Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for 3D human pose estimation in ubiquitous Internet of Things (IoT) applications, owing to its privacy-preserving nature and robustness under challenging illumination conditions. However, accurate skeletal reconstruction remains difficult due to the inherent sparsity, non-uniform distribution, and instability of radar point clouds. To address these challenges, this paper proposes MS-STPoseNet, a unified multi-scale spatio-temporal learning framework for robust mmWavebased human pose estimation. For spatial representation, MS-STPoseNet employs a hierarchical multi-scale spatial encoder built upon PointNet++, which leverages multi-scale grouping to effectively capture body structures across different spatial resolutions, from fine-grained joint regions to limb- and torso-level configurations, under irregular radar observations. For temporal modeling, a multi-branch Temporal Convolutional Network (TCN) with different dilation rates is introduced to model multi-rate motion dynamics, enabling effective representation of both rapid limb movements and smoother torso motions. An attention mechanism is further incorporated to enhance informative temporal features while suppressing noise. The entire framework is trained end-to-end to estimate per-frame 3D poses by exploiting short-term temporal context, thereby improving robustness under noisy sensing conditions. Extensive experiments conducted on a self-collected dataset and two public benchmarks demonstrate that MS-STPoseNet consistently outperforms state-of-the-art methods in terms of pose estimation accuracy and cross-subject generalization, achieving an MPJPE of 3.08 cm on the self-collected dataset. In addition, the proposed framework exhibits favorable computational efficiency and a compact model size, highlighting its potential applicability to practical IoT sensing systems.

Affected by the large dynamic range of gray values, strong scattering point edge effect, noise and clutter, inverse synthetic aperture radar (ISAR) images have problems such as boundary blurring and target discontinuity, which bring great challenges to ISAR space target component segmentation. In this paper, a novel ISAR space target component segmentation method, called scattering characteristics guided network (SCGN), is proposed. First, a cross-scale self-attention module (CSSAM) is proposed, which establishes global relationships in different dimensions during cross-scale feature fusion, refining the detailed features of the target while suppressing high sidelobe scattering points and noise. Second, a novel component scattering center extractor (CSCE) is proposed to combine scattering center distribution with the network via explicit supervision. Finally, a novel scattering characteristics-assisted segmentation head (SCASH) is proposed, which introduces the scattering characteristics of each component into the mask segmentation process and models the semantic interdependencies over long distances through a spatial attention mechanism to achieve fine-grained component segmentation. Experimental results on the ISAR simulation dataset and realistic ISAR images show that SCGN outperforms existing methods.

This letter investigates the low-variance broad beampattern design method in distributed phased multiple-input multiple-output (phased-MIMO) radar. The constant modulus constraint across multiple subarrays results in a low-rank and nonconvex objective function, which is traditionally addressed by reformulating it into a solvable semidefinite program through convex relaxation. In contrast, we propose a Riemannian manifold-based method to directly address the low-rank problem without relaxation. The low-variance broad beampattern design is first transformed into an unconstrained quadratic form on a complex constant modulus manifold. Then, a Riemannian conjugate gradient descent (RCGD)-based optimization is proposed to solve the nonconvex objective function by deriving the gradient descent direction and adaptive step size. Numerical simulations demonstrate the superior performance in terms of computation speed and accuracy compared to the conventional methods.

Pixel-wise depth completion using multi-sensor fusion is crucial in areas such as autonomous driving. While LiDAR and image fusion methods exhibit reliability, it can face challenges in adverse weather conditions, such as rain and fog. In contrast, mmWave radar, emerged in recent years, has stronger anti-interference capability. However, radar point typically features high sparsity. And mmWave radar has lower resolution in the height dimension, leading to increased errors when projected onto the image plane. To solve the problem, this paper proposes a two-stage fusion convolutional neural network. In the first stage, image features are utilized to filter the noisy radar point cloud and learn the mapping of radar points to image regions. In the second stage, we perform multiscale fusion of the image with the coarse depth map generated in the first stage to predict the missing depth values. Experiment results indicate that our improved strategy reduces the error of depth value estimation. Our network shows a 4.5% improvement in RMSE(root-mean-square error) compared to the previous method.

