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Ground moving target indication (GMTI) has been extensively applied in modern warfare. In this letter, a novel deceptive jamming technique for countering multichannel synthetic aperture radar (SAR)-GMTI is presented, which is implemented with the frequency diverse array (FDA). Different from the phased array (PA), FDA modulates the transmitting signal with multiple frequencies across its array elements, which, consequently, enables range-dimension false target deception for SAR imaging. Furthermore, to achieve effective deception for the GMTI, micromotion modulation is adopted to simulate dynamic features of the moving targets. Mathematical derivation and simulation results show that the proposed technique can efficiently produce massive false targets in both the range and azimuth dimensions.

Deep learning algorithms have been widely utilized for synthetic aperture radar (SAR) target detection. Nevertheless, the traditional feature extraction methods and deep learning methods achieve improved ship detection accuracy at a cost of increased complexity and lower detection speed. As detection speed also is meaningful, especially in real-time maritime rescue and emergency military decision-making applications, we propose a new framework of faster region-based convolutional neural network (R-CNN) detection method to handle this problem. A new lightweight basic network with feature relay amplification and multiscale feature jump connection structure is designed to extract the features of each scale target in the SAR images, so as to improve its recognition and localization task network. Moreover, the K-Means method is used to obtain the distribution of the target scale, which enables to select more appropriate preset anchor boxes to reduce the difficulty of network learning. Finally, RoIAlign instead of region of interest (RoI) Pooling is used to reduce the quantization error during positioning. Experimental results show that the detection performance of the proposed method achieves 0.898 average precision (AP), which is 2.78% better than the conventional Faster R-CNN and 800% faster detection speed.

Deception against inverse synthetic aperture radar (ISAR) has been a topic of active research in electronic warfare. However, most of the studies mainly focus on producing one-dimensional deception, which is inadequate for two-dimensional imaging system like ISAR. Thus, in this letter, a joint two-dimensional ISAR deception based on frequency diverse array (FDA) and interrupted sampling is proposed. Compared with conventional phased-array (PA) and single-channel methods, FDA permits more degrees of freedom in producing range deception. Combined with interrupted sampling in the slow time domain, the proposed method is anticipated to produce a group of false targets in both range and cross-range directions. The benefit is easy to control the number and distribution of false targets with high efficiency and less computation burden. Simulation results verify the effectiveness of the proposed method.

Most of the existing deception jamming techniques focus on designing effective jamming signals for static targets or scenes. This paper proposes a multi-scene deception jamming against synthetic aperture radar (SAR) imaging with frequency diverse array (FDA) antenna. Different from traditional single-element and phased-array antennas, FDA antenna employs a small frequency offset across the array elements to produce angle-, range- and even time-dependent transmit beampattern, which brings bright prospect for the application of new jamming techniques against SAR imaging. Such a new method will generate many false targets or multiple deception scenes for the jammed SAR, which helps jam better on the static scene and moving target. Moreover, the location and number of false targets can be manipulated. This paper will illustrate the FDA-based deception jamming against SAR imaging, specially designed waveform and the relationship between the number of false targets or scenes and FDA parameters. The relationship between the location of false targets and the position of jammer will also be described in detail. All proposed methods are validated by extensive simulation results.

In this paper, we investigate frequency diverse array (FDA) antenna for physical-layer security and analyze the system performance (i.e., average secrecy capacity and secrecy outage performance) over independent but not necessarily identical distributed Rayleigh fading channels. A single-antenna multiple-channel receiver structure is proposed to address the time-variance property of FDA antenna. The transmitter allocates part of its power to send artificial noise (AN) so as to ensure the security of communications, such that the channels other than the desired receiver’s are degraded. Specifically, we derive the closed-form expressions for average secrecy capacity, probability of nonzero secrecy capacity, and secrecy outage probability, respectively. The correctness of the proposed analysis is corroborated via simulations. Furthermore, the corresponding asymptotic expressions for average secrecy capacity and secrecy outage probability in high signal-to-interference-plus-noise ratio (SINR) regime are also provided, respectively, to have deep insights into the performance of the considered system. Numerical results show that the FDA communications significantly outperforms the phased-array (PA) scheme in range dimension for secure communication.

