Millimeter-wave (mmWave) radar has attracted increasing research interest for contactless gesture recognition. However, in practical deployment, models trained on existing users often exhibit significant performance degradation when applied to unseen users. This decline arises because each user constitutes a distinct domain with unique behavioral patterns and physiological characteristics, leading to severe cross-domain distribution shifts in radar signal features. To address these challenges, we propose a two-stage few-shot gesture recognition adaptation method. During pre-training, a dual-prototype consistency strategy is proposed to explicitly align user-specific sub-distributions within each gesture class, overcoming the limitation of conventional methods that treat multiple source users as a single monolithic domain and thereby enhancing the model’s transferability to unseen users. In the adaptation stage, we perform source-free and lightweight fine-tuning using only a few labeled samples from the target domain, while avoiding the computational burden and privacy concerns of joint training. Specifically, a variational autoencoder (VAE)–based latent-space regularization with z-space alignment is employed to achieve stable and accurate recognition for the new target domain. Furthermore, Knowledge Distillation (KD) and L2 regularization toward the starting point (L2-SP) are incorporated to mitigate overfitting and prevent catastrophic forgetting of the source gesture knowledge. Experiments on a multi-subject mmWave gesture dataset show that the proposed method significantly improves recognition performance for the target domains and maintains high accuracy on the original users. Visualization analyses further confirm effective latent-space semantic alignment with minimal supervision.

In complex and dynamic synthetic aperture radar (SAR) scenes, few-shot detection of novel classes suffers from sample scarcity and significant distribution differences between base and novel class features, leading to severe bias and poor generalization in existing few-shot object detection (FSOD) models. To address this issue, we propose a meta-transfer learning method based on dynamic semantic guidance (DSG). This approach combines the strengths of meta-learning and transfer learning, comprising three modules: semantic guidance (SG), distribution alignment metric (DAM), and global feature dynamic aggregation (GFDA). The SG module generates guided features with query semantic information to reduce the distribution gap between base and novel classes, dynamically adapting to few-shot novel class SAR targets. The DAM module applies adversarial training to achieve dynamic feature distribution alignment, improving model bias and generalization. The GFDA module dynamically aggregates and retains critical feature information, enhancing model detection performance. Experimental results on the SRSDD-v1.0, MSAR-1.0, and SAR-AIRcraft-1.0 datasets show that the DSG method outperforms state-of-the-art methods in the SAR field (GMFBA) and the optical domain (G-FSOD), with average detection performance improvements of 1.21%, 1.45%, 1.44%, and 9.76%, 2.86%, 1.8%, respectively.

As a widely used communication signal, orthogonal frequency division multiplexing (OFDM) signal has good sensing performance. It is the key technology of the integration of sensing and communication (ISAC), which is the potential core technology of the sixth generation mobile communication. The fusion of Multiple input and multiple output (MIMO) technology and OFDM signal was conducted to improve the communication speed and imaging resolution of ISAC system. In our work, the multi-signal classification (MUSIC) algorithm is adopted to process MIMO-OFDM data and achieve time of arrival (TOA) and direction of arrival (DOA) estimation. Based on the simulations and experiments in a variety of multi-object scenes, it shows that the MUSIC algorithm can restore the position of the objects more accurately and more robustness than traditional ways.

In this paper, an azimuth velocity estimation method based on spectral peak measurement-moving target shadow detection (SPM-SD) is proposed. Compared with the existing methods, this method takes into account the effect of the moving target position on the doppler shift and improves the accuracy of the azimuth velocity estimation value. The doppler modulation frequency of the azimuth phase is then estimated by using the fractional Fourier transform (FrFt), which in combination with the azimuth velocity gives an estimation of the distance velocity of the moving target. Finally, the estimation results are used to construct the first-order and second-order phase compensation functions for azimuth phase compensation, and the phase gradient autofocus algorithm (PGA) is used to compensate for the residual quadratic and higher phase errors. The real CSAR data processing results prove the effectiveness of the proposed method and greatly improve the imaging quality of the moving target.

High precision and high efficiency motion error compensation is a very challenging work for Terahertz synthetic aperture radar (THz-SAR) imaging, the tiny vibration of platform will make seriously THz-SAR image defocused. In this paper, a new compensation method based on the division of range sub-band conjugate, name as SBCM, is presented for THz-SAR effective motion correction. In the scheme, we use image entropy to iterate the overlap rate of the divided range sub-bands of THz-SAR echoes, and conjugate multiplying the division range-compressed results to obtain a more ideal imaging effectively. Both simulation and experiment results demonstrate the effectiveness of SBCM method. Compared with traditional echo-derived phase estimated autofocusing method, SBCM method not require parameter estimation and its arithmetic is small.

This paper introduces a method based on sub-aperture division for estimating the cross-track velocity of moving targets in terahertz circular synthetic aperture radar (CSAR). By carefully choosing the number of sub-apertures, the complete aperture is divided into multiple smaller apertures. In the sub-aperture regime, the small-angle approximation is applied to separate the cross-track velocity from other parameters related to moving targets. This approach allows for precise estimation of the cross-track velocity, facilitating accurate repositioning of the moving target. The effectiveness of the proposed method is confirmed through experimental results, evidencing its practicality.

The Traditional Range-Doppler algorithm is completed by two-dimensional matched filtering, so there are often sidelobes which may affect synthetic aperture radar (SAR) imaging performance. However, recent studies have shown that CP-OFDM SAR imaging algorithm, which using orthogonal frequency-division multiplexing (OFDM) signals with sufficient cyclic prefix (CP), can obtain ideally zero sidelobes and build an inter-range-cell interference (IRCI)-free SAR image, but with a constraint that the module of each subcarrier’s coefficient should be constant and nonzero. Considering some practical applications, the subcarriers within a certain bandwidth may not necessarily be continuously allocated, thus "0" may appear in the frequency domain vector of an OFDM signal, making it difficult to complete range compression. In this paper, we combine the existing CP-OFDM SAR imaging algorithm with generative adversarial imputation nets (GAIN) to solve this problem, and simulation results are presented to illustrate the performance of this method.

This letter addresses the problem of changes in target shape and structure during imaging caused by different height structures being focused at the same height in the terahertz (THz) band. A new circular synthetic aperture radar (SAR) imaging algorithm based on the extraction of target structure scattering characteristics is proposed to solve this phenomenon. First, a circular SAR (CSAR) imaging model is established for different heights. The target was imaged at different heights, resulting in a series of image sequences in different height and azimuth directions. Then, incoherent tracking of the backscattered energy is used to search for effective scattering centers at different heights. The scattering characteristics curves of different structures are obtained based on the effective scattering centers. Finally, the effectiveness of the proposed algorithm is verified by simulation and measured data. The proposed algorithm has accurately focused the target, while also enhancing the details. In addition, the contrast-to-noise ratio of the imaging results has been improved by 4.87% compared to traditional algorithms.

