to achieve precise 3D geometry reconstruction. Simulation results have proved the effectiveness and robustness of the proposed method.

Radio frequency interference is a major issue for synthetic aperture radar (SAR) imaging. Especially with the presence of wide band interference (WBI), the signal to interference and noise ratio of the measurements are greatly degraded, and thus makes it difficult to obtain high quality SAR image. In this paper, we analyzed the WBI signatures in a real measured data, and addressed the WBI mitigation problem by using the Eigensubspace filtering on the instantaneous spectra. Experimental results of the real measured data show that the proposed scheme is effective for suppressing the interference signal and for obtaining high-quality image.

For polarimetric synthetic aperture radar (PolSAR) data, various polarimetric signatures can be obtained by target decomposition techniques, which are of great help for characterizing the land cover. It is straightforward to combine these polarimetric features together and formulate them as a third-order polarimetric feature tensor. However, how to make full use of the abundant information provided by these polarimetric features remains a challenge. A feasible solution is applying feature extraction (FE) techniques on the high-dimensional polarimetric manifold to obtain a lower dimensional intrinsic feature set. Common FE methods, such as principal component analysis (PCA), independent component analysis (ICA), etc., use matrix linear algebra and require rearranging the original tensor into a matrix. This leads to the loss of the spatial information of the PolSAR data. In this paper, to jointly take advantage of the spatial and feature information, a novel FE scheme incorporating ICA with the tensor decomposition techniques is proposed. After applying the proposed FE method on the third-order polarimetric feature tensor, each PolSAR image pixel is represented by a low-dimensional intrinsic feature vector. Furthermore, these feature vectors are fed to the k-nearest neighbor (KNN) classifier and support-vector-machine classifier for supervised classification. Simulated data, together with two measured data sets, i.e., Flevoland of Airborne Synthetic Aperture Radar (AIRSAR) and Québec City of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), are utilized to evaluate the performance of the proposed method. For comparison purpose, several classical and advanced FE methods, such as PCA, ICA, Laplacian eigenmaps, and LRTAdr- (K1,K2,p), are also applied. The experimental results demonstrate the superiority of the proposed FE method in three folds: 1) The extracted features by the proposed method are more discriminative, characterized by the high separability in the scatterplots; 2) the classification accuracy is improved as much as approximately 7% compared with the complex Wishart classifier; and 3) the proposed method is computational efficient and has fast convergence.

With the development of target decomposition theorems for polarimetric synthetic aperture radar (PolSAR) data, various informative polarimetric descriptors could be obtained. The redundancy among these descriptors poses a hindrance to accurate classification. In this paper, we propose a tensor-based dimension reduction technique, which aims to obtain a lower-dimensional intrinsic feature set from the high-dimensional polarimetric manifold. We combine 48 polarimetric features together and formulate them as a third-mode tensor. The spatial information is taken into consideration for feature reduction. Experimental results in comparison with principal component analysis (PCA), independent component analysis (ICA) and Laplacian Eigenmaps (LE) proves its effectiveness.

Narrow-band interference (NBI) poses a hindrance to high quality imaging for synthetic aperture radar (SAR). In this paper, we addressed the NBI suppression problem by introducing two advanced techniques: the complex empirical mode decomposition (CEMD) and the independent component analysis (ICA). Both of these two methods utilize the statistical difference between the useful radar echoes and NBI. They decompose the contaminated pulse into some basis signals, and select out the basis that corresponding to NBI. Then the contribution of NBI is excised by filtering out the corresponding NBI components. We compare the performance of these advanced methods with the conventional notching filtering method. The experimental results of the real dataset show the effectiveness of the proposed methods.