This paper aims to address the issue of geometric distortion in traditional 2D SAR imaging under near-field conditions. To achieve this, the study explores the geometric model of MIMO SAR 3D imaging and derives the 3D back projection (BP) imaging algorithm based on it. The algorithm’s effectiveness is verified through the simulation of point targets and the collection of data using a sliding rail SAR system to image metal sheets in actual scenes. The simulation and actual imaging results confirm that our near-field BP algorithm has better imaging results compared to the BP algorithm which does not consider near-field effects while maintaining acceptable computational complexity.

Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring.

Convolutional Neural Networks (CNNs) have significantly impacted embedded system applications across various domains. However, this exacerbates the real-time processing and hardware resource-constrained challenges of embedded systems. To tackle these issues, we propose spin-transfer torque magnetic random-access memory (STT-MRAM)-based near memory computing (NMC) design for embedded systems. We optimize this design from three aspects: Fast-pipelined STT-MRAM readout scheme provides higher memory bandwidth for NMC design, enhancing real-time processing capability with a non-trivial area overhead. Direct index compression format in conjunction with digital sparse matrix-vector multiplication (SpMV) accelerator supports various matrices of practical applications that alleviate computing resource requirements. Custom NMC instructions and stream converter for NMC systems dynamically adjust available hardware resources for better utilization. Experimental results demonstrate that the memory bandwidth of STT-MRAM achieves 26.7 GB/s. Energy consumption and latency improvement of digital SpMV accelerator are up to 64× and 1,120× across sparsity matrices spanning from 10% to 99.8%. Single-precision and double-precision elements transmission increased up to 8× and 9.6×, respectively. Furthermore, our design achieves a throughput of up to 15.9× over state-of-the-art designs.

With the rapid development of deep learning technology, many synthetic aperture radar (SAR) target recognition algorithms based on convolutional neural networks have achieved exceptional performance on various datasets. However, conventional neural networks are repeatedly iterated on a fixed dataset until convergence, and once they learn new tasks, a large amount of previously learned knowledge is forgotten, leading to a significant decline in performance on old tasks. This article presents an incremental learning method based on strong separability features (SSF-IL) to address the model’s forgetting of previously learned knowledge. The SSF-IL employs both intraclass and interclass scatter to compute the feature separability loss, in order to enhance the linear separability of features during incremental learning. In the process of learning new classes, an intraclass clustering loss is proposed to replace the conventional knowledge distillation. This loss function constrains the old class features to cluster around the saved class centers, maintaining the separability among the old class features. Finally, a classifier bias correction method based on boundary features is designed to reinforce the classifier’s decision boundary and reduce classification errors. SAR target incremental recognition experiments are conducted on the MSTAR dataset, and the results are compared with several existing incremental learning algorithms to demonstrate the effectiveness of the proposed algorithm.

Convolutional neural networks (CNNs) have made remarkable achievements in many tasks. However, most of them are hardly applied to embedded systems directly because of the requirement of huge memory space and computing power. In this article, we propose a pruning framework, namely, FiltDivNet, to accelerate and compress CNN models for their applicability to small or portable devices. The correlations among filters are taken into account and measured by the goodness of fit. On this basis, a hybrid-cluster pruning strategy is designed with dynamic pruning ratios for different clusters in CNN models. It aims at representing its filters in their diversity by removing redundant ones cluster by cluster. In addition, a new loss function with adaptive sparsity constraints is introduced for the retraining and fine-tuning in the FiltDivNet. Finally, some comparative experiments based on classical CNN models are carried out to demonstrate its effectiveness in compression performance and its adaptability with different CNN architectures.

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.

Tracking an Unmanned Aerial Vehicle (UAV) to obtain its locations and trajectory is a crucial task to avoid the unlawful use of UAVs. However, most existing UAV tracking methods fail when facing cluster environments, out-of-view, and occlusions because of their insufficient representation of global context information capacity. To mitigate these issues, we propose a new tracker, namely SiamFusion, to innovate a dual fusion procedure that leverages the advantages in both the feature and decision levels. In particular, we propose a novel feature fusion module named Modality-Fusion to utilize multi-modal information, enhancing the perception of the target. From the decision level, we further develop a local-global converter based on a multi-modal fusion decision-making mechanism to reduce the accumulation during tracking, which significantly increases the robustness of the tracking process. Extensive experiments demonstrate the superiority of the proposed SiamFusion, which achieves the best performance on Anti-UAV in terms of accuracy and speed. In particular, we exceed the state-of-the-art tracking algorithm in the tracking accuracy by 4.2% at a similar frame rate. Our source codes, pre-trained models, and online demos will be released upon acceptance.