In conventional synthetic aperture radar (SAR) processing algorithms, it is generally assumed that the platform performs a uniform linear motion. However, if there is a three-dimensional acceleration during the flight, the motion trajectory will greatly increase the image processing complexity, especially in highly squinted scenarios. This paper proposes a high-precision imaging algorithm for highly squinted SAR with 3D acceleration. Firstly, the range history is extended by the Taylor series, and then a two-dimensional non-uniform fast Fourier transform (2D-NUFFT) is presented to focus the SAR image in range velocity domain. Moreover, as terrain matching is required in practice, the range-velocity image is further transformed into range-azimuth domain. The effectiveness of the proposed algorithm for highly squinted SAR imaging is verified with both simulation data for multi-point targets and experimental data for surface targets.

Impaired array may significantly deteriorate the beampattern and consequently degrade the system performance. In this letter, we propose a signal processing scheme for impaired array diagnosis and mitigation via Khatri–Rao processing, which requires uncorrelated far-field sources to evaluate the mutual correlation between the vector signal received by the impaired array and a failure-free array. The essence is to detect impaired elements by a difference operation and then reconstruct an unpaired array through the Khatri–Rao processing. Moreover, the correlation problem due to the Khatri–Rao processing is addressed by a compressive sensing reconstruction algorithm instead of a conventional spatial smoothing algorithm to achieve improved performance. Both diagnosis and mitigation approaches require no a priori knowledge of the failed elements, but the diagnosis method needs the failure-free beampattern.

Multiple Samples Correlation Power Analysis (MSCPA) is proposed to attack the chaos-based block cipher which is based on Feistel structure. This is the first time to analyze the security of Feistel-based chaotic block cipher from hardware prospective. Firstly, attack points of the cipher are comprehensively analyzed by the proposed MSCPA. The outputs of Cubic map and addition operation generate relatively small correlation coefficients for incorrect keys, therefore they are selected as the attack points. Then correlation coefficients between the keys and power samples are calculated and analysed to find interesting points of power traces. These interesting points are combined together to obtain the correct key by the MSCPA. The encryption algorithm based on Feistel structure is implemented on an Atmel XMEGA microcontroller. Results show that although the Feistel-based chaotic block cipher has a good performance under conventional statistical analyses, it still can be attacked by MSCPA. Moreover compared to CPA, MSCPA has a better performance in accurate key differentiating level.

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

This paper proposes a modified two-step processing approach to deal with the problem of azimuth time domain aliasing in processing high resolution spaceborne sliding spotlight synthetic aperture radar (SAR) data. The signal model of spaceborne SAR is formulated, which includes the geometric model, echo model and Doppler bandwidth. This paper proposes a modified approach through parameter design to avoid azimuth-time domain aliasing. The sliding factor has a critical value for the azimuth-time aliasing, which meet the requirement of azimuth resolution and large imaging scene. The effectiveness of the proposed approach for sliding-spotlight SAR imaging is verified with simulation data for point targets.

Lumbar disc diagnosis belongs to Magnetic Resonance Imaging (MRI) segmentation and detection. It is a challenge for even the most professional radiologists to manually check and interpret MRI. In addition, high-class imbalance is a typical problem in diverse medical image classification problems, which results in poor classification performance. Data imbalance is a typical problem in medical image classifications. Recently computer vision and deep learning are widely used in the automatic positioning and diagnosis of intervertebral discs to improve diagnostic efficiency. In this work, a two-stage disc automatic diagnosis network is proposed, which can improve the accuracy of training classifiers with imbalanced dataset. Experimental results show that the proposed method can achieve 93.08%, 95.41%, 96.22%, 89.34% for accuracy, precision, sensitivity and specificity, respectively. It can solve the problem of imbalanced dataset, and reduce misdiagnosis rate.