A terahertz (THz) airborne synthetic aperture radar (SAR) with a carrier frequency of up to 220 GHz is developed. However, due to the short wavelength of THz, the motion compensation (MOCO) algorithm suitable for submeter resolution SAR systems becomes ineffective. To address this issue, we propose an extended Kaiser distribution optimization (EKDO)-based phase compensation algorithm for airborne THz SAR imaging. First, a subaperture division strategy is employed to divide the full-aperture data into multiple subapertures. The envelope error and phase error are roughly compensated using the combination of inertial measurement unit (IMU) and global positioning system (GPS) data. Then, the linear error of the compensated echo is estimated using the Doppler centroid estimation. Subsequently, the EKDO MOCO algorithm proposed in this article is used to compensate for the quadratic phase error. In the third step, residual errors are compensated using a high-order phase error estimation (HPEE). Finally, real data from experiments conducted in four different types of scenarios all confirmed the effectiveness and validity of the algorithm.

Terahertz (THz) circular synthetic aperture radar (CSAR) demands higher precision in motion compensation (MOCO), posing significant challenges. To address this issue, this article proposes a multichannel MOCO algorithm based on adaptive subaperture division (MCAAD). First, the adaptive aperture division (AAD) algorithm is used to divide the image into multiple subapertures. Within each subaperture, initial coarse compensation is performed using data recorded by the inertial navigation system (GPS/INS). Subsequently, high-frequency vibration errors are compensated using the interferometric phase of different channels. Then, an image reconstruction algorithm is employed to image the compensated results, and a residual error estimation (REE) algorithm is applied to compensate for high-order phase errors and residual envelope errors, yielding well-focused subaperture images. Finally, the subaperture images are fused using a base-4 registration algorithm. The simulation and experimental results with measured data demonstrate that the proposed algorithm effectively compensates for THz CSAR imaging.

Synthetic Aperture Radar (SAR) targets often exhibit characteristics such as high mobility and strong concealment, resulting in scarce SAR data and the manifestation of few-shot data properties. These few-shot SAR targets are susceptible to interference from complex background information and mutual interference of target features, making it challenging to distinguish SAR targets from the background. Additionally, there is confusion in features among different targets, leading to models being highly insensitive to few-shot SAR targets under complex distribution conditions in new tasks. Similarly, these few-shot SAR targets exhibit significant sample scarcity and sample variations, resulting in pronounced fluctuations in class centers and difficulty in determining sample distributions. This leads to challenges in accurately representing the potential representative features of few-shot SAR targets by the model. To address these issues, further enhancement of SAR target features is necessary to provide a robust foundation for the ultimate aggregation module. Therefore, based on the meta-learning paradigm, we propose a method for few-shot target detection in SAR imagery via intensive meta-feature aggregation (IMFA), aiming to reinforce SAR target features for improved representation. Specifically, firstly, we propose a novel hierarchical multi-head cross attention (HMCA) to capture global multiscale contextual information in different subspaces and analyze representative features between different targets to distinguish SAR targets from the background. Then, based on HMCA, we introduce a novel feature coupling module (FCM) to couple support features with cognitive information from the query image on the support branch. This is done to reduce the confusion and mutual interference of features between targets while enhancing the model’s generalization ability on new tasks. Finally, on the support branch with query-aware information, we construct a Gaussian distribution to estimate the class distribution of few-shot SAR targets and replace traditional class prototypes. On this basis, we propose the feature information maximization module (FIMM) to avoid feature information shift, greatly strengthening the expression of potential features. Through these steps, reinforced meta-features can be obtained, enabling efficient aggregation. Experiments on the SRSDD-v1.0 and MSAR-1.0 datasets demonstrate that our method has consistently outperformed state-of-the-art approaches in all configurations, achieving state-of-the-art performance.

Synthetic aperture radar (SAR) images are difficult to acquire, and the number of images of different targets often varies greatly, resulting in a large number of imbalanced class distributions in practical applications. Existing classification models usually focus too much on the classes with a large number of samples and less on the minority classes with higher-value, which leads to the degradation of classification performance. A novel method for imbalanced classification in SAR images based on reinforcement learning adaptive cost adjustment is proposed in this paper. The method can adaptively search a cost factor and adjust it continuously according to the predicted classification effect so that the performance of all classes of samples is the best possible. This method can obtain a more accurate cost factor and modify the decision boundary of the classifier to achieve a more accurate classification of the minority classes. Experiments on the MSTAR dataset show that the proposed method achieves binary and multi-classification, significantly alleviates the effect of imbalanced data, shows better classification results on all kinds of samples, and outperforms existing methods in overall performance.

In order to rapidly improve the automatic target recognition (ATR) system when new unknown samples are constantly captured, it is necessary to examine the existing training samples and recognition model so that the ATR system could autonomously assess new unknown samples with low predictive reliability during the recognition process and learn them preferentially. Incremental learning methods generally consider forming key exemplar set from existing known samples, but rarely managing updates of unknown samples. In this paper, an incremental samples’ evaluation and management method from the perspective of distribution reliability (DRaIL) is proposed, which realizes the retention of existent reliable exemplars and the predictive-reliability-assessment-based updating of new unknown samples simultaneously. DRaIL preserves the prior distribution in the high-density and overlap regions first, and then the classification reliability and “in-of-distribution" reliability of new unknown samples are evaluated based on the consistency between the new and the preserved distribution. Updating the new samples with low reliability using new labels could rapidly improve the classification surface and add new classes. Experimental results for the practical incremental learning scenario demonstrate the validity of the proposed DRaIL on representative exemplar selection and reliability ranking performance.

Bistatic synthetic aperture radar (BiSAR) has been attracting worldwide attention because of its forward-looking imaging and high anti-interference. In harsh environment, it is vital for BiSAR to conduct extensive surveillance, imaging, and recognition of maritime ship targets. However, under the disturbance of sea waves, the ship target has an unknown and massive three-dimensional (3-D) rotation, so that its imaging projection plane (IPP) is also undetermined. Thus, high-dimensional random distortion appears in imaging results, making it difficult to recognize the target through two-dimensional distorted images effectively. To solve these problems, bistatic SAR maritime ship target 3-D image reconstruction method without distortion in local Cartesian coordinate (LCC) is proposed. In this paper, according to positions of scatterers and rotation parameters, significant differences of different scatterers of maritime ship targets have been found in bistatic range, Doppler centroid (DC), and Doppler frequency rate (DFR), which lays a solid foundation for the scatterer separation of ship targets. On this basis, a 3-D R-DC-DFR domain is constructed, and 2-D echoes of the maritime ship target are projected into R-DC-DFR domain to separate scatterers. Then, by remapping data of transmitter and receiver in R-DC-DFR domain to LCC, as well as evaluating their similarity metric, the optimal rotation parameters of the ship target can be obtained via the maximal similarity. Therefore, the image distortion caused by the unknown IPP has been removed, and 3-D image reconstruction of ship targets can be realized without distortion in the LCC. Furthermore, to evaluate performances of 3-D image reconstruction for different rotation parameters and bistatic configurations, 3-D reconstruction index is proposed and analyzed. Both point-targets and maritime ship targets are simulated to emphasize the effectiveness of the proposed method.