When wide or ultrawideband, low pulse repetition frequency (PRF) radar is applied to the imaging of space targets; it is highly possible that motion through range cell and azimuth under-sampling occurs, which will result in image smearing. To figure out this problem, this paper proposes a novel, three-step imaging method using the high-resolution range profile (HRRP) series. In the first step, high-quality HRRP series are obtained based on the theory of sparse signal representation. Then, based on the Kalman predictor and the minimum Euclidean distance criterion, motion and amplitude feature-based scatterer trajectory association is carried out to form the scatterer trajectory matrix, from which the scatterer locations are conveniently solved in the last step. Compared to the traditional imaging techniques based on Doppler analysis, the proposed method is able to mitigate the influence of azimuth under-sampling, and may provide a new solution to high-resolution imaging of targets moving nonuniformly in low PRF scenarios. Finally, simulations have proved the effectiveness of the proposed method.

The mitigation of narrow-band interference (NBI) is an appealing topic in the synthetic aperture radar (SAR) community. It is an underdetermined single-channel separation problem. This paper proposes a method for NBI mitigation using the independent subspace analysis. First, each single pulse is transformed onto a manifold time-frequency distribution by the short-time Fourier transform (STFT). Then, the singular value analysis is carried out to extract the prominent features corresponding to the NBIs. Next, independent component analysis is employed to obtain statistically independent basis components. Furthermore, the independent subspaces corresponding to NBI are reconstructed and subtracted from the raw signal space. The signal with NBI mitigated is resynthesized by inverse STFT. Finally, after processing all the pulses, a well-focused SAR imagery is obtained by a conventional imaging algorithm. Experimental results of simulated and measured data have demonstrated the effectiveness of the proposed method.

To achieve finer range resolution without increasing the bandwidth and sampling rate of the radar system, high-resolution imaging by data synthesizing using sparse subbands has received intensive attention in recent years. This paper derives the imaging geometry and signal model for radar imaging of space targets from sparse subbands. Next, it introduces and analyzes the available methods. Then, a practical method is proposed for sparse subband imaging of space targets in high-speed motion, which comprises phase compensation along the range and azimuth, gapped-data filling, scatterer number estimation, amplitude estimation, and azimuth image fusion. Finally, imaging of the simulated data using the fixed-point and electromagnetic scattering models proved the validity of the proposed method.

The narrow-band interference (NBI) is a common jamming signal against synthetic aperture radar (SAR), which can degrade the imaging quality severely. This paper proposes a new method for NBI suppression in the data domain based on the independent component analysis (ICA). In this method, echoes contaminated by the NBI are identified in the frequency domain. Next, time filtering and whitening are performed to the identified echoes. Then, the ICA is carried out to decompose the echoes into a series of basis signals, and the jamming components are selected by thresholding. Finally, the NBI is reconstructed and subtracted from the echoes, and the well-focused SAR imagery is obtained by conventional imaging methods. The proposed method copes well with the time-varying NBI with little signal loss. Results of simulated and measured data have proved the validity of the proposed method.

Based on the synthetic aperture radar (SAR) geometric model, a novel, fast algorithm of large scene deceptive jamming against the space-borne SAR is proposed. First, we divide the jamming scene template into sub-templates according to the depth of focus in the range dimension. Next, each sub-template is decomposed into the slow-time-dependent and slow-time-independent terms in the range frequency-azimuth time domain. The slow-time-independent terms are generated off-line while the slow-time-dependent terms are generated by real-time 1-D frequency modulation. Then, the sub-templates are convolved with the intercepted SAR signals simultaneously. Finally, fast deceptive jamming is achieved by incorporating all the sub-templates together. In the proposed method, the two-step realization of the sub-templates and the parallel sub-block processing improves the algorithm efficiency. The simulation results prove the validity of the proposed algorithm.

Based on the difference in statistical distribution between the target and jamming signal in the synthetic aperture radar (SAR) image, this letter proposes a novel adaptive method for barrage jamming suppression. In this method, the covariance matrix of jamming is estimated from the SAR image. Then, the 2-D sinc function of the ideal point target is utilized as the steering vector to obtain the optimal adaptive filter. This filter can suppress the random barrage jamming effectively, thus improving the image contrast and interpretability. Additionally, this letter analyzes in detail the theoretical basis and performance of the proposed method. Finally, simulations are provided to demonstrate its effectiveness.