Abstract Recent studies reveal the crucial role of local features in learning robust and discriminative representations for person re‐identification (Re‐ID). Existing approaches typically rely on external tasks, for example, semantic segmentation, or pose estimation, to locate identifiable parts of given images. However, they heuristically utilise the predictions from off‐the‐shelf models, which may be sub‐optimal in terms of both local partition and computational efficiency. They also ignore the mutual information with other inputs, which weakens the representation capabilities of local features. In this study, the authors put forward a novel Attribute‐guided Transformer (AiT), which explicitly exploits pedestrian attributes as semantic priors for discriminative representation learning. Specifically, the authors first introduce an attribute learning process, which generates a set of attention maps highlighting the informative parts of pedestrian images. Then, the authors design a Feature Diffusion Module (FDM) to iteratively inject attribute information into global feature maps, aiming at suppressing unnecessary noise and inferring attribute‐aware representations. Last, the authors propose a Feature Aggregation Module (FAM) to exploit mutual information for aggregating attribute characteristics from different images, enhancing the representation capabilities of feature embedding. Extensive experiments demonstrate the superiority of our AiT in learning robust and discriminative representations. As a result, the authors achieve competitive performance with state‐of‐the‐art methods on several challenging benchmarks without any bells and whistles.

Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) is one of the most important research directions in SAR image interpretation. While much existing research into SAR ATR has focused on deep learning technology, an equally important yet underexplored problem is its deployment in incremental learning scenarios. This letter proposes a new benchmark approach, termed Memory augmented weights alignment and Enhancement Discrimination Incremental Learning (MEDIL) algorithm to address this issue. Firstly, the attention mechanism is employed as part of the benchmark. Next, we discuss the problem of height deviation of weights at the fully connected layer and design a more suitable alignment of weights by guiding the memory module for contextual data processing. In addition, we leverage the incremental progressive sampling strategy to alleviate the imbalance between old and new classes during the training period. Finally, we propose to enhance the distinction among various classes with an angular penalty loss function to ensure the diversity of incremental instances. The proposed method is evaluated on MSTAR and OpenSARShip under different experimental settings. Experimental results demonstrate that our proposed approach can effectively solve catastrophic forgetting in SAR multiclass recognition problems.

The size of parameters in artificial neural network (NN) applications grows quickly from a handful to the GB-level. The data transmission poses a key challenge for NN, and either neuron is removed or data compression reduces pressure on memory access but cannot successfully decrease data traffic. Therefore, we propose the near spin-transfer-torque magnetic random processing architecture for developing energy-efficient NNs. Our approach provides system architects with a preliminary scheme to obtain real-time transmission that near memory controller directly compresses non-zero elements, and encodes the corresponding index depending on the kernel size. Furthermore, it adjusts the number of multiplication accumulators and avoids unnecessary hardware overheads during computation. The preliminary experimental results demonstrated this design verified with weights that currently achieve up to 3.05x speedup and 29.6% power compared with the unoptimized one.

Pedestrian detection has received considerable research interest due to its wide application and has made significant progress along with the development of deep neural networks. However, crowd occlusion still remains a significant challenge to current state-of-the-art pedestrian detectors due to the complication in formulating interactions between occluded instances. Inspired by the Coulomb force, we in this work set each proposal as a single electric charge and define the attractive and repulsive forces to model the interaction between ground truths and assigned proposals. This design is driven by two motivations: the attractive force pulls bounding boxes toward their assigned targets, aggregating them compactly around the ground truths. The repulsive force pushes bounding boxes away from other instances, preventing them from shifting to surrounding pedestrians. With this insight, we propose a novel bounding box regression loss and achieve more robust localization performance in crowded scenes without introducing any computational overhead. Extensive experimental evaluations on the CityPersons and CrowdHuman benchmarks demonstrate consistent state-of-the-art performance.