With the rapid development of the remote sensing area, now, we can collect massive data. Labeling polarimetric synthetic aperture radar (PolSAR) data is a labor-intensive and time-consuming work. The performance of the supervised model heavily depends on the number of training samples and there is a large number of unlabeled data unutilized, which contains statistical information of the PolSAR image. To solve this issue, in this letter, an adaptive anchor graph regularization (AdaAGR) is proposed for PolSAR images. The accuracy of the conventional anchor graph-based method is dependent on the choice of the number of nearest anchors. Applying a global number of nearest anchors to the anchor graph method ignores the local data density. Low data density corresponds to a small number of nearest anchors and vice versa. A global number of nearest anchors may limit the performance of classification. In this letter, an adaptive anchor graph strategy is proposed. First, cluster centers generating from Wishart clustering are served as graph anchors. Second, the distances between the data point and its closest anchors are calculated in ascending order. Then, an optimal number of nearest anchors is selected for every pixel. Third, the similarity between data and its neighboring anchors is measured, and then, an anchor graph is constructed. Finally, the anchor graph is regularized and the unlabeled samples are predicted. Experimental results on two real PolSAR data sets show that the classification accuracy can be further improved by employing the proposed adaptive anchor graph strategy.

Synthetic aperture radar (SAR) human-imaging technology has been widely used in the fields of security screening and activity recognition. However, most of the existing methods aim at stationary human targets, resulting in severe restrictions on their potential applications. In this article, an SAR imaging method for walking human is proposed with short aperture terahertz (THz) radar. This method is based on a modified wavenumber domain approximate algorithm ($\text{m}{\omega }$ka), whose interpolation mapping is modified according to the approximation of region of interest (RoI). Because the nonrigid motion phase error caused by the walking human would lead to severe deformation and blur in imaging results, a compensation processing is essential. To figure it out, the fractional Fourier transform combined with the maximum and minimum generalized likelihood ratio test (FrFT-mmGLRT) is devised in this article. Within a short aperture time, the nonrigid motion of walking human can be approximately assumed as a superposition of the rigid movements of different human body parts. Particularly, by taking advantage of the superposition property of FrFT, the motion phase errors of different human body parts are distinguished and estimated by jointly searching for the peaks of FrFT energy spectrum. Furthermore, the mmGLRT-based composite imaging technique is utilized to obtain a whole body result from the compensated results of different body parts. Finally, numerical simulation and real experiments are conducted to verify the feasibility and capability of the proposed method for walking human SAR imaging.

Bistatic synthetic aperture radar (BiSAR) has received more and more attentions because of its forward-looking imaging capability and configuration flexibility. For BiSAR moving target imaging, its non-cooperative motion leads to unknown range cell migration (RCM) and additional phase modulation. Consequently, moving target imaging in BiSAR face two main challenges: 1) The unknown RCM correction and Doppler parameter estimation are tightly coupled. 2) The Doppler parameters of the extended moving target’s different scattering points are different, i.e., the Doppler parameters are spatially variant. To cope with these problems, an optimal polar format refocusing method for bistatic SAR moving target imaging is proposed. First, the main part of tight coupling and spatial variation effects caused by the BiSAR platforms are eliminated, while the moving target is two-dimensional (2-D) defocused and shifted. Then, we analyze the characteristics of two-dimensional defocused and shifted of the moving target in BiSAR, and give the analytical expressions. On this basis, a new bistatic polar format transformation is introduced, in which the degree of freedom of defocusing result is reduced from 2-D to only one-dimension. After that, the parameter estimation and refocusing issues are transformed into a constrained optimization problem (COP), and differential evolution (DE) is applied to solve the COP and obtain the refocusing results. Finally, considering the spatial variation of the extended moving target, the compensation processing is performed to relocate each scattering point. Numerical simulations verify the effectiveness of the proposed method.

This paper proposes an imaging algorithm with adaptive aperture energy division. This method is suitable for circular synthetic aperture radar (CSAR). The algorithm is an extension of the traditional CSAR polar format algorithm (PFA). Firstly, to avoid the loss of image detail information caused by the mismatch between the aperture size and the energy distribution in the traditional self-aperture division, a method based on the aperture energy to divide the sub-aperture is proposed. Then, according to the PFA algorithm image the sub-apertures separately, and perform incoherent fusion of the imaging results to quickly obtain high-resolution imaging results. Finally, simulation results validate the feasibility of the proposed approach.

Mainstream CNN-based SAR ship detectors are prone to produce false targets in the land area, which has a great relationship with the anchor generation mechanism in the network structure. The anchor generator will indiscriminately generate a set of anchors for each point on the predicted feature map and map it back to the original image. These anchors will become the initial candidate boxes after screening. Considering that ship targets in SAR images are usually small and sparsely distributed, this dense anchor generation method can easily generate candidate boxes in land areas. To solve this problem, this paper proposes a method to guide the generation of anchors through image area information, called selective anchor generator (SAG). Unlike previous anchor generator, SAG will do sea-land segmentation for image first and then establish a mapping relationship with the feature map. Points on the feature map that only contain land feature information are marked as negative points with no anchors being generated. In this way, the number of candidate prediction boxes in the land area is greatly reduced, thereby achieving the effect of false alarm suppression. Experiments on test data show that this method can effectively reduce the number of false targets generated in land areas.

In SAR images, there are objects with large scale difference, which is called cross-scale objects. For example, there are both large-scale airport objects and small-scale ship objects in SAR images. However, the current multi-scale object detection methods are difficult to detect objects with large scale difference. To address this issue, we propose a cross-scale object detection method for SAR images based on Scale Expansion Pyramid Network(SEPN) in this paper. The proposed SEPN can extract the salient features of the objects with a large scale difference, and by closely connecting the scale expansion layer with the convolutional layer during the downsampling process of the Feature Pyramid Network (FPN), the receptive field of the feature extracted by the convolutional layer can be adaptively extended, and finally it achieves the effect of cross-scale object detection in SAR image. Experiments on SSDD dataset and Gaofen-3 dataset show the effectiveness of our proposed methods in detecting objects of different scales in different scenes of SAR images.

When adding new tasks/classes in an incremental learning scenario, the previous recognition capabilities trained on the previous training data can be lost. In the real-life application of automatic target recognition (ATR), part of the previous samples may be able to be used. Most incremental learning methods have not considered how to save the previous key samples. In this article, the class boundary exemplar selection-based incremental learning (CBesIL) is proposed to save the previous recognition capabilities in the form of the class boundary exemplars. For exemplar selection, the class boundary selection method based on local geometrical and statistical information is proposed. And when adding new classes continually, a class-boundary-based data reconstruction method is introduced to update the exemplar set. Thus, when adding new classes, the previous class boundaries could be kept complete. Experimental results demonstrate that the proposed CBesIL outperforms the other state of the art on the accuracy of multiclass recognition and class-incremental recognition.