Summary Background The management of latent Mycobacterium tuberculosis infection is a new priority action for the WHO End Tuberculosis (TB) Strategy. However, national guidelines on latent tuberculosis infection testing and treatment have not yet been developed in China. Here, we present the results from the 2-year follow-up of a study that aimed to track the development of active disease in individuals with latent tuberculosis infection, identify priority populations for latent infection management, and explore the most suitable latent infection diagnostic approach. Methods A population-based multicentre prospective study was done in four sites in rural China, between 2013 and 2015. The baseline survey in 2013 measured the prevalence of latent tuberculosis infection using QuantiFERON-TB Gold In-Tube (QFT) and tuberculin skin test (TST) in eligible participants. During the follow-up phase between 2014–15, we assessed individuals who had tuberculosis infection at baseline (QFT-positivity or TST tuberculin reaction size [induration] of ≥10 mm) for the development of active disease through active case finding. Eligible participants included in follow-up survey had a birth date before June 1, 2008 (5 years or older in 2013), and continuous residence at the study site for 6 months or longer in the past year. Participants with current active tuberculosis at baseline survey were excluded. Findings Between Sept 1, 2013, and Aug 31, 2015, 7505 eligible participants (aged 5 years or older) were included in tuberculosis infection test positive cohorts (4455 were QFT positive, 6404 had TST induration ≥10 mm, and 3354 were positive for both tests) after baseline examination. During the 2-year follow-up period, 84 incident cases of active tuberculosis were diagnosed. Of participants who developed active tuberculosis, 75 were diagnosed with latent infection by QFT, 62 were diagnosed by TST, and 53 were diagnosed by both tests. An incidence rate of 0·87 (95% CI 0·68–1·07) per 100 person-years was observed for individuals who tested positive with QFT, 0·50 (0·38–0·63) per 100 person-years for those who tested positive with TST (p Interpretation Our results suggest that high-risk populations in communities in rural China, such as individuals at a high risk of disease reactivation from previous tuberculosis, should be targeted for latent infection screening and treatment with an interferon-γ releasing assay rather than a TST. Funding National Science and Technology Major Project of China, Program for Changjiang Scholars and Innovative Research Team in University of China, CAMS Innovation Fund for Medical Sciences, and Sanming Project of Medicine in Shenzhen.

Mosaic synthetic aperture radar (SAR) can flexibly steer its antenna beam, achieving high resolution wide swath (HRWS) imaging. Nevertheless, the conventional imaging algorithm may suffer from spectrum ambiguity and finally lead to defo-cused result due to data acquisition geometry. In this paper, the imaging geometry of mosaic SAR is introduced analyzed. An imaging algorithm based on the spectrum analysis technique is proposed to handel the mosaic SAR imaging. Simulation results validate the effectiveness of the proposed approach.

In object detection, the spatial context information of the actual scene can improve the accuracy of detection while current object detection methods based on deep learning usually relies on CNNs to combine the local geometric and texture features without using these context information. In this paper, we proposal a method inspired by RNN that can extract the spatial context features and design a lighter, faster and more accurate network model for object detection that can fuse these features into multi-scale feature maps from CNNs to generate more comprehensive multi-information fusion features as inputs of localization and classification tasks subnetwork. The spatial context features extractor can be trained by joining in the BP process of the entire model, and we give the detailed optimized derivation procedures. Then we also demonstrate the validity of our model by conducting the contrastive experiments on KITTI dataset.

When the chaotic block cryptographic algorithms are performed on hardware devices, the leakages of power consumption etc. are crucial information which can be used to analyse the security of the cryptosystems. Template Attack (TA) can recover the secret key. However, there are still some challenges for TA such as irreversible covariance matrix and exponentiation calculation overflow. Machine Learning-based Similarity Attacks (MLSAs) are proposed to effectively analyse the sensitive information of the chaotic block cryptosystem. The proposed method consists of three steps: parameter tuning, learning and attacking. For the parameter tuning, the profiling traces are categorised according to the Hamming weights of sensitive intermediate data. Then a 10-fold cross-validation is executed to determine the corresponding parameter settings for learning algorithms. In the learning step, the profiling traces and Hamming weight labels are used to train machine learning models, and in the attacking step different similarity measure methods are used to calculate similarities between actual and hypothetical Hamming weight labels to attack the secret keys. Performance analyses demonstrate that the proposed MLSAs have higher success rates than TA and lower computational time consumptions under most of scenarios. Therefore, the MLSAs can efficiently attack and analyse hardware security of chaotic block cryptosystems.