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients’ treatment outcome. Nowadays, the evaluation is performed in a manual manner, which is time-consuming, subjective, and error-prone. In this article, we aim to automate this process by leveraging the advances in computer vision and artificial intelligence, to provide an objective and accurate method for root canal therapy result assessment. A novel anatomy-guided multi-branch Transformer (AGMB-Transformer) network is proposed, which first extracts a set of anatomy features and then uses them to guide a multi-branch Transformer network for evaluation. Specifically, we design a polynomial curve fitting segmentation strategy with the help of landmark detection to extract the anatomy features. Moreover, a branch fusion module and a multi-branch structure including our progressive Transformer and Group Multi-Head Self-Attention (GMHSA) are designed to focus on both global and local features for an accurate diagnosis. To facilitate the research, we have collected a large-scale root canal therapy evaluation dataset with 245 root canal therapy X-ray images, and the experiment results show that our AGMB-Transformer can improve the diagnosis accuracy from 57.96% to 90.20% compared with the baseline network. The proposed AGMB-Transformer can achieve a highly accurate evaluation of root canal therapy. To our best knowledge, our work is the first to perform automatic root canal therapy evaluation and has important clinical value to reduce the workload of endodontists.

Deep learning (DL) techniques recently have attracted much attention in the synthetic aperture radar (SAR) automatic target recognition (ATR). Due to the coherent imaging pattern, SAR images inherently suffer from the speckle noise. To mitigate its influence, this letter proposes a joint convolutional neural network (J-CNN) for simultaneous despeckling and classification of SAR targets. It integrates a two-step process in the CNN framework but without the pooling operation during the despeckling phase. Then, a new loss function is introduced, and its partial derivatives with respect to weights are given for the training of J-CNN. Finally, comparative experiments with some classical network models are carried out based on synthetic SAR target images. The results demonstrate that the proposed method not only significantly outperforms other models under strong speckle noise condition but also has an efficient architecture with fewer weight parameters.

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.

Recently, optimistic fair exchange in electronic commerce (e-commerce) or mobile commerce (m-commerce) has made great progress. However, new technologies create large amounts of data and it is difficult to handle them. Fortunately, with the assistance of cloud computing and big data, optimistic fair exchange of digital items in cyber-physical systems (CPSes) can be efficiently managed. Optimistic fair exchange in cloud-assisted CPSes mainly focuses on online data exchange in e-commerce or online contracts signing. However, there exist new forms of risks in the uncertain network environment. To solve the above problems, we use a new technique called verifiably encrypted identity-based signature (VEIS) to construct optimistic fair exchange in cloud-assisted CPSes. VEIS is an encrypted signature, and we can check the validity of the underlying signature without decrypting it. We introduce a robust arbitration mechanism to guarantee fairness of the exchange, and even the trusted third party (TTP) cannot get the original signatures of the exchange parties. And the TTP in our protocol is offline, which greatly improves the efficiency. Besides, we show that our protocol is secure, fair, and practical.

Detecting dynamic vehicles is of great significance in the field of autonomous vehicles. In the literature, a few vehicle detection methods are proposed to detect vehicles within 50 m from the Lidar, where the point clouds are relatively dense. It is a great challenge to detect vehicles that are far from the Lidar because of sparse point clouds. Fewer returned point clouds will result in a larger fitting randomness and lower detection rate. To tackle this issue, a dynamic vehicle detection method based on likelihood-field-based model combined with coherent point drift (CPD), which includes the steps of dynamic object detection and dynamic vehicle confirmation, is proposed in this paper. An adaptive threshold based on the distance and grid angular resolution is applied to detect the dynamic objects. The pose estimation based on CPD (PE-CPD) is proposed to estimate the vehicle pose. The scaling series algorithm coupled with a Bayesian filter that is improved by PE-CPD is utilized for updating the vehicle states. Finally, comparative experiments of vehicle detection based on KITTI data sets are conducted. The results show that the proposed method improves the detection rate, especially the detection in the radius of 40-80 m, which is termed as distant area, compared with the method based on pose estimation with modified scaling series.

Traditional multitarget tracking algorithms assume that each target can generate at most one detection per scan. However, in the over-the-horizon radar (OTHR), a target may produce multiple detections because of multipath propagation. In this paper, we propose a new algorithm, called multipath generalized labeled multi-Bernoulli (MP-GLMB) filter, to effectively track multiple targets in such multiple-detection systems. The proposed technique is based on the labeled random finite set (RFS), which estimates the number of targets and the trajectories of their states. The proposed MP-GLMB filter is compared with the multipath version of the probability hypothesis density (PHD) filter and the multi-target multi-Bernoulli (MeMber) filter, and simulation results show that our algorithm has improved tracking performance.