This paper proposes a novel method for high-resolution imaging of group targets moving in a formation with constant accelerated rectilinear motion. In this method, the first- and second-order phase terms are compensated simultaneously to obtain a “bulk” image of group targets. Then, regions of subtargets are determined by the segmentation method based on clustering number estimation and normalized cuts. Finally, refined compensation of the second- and third-order phase terms is carried out jointly to obtain a well-focused image of group targets, following the minimum local image entropy criterion. Simulation results have proved the validity of the proposed method.

In 3-D inverse synthetic aperture radar (ISAR) imaging of spinning space debris, the image obtained via the available algorithm is modified by a scaling factor. Determined by the angle between the spinning vector and the radar line of sight, this factor cannot be estimated by a monostatic radar in a short imaging interval. This letter derives the bistatic ISAR (Bi-ISAR) geometry and signal model for 3-D imaging of space debris. Then, a 3-D image scaling algorithm is introduced based on the connections between the mono- and bistatic echoes of the same scatterer. Numeric simulations have proved the validity of the proposed algorithm.

Narrow-band interference (NBI) is a common interference source in synthetic aperture radar (SAR) imaging. Its existence will degrade the imaging quality greatly. Based on detailed analysis on the characteristics of NBI, this letter proposes a new NBI suppression algorithm using the complex empirical mode decomposition (CEMD) method. In this algorithm, echoes that include NBI are recognized in the time domain first. Then, these echoes are decomposed into a number of intrinsic mode functions (IMFs) via the CEMD. After that, IMFs that correspond to NBI are subtracted from the echoes by thresholding. Finally, well-focused SAR imagery can be obtained from the separated target echoes using traditional SAR imaging algorithms. The effective data loss in this algorithm is smaller than other NBI suppression approaches. In addition, this algorithm is robust to time-varying NBI. Imaging results of measured data have proved the validity of this algorithm.

Since space debris could post a significant threat to orbiting objects around the Earth, their reorganization, measurement, and catalogue are of great importance. This paper establishes a 3-D inverse synthetic aperture radar (ISAR) imaging geometry and signal model for space debris. Then, a 3-D imaging algorithm is proposed to realize coherent imaging in the range-slow-time domain. This algorithm is based on the complex-valued back-projection transform according to the spinning nature of space debris. The simulation results for both point scattering and continuous targets have proved the validity of the proposed algorithm.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is very important for battlefield awareness. For SAR systems mounted on a UAV, the motion errors can be considerably high due to atmospheric turbulence and aircraft properties, such as its small size, which makes motion compensation (MOCO) in UAV SAR more urgent than other SAR systems. In this paper, based on 3-D motion error analysis, a novel 3-D MOCO method is proposed. The main idea is to extract necessary motion parameters, i.e., forward velocity and displacement in line-of-sight direction, from radar raw data, based on an instantaneous Doppler rate estimate. Experimental results show that the proposed method is suitable for low- or medium-altitude UAV SAR systems equipped with a low-accuracy inertial navigation system.

For micromotion targets with rotating parts, the inverse synthetic-aperture-radar image of the main body may be shadowed by the micro-Doppler. To solve this problem, this paper proposes an imaging algorithm based on the complex-valued empirical-mode decomposition. First, the radar echoes are decomposed into a series of complex-valued intrinsic-mode functions (IMFs). Then, the IMFs from the rotating parts and those from the main body are separated according to the characteristics of their zero-crossings. Finally, the well-focused imaging of the main body via traditional imaging algorithm and the accurate parameter estimation of the rotating part can be obtained. Both the imaging results for the simulated and measured data are given to verify the validity of the proposed algorithm.

Synthetic aperture radar (SAR) has found wide applications in many areas, e.g., battlefield awareness. However, SAR is vulnerable to various kinds of interference, among which narrow-band interference (NBI) is commonly used. In this letter, an eigensubspace-based filtering approach is proposed for NBI suppression in SAR without using passive-sniff data as the reference signal. Moreover, the proposed method can deal with smart or interrupted NBI. Both simulation and experimental results are provided to illustrate the performance of the proposed approach