Noncontact gesture recognition is gradually being applied to emerging applications, such as smart cars and smart phones. Negative latency gesture recognition (recognition before a gesture is finished) is desirable due to the instantaneous feedback. However, it is difficult for existing methods to achieve a high precision and negative latency gesture recognition. A fragment can provide too few features to directly identify all gestures well. By observing a large number of existing gesture sets and people's daily operating habits, we found that some high frequency used gestures are similar. To the best of our knowledge, it is the first time to redivide the gestures into two subsets according to their movement physical states. We divided the gestures with different shapes or motion states into a parent-class subset, and further divided each pair of parent-class gestures to obtain a child-class subset. In order to achieve a better tradeoff between the high-precision and negative latency, an approach of motion pattern and behavior intention (MPBI) is proposed. Taking full advantage of the characteristics of each subset, MPBI includes two models. First, pattern model coarsely classify the parent-class gestures by a convolutional network, and then intention model further classifies child-class gestures according to their opposite motion direction. MPBI is evaluated on a 340-GHz terahertz radar. With the advantage of its accurate ranging, intention model can recognize child-class gestures directly without training. MPBI is evaluated on 12 gestures and achieves a recognition accuracy of 94.13%, which only needs a 0.033-s gesture fragment as an input sample.

Gabor wavelets filters with different orientations and scales were applied on SAR image as a feature extraction technique. However, due to the different characteristics of the constructed Gabor filters, different Gabor features could have different impact on material representation, influencing the recognition rate eventually. In this paper, a novel Gabor weight optimization based multi-task sparse representation is proposed for synthetic aperture radar (SAR) image target recognition. First, each Gabor feature is sparsely represented over the corresponding set of Gabor features of all training samples under multi-task sparse representation framework. Then, the weights of multi-task representation are optimized by a least-squares optimization with l2-norm regularization according to the loss function defined by the classification results of the classifiers. The final classification results are acquired by a weighted fusion strategy. Experiment results prove the effectiveness of the multi-task sparse representation method based on weight optimization.

We propose a fast algorithm to circular synthetic aperture radar (CSAR) imaging for arbitrary region of interest (ROI). This algorithm is an extension of traditional CSAR polar format algorithm (PFA). Firstly, to avoid severe defocus and distortion of off-centered ROI image caused by traditional CSAR PFA plane-wave approximation error, a novel PFA matched filter kernel is derived with ROI characteristic reference function. Then, according to the wavenumber support domain approximation of ROI, a new interpolation mapping relation is defined to obtain uniform rectilinear wavenumber domain data from polar format echo data. With the rectilinear data, the imaging result of ROI can be obtained by two dimensional inverse fast Fourier transform.

Due to the merit of low cost and flexible flight, small drones are showing tremendous application prospect in both military and civil fields. Aiming at public area monitoring, a synthetic aperture radar (SAR) imaging model using drone fleet borne radars is proposed. In this model, the radars illuminate the imaging area while the drones fly forward straightly. To make the implementation easier, we assume that each radar transmits signal and receives echo independently. Furthermore, the frequency-division (FD) signals are adopted for different radars to avoid interference. With broadband signal, synthetic aperture via the motion of the drones and linear array formed by the multiple radars, three-dimensional (3D) imaging can be realized. Numerical simulations are conducted to verify the effectiveness of the proposed model and the 3D image formation method.

Previous convolutional neural networks (CNNs) used for synthetic aperture radar (SAR) target recognition are over-parameterized which limits their application in real-time radar recognition systems. To solve this problem, a micro convolution neural network (MCNN) for SAR target recognition is proposed in this paper. Our MCNN is compressed from a deep convolutional neural network (DCNN) with 18 layers by a novel knowledge distillation algorithm. The experiments on MSTAR dataset show that the proposed MCNN can obtain the recognition rate of 98.2%. This recognition rate is almost the same as the DCNN. However, compared with the DCNN, the memory footprint of the proposed MCNN is compressed by nearly 177 times, and the calculated amount is nearly 12.8 times less, which means that the proposed MCNN can obtain a better performance with the smaller network.

The scales of different ships vary in synthetic aperture radar (SAR) images, especially for small scale ships, which only occupy few pixels. So ship detection methods currently face difficulties in detecting multiscale ships. A novel method for multiscale ship detection in SAR images based on Dense Attention Pyramid Network (DAPN) is proposed in this paper. It can extract multiscale and salient features by DAPN, which densely connects Convolutional Block Attention Module (CBAM) to each feature map from top to down of the pyramid network. Then the fused feature maps are fed to the detection network for multiscale ship detection. Experiments on SSDD dataset show a better performance of this method to detect multiscale ships in different scenes of SAR images.

The scales of different ships vary in synthetic aperture radar (SAR) images, especially for small scale ships, which only occupy few pixels. So ship detection methods currently face difficulties in detecting multiscale ships. A novel method for multiscale ship detection in SAR images based on Dense Attention Pyramid Network (DAPN) is proposed in this paper. It can extract multiscale and salient features by DAPN, which densely connects Convolutional Block Attention Module (CBAM) to each feature map from top to down of the pyramid network. Then the fused feature maps are fed to the detection network for multiscale ship detection. Experiments on SSDD dataset show a better performance of this method to detect multiscale ships in different scenes of SAR images.

Previous ship detection methods for synthetic aperture radar (SAR) images suffer from an extreme variance of ship scale. The problem of large scale variation across ships lies in the heart of ship detection. In this paper, scale-transferrable pyramid network for multi-scale ship detection in SAR images is proposed. We construct a feature pyramid network by lateral connection, and densely connect each feature maps from top to down using scale-transfer layer. Lateral connection injects more semantic information into feature maps with high resolution. Dense scale-transfer connection can expand the resolution of feature maps and explicitly explore information contained in channels. Finally, we can detect multi-scale ships by combining these multi-scale feature maps. Experimental results demonstrate that our network outperforms the state-of-the-art methods.

The Synthetic Aperture Radar (SAR) target detection using Adaptive Boosting (AdaBoost) based on Haar-like feature is accelerated via Graphics Processing Unit (GPU) in this paper. As a machine learning algorithm, AdaBoost has achieved great success in the field of target detection. However, due to the time-consuming training process, it is difficult to achieve real time requirements, which limits its further development. In this paper, based on the analysis of the algorithm, the AdaBoost algorithm based on Haar-like feature is parallel decomposed and then implemented by using Moving and Stationary Target Recognition (MSTAR) dataset to improve the detection efficiency. First, the AdaBoost algorithm based on Haar-like feature is investigated. Then, in order to improve the detection speed, the algorithm is parallel decomposed. Finally, the algorithm is implemented by Compute Unified Device Architecture (CUDA) for acceleration to see the acceleration effect. According to the experiments, the time spent on the training process and the testing process has been greatly reduced by using CUDA. Compared to the traditional CPU-based AdaBoost algorithm based on Haar-like feature, the algorithm using CUDA parallel computing can achieve a speedup of 30.

The performance of vehicle detection system is often affected by both internal and external noise. In this paper, a novel vehicle detection scheme called vehicle detector based on EMD-HT and multichannel GLRT (V-EHMG) is proposed, which composed of signal denoising part based on empirical mode decomposition (EMD) and signal detection part which takes advantage of the spatial–temporal relationship acquired by multiple sensors. In the first part, we propose a new denoising algorithm null-hypothesis interval threshold EMD denoising (EMD-HT) to check the similarity between the probability density function of the decomposed intrinsic mode functions (IMFs) from input signal and the generalized Gaussian distribution to determine relevant IMFs for signal reconstruction. Combined the interval threshold method, the denoised signals are acquired. In the second part, we use multichannel generalized likelihood ratio test (GLRT) detector to judge the presence of other vehicles with the denoised signals as the input. Simulation and physical experimental results show that with V-EHMG, the signal-to-noise ratio of the signal is doubled and the detection range is improved more than 40% compared with the traditional methods.