The security of chaotic cryptographic system can be theoretically evaluated by using conventional statistical tests and numerical simulations, such as the character frequency test, entropy test, avalanche test and SP 800-22 tests. However, when the cryptographic algorithm operates on a cryptosystem, the leakage information such as power dissipation, electromagnetic emission and time-consuming can be used by attackers to analyze the secret keys, namely the Side Channel Analysis (SCA) attack. In this paper, a cryptanalysis method is proposed for evaluating the security of a chaotic block cryptographic system from a hardware perspective by utilizing the Template Attacks (TAs). Firstly, a chaotic block cryptographic system is described briefly and implemented based on an Atmel XMEGA microcontroller. Then the TA using a multivariate Gaussian model is introduced. In order to reduce computational complexity and improve the efficiency of TA, the Hamming weight is used in this work to model power consumption traces. The proposed TA method has the following advantages including (a) using the sum of difference to select points of interest of traces, (b) using a data processing method to minimize the influences on power information modeling from the redundant sampling points, and (c) all the traces are aligned precisely before establishing the templates. Experimental results show that the TA can be used to attack the chaotic cryptographic systems and is more efficient, i.e. [Formula: see text]32% less attack traces than correlation power analysis, when the templates are properly built.

When synthetic aperture radar (SAR) imaging is applied to observe ground scene containing a moving target, the imagery of moving target will be typically smeared due to range cell migration and Doppler spectrum broadening caused by the target motions, especially for the accelerating targets within a long observation time. To eliminate these effects, a novel imaging algorithm using cooperative between SAR under high-orbit and low-orbit satellites is proposed. The range migration including range walk and range curvature within the coherent integration period has been corrected via the third-order keystone transform. Then, by estimating and compensating the phase errors and the fold factor terms, the target's resolution is improved and the motion parameters are correct-ly estimated. The effectiveness of the proposed algorithm is demonstrated by simulations.

Compared with stationary targets imaging, detection and imaging of moving targets becomes more difficult in Geosynchronous synthetic aperture radar (GEO SAR), especially when the moving target is drowned in strong ground clutter. In this paper, a scheme of moving target detection and imaging with three antennas is proposed for GEO SAR. Firstly, the displaced phase center antenna (DPCA) is used to eliminate stationary targets' echo. Then, the third-order keystone transform is applied to correct the range migration. Next, after coherent integration, the constant false alarm ratio (CFAR) is used to detect the moving target. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Compressed sensing (CS) has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. In existing CS-based ISAR imaging algorithms, Laplace distribution is widely adopted to enforce sparseness on signal recovery. However, this kind of CS method using Laplace prior encounters the problems of determining optimum regularization factor and heavy computation load. In this paper, a fast marginalized sparse Bayesian learning (MSBL) method is proposed for three-dimensional (3-D) interferometric super-resolution ISAR imaging. After deriving the target sparsity-driven imaging model, a fast MSBL approach is applied to obtain super-resolution ISAR image, and then a high-quality 3-D view of the target is achieved via the interferometry technique using an ISAR imagery pair. Experiments on simulated and real data are provided to validate the effectiveness of the proposed method.

The existing bistatic inverse synthetic aperture radar (Bi-ISAR) imaging methods usually uses a “stop-and-go” assumption where the target can be considered not in motion (stop condition) during the fast-time and in motion (go condition) during the slow time. However, for the high-speed target, this assumption is violated; furthermore, the conventional compression via Fourier transform is also invalid due to the quadratic phase term induced by the high-speed motion. In this case, a range compression method using the fractional Fourier transform (FrFT) based on minimum entropy criterion is presented to obtain high-resolution one-dimensional (1-D) range profile. Moreover, to achieve azimuth focusing for the complex-motion target, a new method of imaging time selection based on frequency smooth degree (FSD) is proposed. Simulated and real data are provided to verify the effectiveness of the proposed method.

The theory of compressed sensing (CS) states that an unknown sparse signal can be accurately recovered from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, we present a new framework of high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) imaging based on CS. A phase-preserved CS approach for high-range resolution imaging is proposed. The phase of a Bi-ISAR signal can be extracted by constructing a phase-preserved Fourier basis, which is crucial to azimuth processing of Bi-ISAR imaging. After performing CS reconstruction in range, we present an improved version of CS-based cross-range imaging by combining modified Fourier basis and weighting with CS optimization. Simulated data are used to test the robustness of the Bi-ISAR imaging framework with two-dimensional (2-D) CS method. The results show that the framework is capable of accurate reconstruction of Bi-ISAR image in both range and cross-range.