This paper considers the target localization problem using the hybrid bistatic range and time difference of arrival (TDOA) measurements in multistatic radar. An algebraic closed-form solution to this nonlinear estimation problem is developed through two-stage processing, where the nuisance variables are introduced in the first stage and the localization error of first stage solution is estimated to improve the final target position estimate in the second stage. Theoretical analysis shows that the performance of the proposed method can reach the Cramer-Rao lower bound (CRLB) for Gaussian measurement noise over the small error region. Simulations are included to corroborate the performance of the proposed estimator.

Synthetic aperture radar (SAR) target configuration recognition is a challenging task, and the key point is to realize effective feature extraction. An algorithm combing the advantages of sparse graph and locality preserving projections (LPP) is proposed to achieve SAR target configuration recognition. Taking the merits of sparse representation (SR) into consideration, an affinity matrix is established to realize effective structure preserving of the dataset. Besides, the problem of matrix singularity in LPP is effectively resolved by diagonal loading. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database validate the effectiveness and superiority of the proposed algorithm.

Focusing on the problem of the real-time implementation in sparse representation (SR) based recognition algorithm, a fast sparse representation (FSR) algorithm is presented in this paper to improve the efficiency of synthetic aperture radar (SAR) target configuration recognition. Taking the inertia variance characteristic of SAR target images over a small range of azimuth angles into consideration, training samples of each configuration are averaged. Instead of using all the training samples to establish the dictionary in SR, the average samples are utilized to construct the dictionary in FSR. A small dictionary accelerates the speed of the proposed algorithm.

It is very difficult for the millimeter wave (MMW) seeker to detect and capture the target. Tracking the target unstably, even losing the target happens frequently. Focusing on the problem, a simple but effective target recapture method is presented for narrow-beam MMW seeker in this paper. The parameters outputted by the inertial navigation system (INS) and the seeker are utilized to deduce the coordinates of the target. And then, target searching is implemented again on the basis of the deduced coordinates. The target recapture time can be dramatically reduced by using the proposed method, thus guaranteeing enough terminal guidance time. The effectiveness of the proposed method is verified by the mooring test-fly experiments.

Focusing on the problem of the millimeter wave (MMW) seeker performance evaluation, which plays an important role for the terminal control algorithm design, an evaluation method is proposed based on the real-time kinematic (RTK) for moving targets. Firstly, time synchronization is realized for different global position system (GPS) carrier platforms taking a controller as the reference. And then, the key parameters associated with the guidance control are calculated on the basis of the GPS measurements. Finally, parameter comparisons are implemented by using the calculated values and the seeker's outputs. The effectiveness of the proposed MMW seeker evaluation method is verified by the mooring test-fly experiments.

Locality preserving projections (LPP) can preserve the local structure of the datasets effectively. However, it is not capable of separating the samples that are close to each other in the high-dimensional space but belong to different classes. Focusing on the problem, a class-dependent locality preserving projections (CDLPP) algorithm is proposed in this paper. The class information is embedded into the LPP model, and the similarity matrix and the difference matrix are constructed according to the class information. The similarity matrix is utilized to preserve the local structure of the samples belong to the same class, whereas the difference matrix is utilized to separate the samples that are close to each other in the high-dimensional space but belong to different classes. Experiments are conducted using the moving and stationary target acquisition and recognition (MSTAR) database, the results verify the effectiveness of the proposed algorithm.

The performance of the seeker highly influences the design of the control algorithms and the attack precision of the missile. Before the missile with seeker mounted on is launched, the performance of the seeker needs to be accurately evaluated, especially for the expensive ones. Focusing on the problem, a millimeter wave seeker evaluation method is proposed based on the differential global positioning system (DGPS) principle. Firstly, the parameters of the line-of-light (LOS) rates and the missile to target distance are calculated with the data obtained by the DGPS. Then, the results are compared to the ones that are outputted by the seeker itself. The effectiveness of the proposed algorithm is verified on the real seeker data, comparisons with the inertial navigation system (INS) further demonstrate the advantage of the proposed method.