Video synthetic aperture radar (ViSAR) is a novel imaging model to persistently surveil the region of interest by a series of consecutive images in real time. The formation time of one frame image, which can influence the image latency, is one of the key factors to realize ViSAR system. In general, the polar format algorithm (PFA) is preferred for ViSAR imaging. However, the 2-D resampling in the PFA has a large computation burden and is complex to realize in the real-time processing system. It has become the main obstacle to reduce image latency. Therefore, we derive four judgment criteria in this letter. According to these criteria, we propose the improved method of the PFA. Based on this method, the modified PFA can reduce the image formation time while guaranteeing the image quality. Simulation results are presented to validate the effectiveness of the improved method proposed in this letter.

Incremental learning methods update the existing model with new knowledge when the target data increase continuously. Open set recognition (OSR) algorithms provide classifiers with a rejection option so that the new untrained target type is identified. In this paper, an open set incremental learning method is introduced for automatic target recognition, which is able to recognize and learn the new unknown classes continually. The proposed method, open set model with incremental learning (OSmIL), is an ensemble classifier so it is able to be updated only by the new data. For saving the computational time and storage source, a new exemplar selection method is introduced for model simplifying. Edge samples are selected to cover training classes; as a result, the model size is deduced and controlled. Moreover, because extreme value theory (EVT) is suitable to fit a classification model that includes open space risk, the decision function based on EVT makes an open set classifier for identifying the new classes. Experimental results demonstrate that the proposed OSmIL outperforms the other state of the arts on the accuracy of multiclass OSR. And OSmIL can maintain good accuracy and efficiency in the incremental learning experiment set.

For conventional polar format algorithm (PFA), because of the image distortion and defocus caused by the plane-wave assumption, the effective imaging scene is bounded to a small region near the reference point. In this paper, refocusing and zoom-in polar format algorithms (RZPFAs) for curvilinear spotlight synthetic aperture radar (SAR) imaging are proposed, which can produce refocused image for an arbitrary region of interest (ROI). First, refocusing is implemented by a phase compensation of the already dechirped signal with respect to the new refocusing point. The relation [named refocused distortion mapping (RDM)] between targets' actual locations and their reconstructed locations in the refocused image is then derived. Based on the RDM, the distortion-negligible region (DiR) and defocus-negligible region (DeR) are defined as extents within which the residual distortion and residual defocus are less than some preselected thresholds. When the ROI is within both the DiR and the DeR, zoom-in imaging based on nonuniform fast Fourier transform of type-1 (NuFFT-1) is sufficient to form a desired image, which is named RZPFA based on NuFFT-1 (RZPFA-1). However, when the ROI exceeds the DiR, zoom-in imaging based on nonuniform fast Fourier transform of type-3 (NuFFT-3) should be selected, which is named RZPFA based on NuFFT-3 (RZPFA-3). With just a small amount of extra computation than RZPFA-1, RZPFA-3 can realize pixel-based quasi-orthorectified imaging directly. Besides, the proposed algorithms can also be extended to wide-area persistent imaging where ROI is larger than DeR. The simulation and real data results demonstrate the effectiveness of the proposed algorithms.

The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods easily result in inaccurate detection due to the complex scenes and multiplicative speckle noise. Therefore, airport detection from SAR images is still a challenging task. In order to limit the influence of unnecessary and attractive details and noise, we propose a multi-layer abstraction saliency model for airport detection in SAR images in this paper. Specifically, we first obtain airport support regions and superpixels in the first layer. According to the dis-similarity between foreground and background superpixels, airport components are explored by iterative refinement for each airport support region in the second layer. In the third layer, airport adobes are produced by clustering. Based on the characteristics of an airport in SAR images, we propose three saliency cues, including local contrast (LC), adobe deformation (AD), and global uniqueness (GU), to obtain adobe-level saliency. Furthermore, we assign saliency to each pixel by Bayesian inference. Finally, we can explore airport location using integrated saliency map. The proposed approach is tested on an airport data set collected from Gaofen-3, TerraSAR, and RadarSat. Our method achieves 88.89% detection rate. The experimental results demonstrate that the proposed algorithm is effective and outperforms the previously airport detection methods. The code will be available at https://github.com/NengyuanLiu/MyAirportSaliency.

Template-matching-based approaches have been developed for many years in the field of synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of template-matching-based approaches is strongly affected by two factors: background clutter and noise and the size of the data set. To solve the problems mentioned above, a multilevel reconstruction-based multitask joint sparse representation method is proposed in this paper. According to the theory of the attributed scattering center (ASC) model, a SAR image exhibits strong point-scatter-like behavior, which can be modeled by scattering centers on the target. As a result, the ASCs can be extracted from SAR images based on the ASC model. Then, ASCs extracted from SAR images are used to reconstruct the SAR target at multilevels based on energy ratio (ER). The multilevel reconstruction is a process of data augmentation, which can not only restrain the background clutter and noise but also augment the data set. Several subdictionaries are designed after multilevel reconstruction according to the label of training samples. Meanwhile, a test image chip is reconstructed into multiple test images. The random projection coefficients associated with multiple reconstructed test images are fed into a multitask joint sparse representation classification framework. The final decision is made in terms of accumulated reconstruction error. Experiments on moving and stationary target acquisition and recognition (MSTAR) data set proved the effectiveness of our method.

In this paper, a novel multi-task sparse representation (MSR) of the monogenic signal is proposed in order to overcome the misclassification caused by heterogeneity of three components of the monogenic signal. In recent years, the monogenic signal has been applied into the field of SAR image recognition due to its capability of capturing the broad spectral information with maximal spatial localization. The monogenic signal can be decomposed into three components (local amplitude, local phase and local orientation) at different scales. The components are concatenated to three component-specific features and then fed into a MSR classification framework. However, the heterogeneity of the three component-specific features makes it difficult to make decisions by simply counting the accumulated error in multi-task sparse representation classification. To solve this problem, a multi-task learning model based on Fisher discrimination criteria is designed and Fisher score is presented to measure the discriminative ability of three types of component-specific feature in different classes. The final decision is made by weighted accumulated reconstruction error. Experiment results prove the effectiveness of adaptive weighted MSR classification method of monogenic signal.

The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods locate airports by connecting pairs of line segments or directly applying saliency analysis to an entire SAR image. These methods are either time-consuming or can easily result in false detection. Considering these issues, a method using line segment grouping and saliency analysis is proposed in this letter. First, line segments are obtained via an improved line segment detector (LSD). After line segment grouping, airport support regions are extracted. Then, selective nonmaximum suppression is proposed to obtain potential airport regions. Finally, airport regions are located by false alarm control and saliency analysis. Experiments on large-scale SAR images prove that our proposed algorithm has a better performance and higher efficiency in airport detection compared with traditional methods.