This letter proposes a Bayesian compressed sensing (BCS) method for high-resolution inverse synthetic aperture radar (ISAR) imaging when the basis matrix depends on an unknown rotation rate. In the proposed method, the radar transmits only a few probing frequencies instead of a wideband signal. In doing so, both the rotation rate and a high-resolution ISAR image can be simultaneously retrieved by jointly applying the BCS algorithm using Gaussian prior and a gradient-based search algorithm to exploit the sparse probing frequencies. The effectiveness of the proposed method is verified by experimental results with both simulated data and real data.

Translational-variant bistatic forward-looking synthetic aperture radar (TV-BFSAR) introduces three difficulties for the imaging algorithm, namely, large range cell migration (RCM), severe range–azimuth coupling and large azimuth spatial variance. To address these problems, an extended azimuth non-linear chirp scaling (EANLCS) algorithm is proposed. The essence is to remove the linear RCM and Doppler centroid and compensate the residual RCM and range–azimuth coupling terms with secondary range compression. Thereafter, the EANLCS operation is applied to eliminate the azimuth-dependent frequency-modulation rate and cubic phase terms. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm in focusing TV-BFSAR data.

Co-designed radar-communication is becoming necessary, but there is a challenge to design suitable waveforms that can be simultaneously employed for information transmission and radar applications. This paper introduces the orthogonal frequency division multiplexing (OFDM) chirp radar waveform by embedding communication codes for co-designed radar-communication system from a top-level system description. Several implementation issues are also discussed. The object is to call for more investigations on this topic in wireless communication community.

In this paper, we focus on high-precision measurement of Doppler information to ensure high positioning accuracy in passive location. Recent studies in which signals are simulated with second order derivative of Doppler frequency have been derived for passive radar application, but can only reach low level of accuracy of Doppler information. This paper proposes an accurate measurement of Doppler information using third order Taylor expansion for the distance function. The paper first discusses theory of Passive Location using Doppler information acquired by one single satellite. Then discusses the improvement of the signal model and introduce a new algorithm of obtaining Doppler information. The advantage of the proposed approach is that it can acquire second order derivative of Doppler frequency to minimize measurement error. Especially under the condition of high carrier frequency, this approach therefore achieves higher measurement precision. Finally, simulations are performed to validate the proposed method.

Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current passive radar imaging techniques. However, a large rotation angle demands a long observation time, which cannot be implemented for actual passive radar system. To overcome this disadvantage, this paper proposes a super-resolution passive radar imaging framework with a sparsity-inducing compressed sensing (CS) technique, which allows for fewer IOs and a smaller rotation angle. In the proposed imaging framework, the sparsity-based passive radar imaging is modeled mathematically, and the spatial frequencies and amplitudes of different scatterers on the target are recovered by the log-sum penalty function-based CS reconstruction algorithm. In doing so, a super-resolution passive radar imagery is obtained by the frequency searching approach. Simulation results not only validate that the proposed method outperforms existing super-resolution algorithms, such as ESPRIT and RELAX, especially in the cases with low signal-to-noise ratio (SNR) and limited number of measurements, but also have shown that our proposed method can perform robust reconstruction no matter if the target is on grid or not.

Simultaneous synthetic aperture radar (SAR) imaging and ground moving target indication (GMTI) is of great importance in remote sensing applications, but it is difficult to be implemented for existing methods due to the cross-interferences between stationary targets/clutter and moving targets. Generally, the imaged moving targets may be displaced in azimuth according to their radial velocities and superimposed upon clutter at a wrong location. In this paper, we proposes a simple simultaneous SAR and GMTI approach by exploiting the fractional Fourier transform (FrFT) algorithm, different from existing methods that perform first stationary clutter suppression and thereafter handle GMTI via Doppler parameters estimation. The feasibility is verified by simulation results.