When the airborne or missile borne radar works at the forward-looking imaging mode, the common techniques for improving azimuth resolution are invalid, because the difference between the Doppler frequencies of targets in different azimuths is very small. Meanwhile, other real beam sharpening methods only have very limited effect on azimuth resolution enhancement. For the problem of forward-looking imaging of ship targets at sea surface, a L1/2regularization based azimuth resolution enhancement algorithm is proposed in this paper. This algorithm can make full use of the obvious sparsity in the imaging area. The linear observation signal model for forward-looking imaging is built, and the iterative calculation process of L1/2regularization and the detailed steps of multi-channel radar forward-looking imaging are provided in this paper. Finally, the effectiveness of the proposed algorithm is tested and verified with simulation data and real data.

As a new application platform of synthetic aperture radar (SAR), the near-space hypersonic vehicle has a more serious range migration problem than the traditional airborne platform due to its curved-flight motion characteristic, and its imaging results will be further affected under the condition of high resolution demand. Since the SAR slant range of hypersonic platform is relatively complicated, the azimuth and range in the two-dimensional spectrum obtained by means of traditional wavenumber domain imaging algorithm are mutually coupled. As a result, it is difficult to accurately correct the range migration by interpolation operation. Therefore, an improved wavenumber domain imaging algorithm is proposed in this paper. Through the appropriate approximation of the two-dimensional spectrum of the SAR echo signal, the reference function is designed and the well focus of targets in the observation scene is achieved by the Stolt interpolation. Finally, simulation results are given to verify the effectiveness of the algorithm.

Speckle is a granular disturbance that affects synthetic aperture radar (SAR) images. Over the last three decades, many methods have been proposed for speckle reduction, where a tradeoff between despeckling and detail preservation is required. As an attempt to balance the performance on both sides, in this paper, we propose a 2-D S transform shrinkage algorithm using adaptive soft threshold for SAR image despeckling. It follows the idea of the wavelet shrinkage algorithm, but extends its major steps to take into account the peculiarities of S transform, i.e., adding adaptivity in the estimation of speckle standard deviation and threshold function, in an optimized computation procedure. Homogeneous and heterogeneous SAR images are used for quantitative evaluations, and both vintage and prevailing algorithms are used for comparison, which demonstrates the validity of the proposed method. Additionally, some instructive pieces of advice are given on the selection of suitable parameters of the proposed method under different circumstances.

As a very recent technique for time-series analysis, singular spectrum analysis (SSA) has been applied in many diverse areas, where an original 1-D signal can be decomposed into a sum of components, including varying trends, oscillations, and noise. Considering pixel-based spectral profiles as 1-D signals, in this letter, SSA has been applied in hyperspectral imaging for effective feature extraction. By removing noisy components in extracting the features, the discriminating ability of the features has been much improved. Experiments show that this SSA approach supersedes the empirical mode decomposition technique from which our work was originally inspired, where improved results in effective data classification using support vector machine are also reported.

In this paper, an estimation method of micromotion parameters for free rigid targets using micro-Doppler (mD) features is investigated. These parameters include spin rate, precession rate, nutation angle, and inertia ratio. They represent the microdynamic characteristics and intrinsic properties of targets. The time variation of mD frequency is found complicated yet valuable to estimate the micromotion parameters. From the viewpoint of the spectra of mixed mD time-frequency (TF) data sequences, the theoretical analysis and mathematical derivation are conducted in detail according to the scatterer distribution of rigid bodies. We then present an approach to realize the micromotion parameter estimation from radar mD echoes. It mainly consists of TF transform, TF image processing, mixed mD TF data sequence formation, and spectral estimation. Simulation experiments and result discussion are carried out to demonstrate the effectiveness of the proposed estimation method.

Radar micro-Doppler (mD) provides a promising approach to parameter estimation and classification of micro-dynamic targets. The micro-motion states and characteristics of instantaneous mD frequency are investigated to free symmetric rigid bodies. They are found to take the precession motion, and thus induce complicated mD features with non-sinusoidal variation. Based on theoretical analysis of the spectral structure of mD time-frequency (TF) sequence, this paper proposes a rotation parameter estimation method for complicated micro-motions. The estimation method includes TF analysis, spectrogram processing, projection mapping and spectral estimation. The spin and precession rates of micro-dynamic targets can then be extracted from their radar echoes. Finally, the effectiveness of the proposed method is verified by Monte-Carlo simulations and further discussion.