The value of the channel state feedback becomes more significant in the upcoming 5G era due to the application of the millimeter-wave (mm-Wave) technologies in 5G. In the meantime, the feedback is challenging in the mm-Wave systems especially with massive MIMO antenna for two reasons: first, the channel states change faster at the millimeter band; second, in the massive antenna system, there are a large number of channel coefficients to report which means the feedback caused overhead will be large. Thus, optimization of the feedback system becomes necessary in the 5G era. In our optimization work, the perfect channel states are assumed to be not accessible at user equipments. Under this assumption, we propose a novel codebook-based-feedback scheme within which the differential channel information is first projected to the codebook, and then the index of the projection in the codebook is fed back. We compare the new scheme with other feedback schemes, such as analog feedback of original states and differential states. From the comparison, we analytically and numerically obtain the insight of the advantage of the proposed scheme. We can also observe that the advantage of the proposed scheme is even more significant in low-SNR conditions.

In high-resolution synthetic aperture radar (SAR) images, targets are often spatially spread, and some separated regions of one target may be detected as different potential targets. The target should be represented by a part model to combine the separated regions into one target. In this letter, a part often means a separated region, and the part model obtains a hierarchical combination of the other parts. Moreover, the bottom hierarchy consists of strong scatters in the local SAR image, which are searched based on compressed intensities. The middle hierarchy consists of parts, which are generated based on connectivity and similarity of scatters in the bottom hierarchy. The top hierarchy delineates the relationship of parts in the middle hierarchy. A search method is designed based on the different features of the elements in each hierarchy. Consequently, the proposed algorithm combines different traits of targets to improve detection, and unites the separated regions of one target in the top hierarchy. This algorithm is validated by SAR images with different resolutions and scenes. scatters connectivity

The rising population of space debris poses an enormous threat to all space vehicles, including space shuttles and other spacecraft with operators aboard; and their detection, tracking, and identification are of great importance. Using the spinning motion and the translational motion component that is parallel to its major axis, we have established a 3-D spiral synthetic aperture radar (SAR) imaging geometry and its corresponding signal model for space debris in this letter. Spatial offsets and coupling in the along-track and altitude directions, caused by the spiral motion of the radar, are compensated in the frequency domain. With these compensation procedures, the spiral SAR is simplified as a cylindrical scan mode. Then, a 3-D wavenumber domain algorithm is proposed to realize coherent imaging. The real data processing results are presented to demonstrate the effectiveness of the proposed algorithm for imaging space debris.

In wireless communication systems, channel state feedback (CSF) is widely used to improve link performance. However, CSF consumes extra system resources and results in transmission overhead. In this paper, we evaluate such resource consumption in terms of bit rate and provide an explicit expression for the lower bound of the CSF overhead. We propose an overhead optimization mechanism under the constraint of channel state reconstruction accuracy. The numerical simulations show that our paper is beneficial to select the optimum CSF parameters, including the average bit number and channel state feedback period. It is also shown that the proposed overhead optimization scheme is able to reduce the system resource consumption with a guaranteed reconstruction accuracy.

The phrase ‘Big Data’ refers to large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future, as defined by U.S. National Science Foundation in its recent solicitation. The research of Big Data will accelerate the progress of scientific discovery and innovation; lead to new fields of inquiry that would not otherwise be possible, encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life. The new knowledge, tools, practices, and infrastructures produced will enable breakthrough discoveries and innovation in science, engineering, medicine, commerce, education, and national security. Big Data presents critical requirements for security in data collection and transmission of selected data through a communication network. This special issue contains 11 papers selected from submissions to the open call for papers on Security in Big Data. These papers highlight some of the current research interests and achievements in the area of security in Big Data. The wide use of high-performance image acquisition devices and powerful image-processing software has made it easy to tamper images for malicious purposes. The paper by Zhang et al. proposes an effective framework for revealing image-splicing forgery. The experiment results show that the proposed method can perform better than some state-of-the-art methods in terms of the detection performance over the Columbia image-splicing detection evaluation data set. Network coding has emerged some exciting future because of its smart technology in wireless sensor networks. At the same time, it is facing security attacks, especially conspiracy attack. The paper by Du et al. proposes a weakly secure scheme from the perspective of topology. Considering the performance of this scheme, an advanced scheme is put forward later. Simulations show that the two strategies can prevent cooperative eavesdroppers from acquiring any useful information transmitted from source node to sink node, and the performance of advanced scheme is better. Traditionally, jamming to the wireless system is a fatal threat to the security of home area networks (HANs), which impedes the two-way data transmission between electric devices and the smart meter and thus deteriorates the reliability of the in-home communication of Smart Grid. The paper by Li et al. incorporates the power line system into the HAN and proposes a hybrid architecture of orthogonal frequency-division multiplexing-based wireless communication and power line communication for the Smart Grid security application. With this new solution, the channel diversity of the HAN is realized, and the communication reliability is still guaranteed even when the wireless channel suffers from jamming. Information of multi-cells is big data because of the enormous quantities of various cells as well as their parameters and status. To securely and efficiently integrate all the cells' information and trace multi-cells are challenging because of varying number of the multi-cells, as well as the complicacy of the multi-cells' movement. The paper by Yin and Sun proposes an automatic big data integration algorithm based on the optical transfer function. The experimental results show that the algorithm can securely and efficiently integrate all the cell information and simultaneously track a large quantity of cells. Real-time digital video presents great challenges on processing and storage and is a typical example in Big Data. How to secure and efficiently transmit digital video is critical. The paper by Zhang et al. uses the distributed compressed sensing to deal with video coding. To reduce the orthogonal matching pursuit algorithm computational complexity, quantum-behaved particle swarm optimization algorithm is used to reconstruct video signal. Simulation results demonstrate that it can obtain the better reconstructed video with low sample value and it can guarantee safety performance. Wireless image sensor network generates a large number of images from the distributed camera sensors. The image data need to be delivered securely and efficiently to the sink in many circumstances. The current node-disjoint multipath and dispersive routings cannot provide enough security and efficiency for the image data collection and transportation. The paper by Su and Hu proposes an ellipse batch dispersive routing algorithm to address the secure and efficient data collection issue in wireless image sensor network. The smart grid system is composed of the power infrastructure and communication infrastructure and thus is characterized by the flow of electric power and information, respectively. The 24/7 information collection and transmission in smart grid is a good example of Big Data. The transmission of Big Data in smart grid needs wireless network, which introduces additional vulnerabilities, given the scale of potential threats. Therefore, the physical layer security issue is of first priority in the study of smart grid and has already attracted substantial attention in the industry and academia. The paper by Wang et al. aims to present a general overview of the physical layer security in wireless smart grid and covers the effective countermeasures proposed in the literature of smart grid to date. Security is a very broad topic; particular attention has been paid in communications, networking on security issues. However, in practical applications, providing security services increases the computation and the occupation of system resources. This problem is particularly important when energy is a limited resource for mobile communication devices operating on battery. Thus, energy-efficient security devices are very necessary for the communication. The paper by Yuan and Liang designed a new low voltage, low power consumption comparator for successive approximation register analog to digital converter to improve the energy efficiency in the problem of secure communication. Big data presents critical requirements for security in data collection and transmission of selected data through a communication network. The paper by Chen et al. presents a new secure transmission for big data based on nested sparse sampling and coprime sampling. With nested sampling and coprime sampling, besides the advantage of higher spectrum efficiency, big data could also achieve higher power spectral density for binary frequency shift keying signal. It proves that both nested sampling and coprime sampling could be used in big data transmission to resist interference, while guaranteeing the transmission performance. With the rapid adoption of cloud storage services, a great deal of data is being stored at remote servers, so a new technology, client-side deduplication, which stores only a single copy of repeating data, is proposed to identify the client's deduplication and save the bandwidth of uploading copies of existing files to the server. It was recently found, however, that this promising technology is vulnerable to a new kind of attack in which by learning just a small piece of information about the file, namely, its hash value, an attacker is able to obtain the entire file from the server. The paper by Yang et al. proposes a cryptographically secure and efficient scheme for a client to prove to the server his ownership on the basis of actual possession of the entire original file instead of only partial information about it. The paper by Wang et al. presents the definitions of big data and anomaly detection. The theory of ultra-wideband radar and the through-wall detection of a human model based on ultra-wideband radar are briefly introduced. The target criterion with wavelet packet transform is deduced, and the procedure for the through-wall human detection with statistical process control is constructed. The radar echo signals are collected at stationary and moving statuses of a human being for three types of walls. The experimental results demonstrate the effective of through-wall target detection based on the proposed algorithm. We would like to thank all authors for contributing papers to the special issue. We appreciate the staff of Security and Communication Networks for their support in editing this special issue. Qilian Liang is a University Distinguished Scholar Professor in the Department of Electrical Engineering, University of Texas at Arlington. He received the BS degree from Wuhan University in 1993, MS degree from Beijing Uni- versity of Posts and Telecommunica- tions in 1996, and PhD degree from University of Southern California (USC) in May 2000, all in Electrical Engineering. Prior to joining UTA in August 2002, he was a Member of Technical Staff in Hughes Network Systems Inc. at San Diego, California. His research interests include wireless sensor networks, wireless communications, signal processing, information theory, radar systems, and wireless networks. Dr. Liang has published more than 270 journal and conference papers. He received 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, 2003 U.S. Office of Naval Research (ONR) Young Investigator Award, 2005 UTA College of Engineering Outstanding Young Faculty Award, 2007, 2009, 2010 U.S. Air Force Summer Faculty Fellowship Program Award, 2012 UTA College of Engineering Excellence in Research Award, 2013 UTA Outstanding Research Achievement or Creative Activity Award, and was inducted into UTA Academy of Distinguished Scholars in 2015. Jian Ren received the BS and MS degrees both in mathematics from Shaanxi Normal University and received the PhD degree in EE from Xidian University, China. He is an Associate Professor in the Department of ECE at Michigan State University. His current research interests include cryptography, network security, energy efficient sensor network security protocol design, privacy-preserving communications, secure and efficient cloud computing, and cognitive networks. He is a recipient of the US National Science Foundation Faculty Early Career Development (CAREER) award in 2009. Dr. Ren is a senior member of the IEEE. Jing Liang received the BS and MS degrees from Beijing University of Posts and Telecommunications, China in 2003 and 2006, respectively, and PhD degree from University of Texas at Arlington in August 2009, all in Electrical Engineering. She is currently a Professor in the Department of Electrical Engineering at University of Electronic Science and Technology of China. Her current research interests include radar sensor networks, collaborative and distributed signal processing, wireless communications, wireless networks, and fuzzy logic systems. Baoju Zhang is a Professor at the College of Physical and Electrical Information, Tianjin Normal Uni- versity. She received the BS degree from Tianjin Normal University in 1990, MS degree from Tianjin Nor- mal University in 1993, and PhD degree from Tianjin University in 2002. She was a Postdoctoral Fellow at Tianjin University from 2002 to 2004. Her research interests include radar sensor networks, digital audio and video technology, image compressing and coding, and compressive sensing. Yiming Pi was born in 1968 in China. He obtained PhD degree in Electronic Engineering from University of Electronic Science and Technology of China in 1993. Since 2002, he has been a Professor of Department of EE, University of Electronic Science and Technology of China. He is a councilor of Signal Processing Society in the Chinese Institute of Electronics and has served in organizing several international conferences in the field of Signal Processing and Radar Systems. He became IEEE Senior Member in 2011. He had been the leaders of some Natural Science Funding of China. He has more than 100 publications in the conferences and journals of IEEE/IET. His research interests are radar imaging, signal processing and terahertz technology, and so on. Chenglin Zhao received his BS degree in Tianjin University in 1986, MS degree and PhD degree in Beijing University of Posts and Telecommu- nications in 1993 and 1997, respec- tively. He is a Professor of the Key Lab of the ubiquitous wireless of Education Ministry, Information and Telecommunication engineering college, Beijing University of Post and Telecommunication. His main research areas include radar sensor networks, wireless broadband interconnection, wireless sensor network, and digital signal processing and its applications.

Abstract The wide use of high‐performance image acquisition devices and powerful image‐processing software has made it easy to tamper images for malicious purposes. Image splicing, which has constituted a menace to integrity and authenticity of images, is a very common and simple trick in image tampering. Therefore, image‐splicing detection is of great importance in digital forensics. In this paper, an effective framework for revealing image‐splicing forgery is proposed. First, the local binary pattern operator is used to model magnitude components of two‐dimensional arrays obtained by applying multisize block discrete cosine transform to test images. Then, all of bins of histograms computed from local binary pattern codes are served as discriminative features for image‐splicing detection. After that, kernel principal component analysis is utilized to reduce the dimensionality of the proposed features to avoid the high computational complexity, high mutual correlation among the constructed features and possible overfitting for support vector machine classifier. Finally, support vector machine classifier is employed to distinguish spliced images from authentic images by using the final dimensionality‐reduced feature set. The experiment results show that the proposed method can perform better than some state‐of‐the‐art methods in terms of the detection performance over the Columbia image‐splicing detection evaluation dataset. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract In this paper, we study the security assurance in application layer in wireless acoustic sensors via event forecasting and detection. In order to perform event forecasting and detection, we try to answer several challenging questions in acoustic signal research based on wireless acoustic sensors: (i) Are acoustic signals predictable? (ii) How are acoustic signals predicted? (iii) Are there any event‐forecasting applications for the security in wireless acoustic sensors? We study these questions based on Xbow acoustic sensors and demonstrate that real‐world acoustic signals are self‐similar, which means that they are predictable. We propose an acoustic signal prediction scheme using interval type‐2 fuzzy logic system (FLS). We show that a type‐2 fuzzy membership function (MF); that is, a Gaussian MF with uncertain mean is appropriate to model the acoustic signal strength. Two FLSs, a type‐1 FLS, and an interval type‐2 FLS are designed for signal strength forecasting. Furthermore, we propose a double sliding window scheme for event detection based on the forecasted signals. Simulation results show that the interval type‐2 FLS outperforms the type‐1 FLS in signal strength forecasting and the performance of event detection based on the forecasted signal from type‐2 FLS is much better than that based on type‐1 FLS. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Satellite‐based apositioning and navigation technology in urban environments is widely studied. As a solution to GPS signal degradation in urban environment, the wireless network‐aided location scheme has been put into practice, which benefits our everyday life. However, there still exist many potential advantageous points that can be exploited to improve the performance of the location service especially in urban environments. This paper proposes a GPS‐based positioning and navigation with the aid of wireless network (GPONAWIN) positioning scheme that utilizes the existing resources of wireless communication networks to provide cheaper and more accurate urban positioning service with higher survivability. After the proposed scheme is described in detail, outperformances over the existing scheme are also analyzed. The GPONAWIN scheme can achieve higher information utilization efficiency, less wireless channel expenditure, more accurate positioning, better expandability to the multiple satellite positioning and navigation systems, and lower cost. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT The signal of a hypersonic spacecraft entering the Earth's atmosphere may be lost at a certain altitude transitorily. This phenomenon is called “blackout,” an important performance feature of aircraft design. Recently, with the development of the nearcraft, communication techniques under the plasma environment have increasingly become more important, with the realization that we can record, monitor, track, and capture the nearcraft. In this paper, an idea based on the capability of radiation from the terahertz communication system to penetrate the plasma sheath is proposed to provide the theoretical basis for real‐time communication with the nearcraft. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we propose a variational multiphase segmentation framework for synthetic aperture radar (SAR) images based on the statistical model and active contour methods. The proposed method is inspired by the multiregion level set partition approaches but with two improvements. First, an energy functional which combines the region information and edge information is defined. The regional term is based on theG0statistical model. The flexibility ofG0distribution makes the proposed approach to segment SAR images of various types. Second, we use fuzzy membership functions to represent the regions. The total variation of the membership functions is used to ensure the regularity. This not just guarantees the energy functional to be convex with respect to the membership functions but also enables us to adopt a fast iteration scheme to solve the minimization problem. The proposed method can segment SAR images ofNregions withN- 1 membership functions. The flexibility of the proposed method is demonstrated by experiments on SAR images of different resolutions and scenes. The computational efficiency is also verified by comparing with the level-set-method-based SAR image segmentation approach.

The measurement of an emitter's position using electronic support passive sensors is termed passive localization and plays an important part both in electronic support and electronic attack. The emitting target could be in terrestrial or underwater environment. In this paper, we propose a time difference of arrival (TDoA) algorithm for passive localization in underwater and terrestrial environment. In terrestrial environment, it is assumed that a Rician flat fading model should be used because there exists line of sight. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. We propose the TDoA finding algorithm via estimating the delay of two correlated channels, and compare it with the existing approach. Simulations were conducted for terrestrial and underwater environment, and simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and root-mean-square error (RMSE). Compared to the TDoA performance in terrestrial environment, the TDoA performance in underwater environment is much worse. This is because the underwater channel has clusters and rays, which introduces memory and uncertainties. For the two scenarios in underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA.

Feature extraction is a very important step in Synthetic Aperture Radar automatic target recognition (SAR ATR). In this paper, a feature extraction procedure based on the nonnegative matrix factorization (NMF) and Fisher linear discriminant (FLD) analysis is proposed for target recognition in SAR images. Firstly, segmented SAR images are processed by the NMF algorithm, which can extract nonnegative features that contain the local spatial structure information of targets. Then the FLD method is applied to the extracted features, thus the discriminability of the features can be enhanced. Both the spatial locality and separability between classes are enforced by this two-phase feature extracting procedure. Finally, the obtained features are used for automatic target recognition. Compared to several other methods, experimental results show the effectiveness of the proposed method for target feature extraction and recognition in SAR images.

The measurement of an emitter's position using electronic support passive sensors is termed passive geolocation. The emitting target could be in underwater environment. In this paper, we propose a Time Difference of Arrival (TDoA) algorithm for passive geolocation in underwater environment. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. To estimate the delay for TDoA of two correlated channels, we propose an TDoA finding algorithm, and compare with existing approach. Simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and Root-Mean-Square-Error (RMSE). In underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA.

To better understand the fundamental of the spaceborne/airborne hybrid bistatic SAR (SA-BSAR) image formation, the range cell migration (RCM) of the SA-BSAR is studied in the range-Doppler domain, where the RCM in SA-BSAR can be explicitly expressed in close form. Through the analysis of RCM relationship with target's azimuth and range position, we can find that, because of the system platforms' velocity difference along the azimuth direction, the RCM in SA-BSAR is 2D space variant. Therefore, the fundamental of frequency-domain image formation in SA-BSAR is to process the 2D space-variant RCM correction (RCMC) for nonreferent targets besides the bulk RCMC operation in the frequency domain. Furthermore, appropriate solutions in the frequency domain to remove the RCM in SA-BSAR are proposed and verified with five-point-target simulation.

In this letter, a novel approach for the coregistration of synthetic aperture radar (SAR) images is proposed based on the level set method. The features of regions are detected by segmenting the images, and the images are coregistered by matching the detected features. The energy functional of level sets is formulated with respect to detecting and matching features. The coregistration is achieved by minimizing the energy functional. Compared with the conventional tie-patch method, the results on a series of simulated experiments and real SAR data demonstrate the feasibility of the proposed approach.

A frequency domain imaging algorithm for the hybrid spaceborne/airborne BSAR is presented. The key point of deriving the algorithm is the analytical evaluation of the system point target response's 2-D spectrum. To overcome the difficulty of resolving analytical solution for the stationary phase point, the spectrum's phase is approximated by two-order Taylor expanding around the point, which is not only in the neighborhood of the system's corresponding stationary phase point but also can be obtained analytically. Thus the approximated analytical spectrum is pretty close to the actual one. In the imaging algorithm, both range-dependent range cell migration and azimuth-dependent range cell migration are compensated in two steps: Inverse Scaled Fourier Transform which can be realized through the chirp z-transform and phase multiplication. The validity of the algorithm is demonstrated by experiment with the simulated data.

The memory organization of Radix-4 FFT is considered. The new memory addressing assignment allows simultaneously access to all the data needed for butterfly calculation. The advantage of this memory addressing lies in the fact that it reduces the delay of address generation to one fourth of the existed

This paper presents a novel approach to the detection of airport runway in SAR imagery, which combines a region-based method and a Hough transform analysis stage using contextual information to identify suitable signatures. For the recognition of runway, the points are grouped using a Hough transform to find potential runway; finally, The result of this procedure is the fast and accurate identification of airfield runways. Recognition results on high resolution SAR images are given.

This paper presents the basic cofiguration of the Distributed Satellite Synthetic Aperture Radar System, discusses the working mode of the system, and indicates the specific performance of the system compare with the conventional SAR system.

A genetic back-propagation (BP) algorithm is used to solve the optimal design problem of multistage weighted order statistic filter under the mean absolute error criterion. It is shown experimentally that this algorithm can find the optimal WOS filter in image restoration application.