In this paper,we propose a sparse Baysesian learning (SBL) based approach for the DOA estimation in the presence of coherent sources. First, the difference technique is used to enhance the input SNR. Then, we construct a virtual array manifold to eliminate the cross-term effect between each coherent group, after eigenvalue decomposition (EVD) of the difference covariance matrix, reduce the dimension of input data and regard each column as one snapshot of the virtual signal containing all bearing information of the coherent sources. Finally, the DOAs of the coherent sources are estimated via the SBL algorithm. Numerical results validate superior performance of the proposed approach.

Synthetic aperture radar (SAR) techniques are typically used to achieve a high azimuth resolution, but conventional monostatic SAR is not applicable in forward direction because of azimuth ambiguities and poor Doppler resolution. This paper focuses on SAR imaging processing in a novel forward-looking bistatic configuration that a pilotless plane transmits signals and a guided missile receives echo which can avoid image ambiguities and to improve the azimuth resolution. A fourth-order range equation and a fourth-order phase term are adopted to improve the accuracy in the approximation of the range history and two-dimensional spectrum for forward-looking bistatic SAR, respectively. The spectrum is used for high-order phase compensation, range compression, secondary range compression, range cell migration correction, and azimuth compression. Especially, the proposed algorithm is able to focus the translational variant bistatic configuration where the transmitter and receiver platforms are moving in parallel ...

The unintentional modulation on pulse (UMOP) features produced by radar emitter are similar to the fingerprint of a person, which plays a significant role in radar emitter recognition.In this paper, we propose an improved Fisher discriminant analysis method for feature extraction of UMOP by combining ambiguity function and wavelet transform. In this method, the radar emitter signals are firstly mapped onto the time-delay and frequency-offset plane via ambiguity function. We extract the diagonal slice of signal in the ambiguity function plane, and obtain wavelet coefficients of this signal through wavelet transform. Then, an improved Fisher discriminant rule is used to achieve feature selection and feature space reduction. Experiments on simulated and measured data verify the effectiveness of the proposed method.

The theory of compressed sensing (CS) indicates that the optimal reconstruction of an unknown ISAR signal with intrinsical sparsity can be achieved by solving a sparsity-driven optimization problem. Due to this property, a novel method to construct sparse basis is presented for ISAR image generation. We utilize discrete chirp fourier transform (dcft) and CLEAN to decompose the signal of interest into multiple LFM or chirp signals. The proposed method can generate adaptive sparse basis according to the frequencies and chirp rates of these chirp signals. Our proposed method which uses much fewer measured data can get almost the same image compared with the uncompressed conventional imaging algorithm and our proposal outperforms the CS-based method with standard Fourier basis. Both simulated and real experimental results can demonstrate the effectiveness and feasibility of the method.

The high-resolution imaging for high-squint synthetic aperture radar (SAR) is still a difficult issue due to large range migration and strong range dependence. An extended keystone transform (EKT) is introduced to correct more complex range cell migration and range-variant, then a two-dimensional non-uniform fast Fourier transform (2D NUFFT) is proposed to obtain the focused image. Simulation results are given to validate the effectiveness of the method.

Range cell migration (RCM) correction is a key procedure in dim maneuvering target detection. Keystone transform (KT) has been employed to correct linear RCM of moving target. In this paper, we take the nonlinear RCM caused by radial acceleration into consideration and extend the original KT. Thus, a novel method based on extended KT (EKT) for maneuvering target detection is proposed. After EKT processing, the RCM is eliminated closely and the target's power integration can be obtained using 2D-NUFFT. A numerical simulation is performed to demonstrate the validity of the proposed algorithm.

The intrinsic sparsity of the ISAR signal provides a foundation to apply compressed sensing (CS) to reconstruct highresolution ISAR imagery. In this paper, a novel two-dimensional CS-based approach is presented for ISAR image generation. It is denoted that through CS technique, the data rate has been reduced in both range and azimuth to obtain high-resolution ISAR image. The proposed method can get almost the same image by using fewer measured data compared with the conventional imaging algorithm and guarantee robust recovery in case of low SNR. Experimental results demonstrate the effectiveness and feasibility of the method.

Focusing on the time-delay problem of multi-agent systems, this paper provides solutions for flocking of systems with time-delay. For the circumstance with a virtual leader, the agents would follow the virtual leader and achieve the same velocity asymptotically. Systems with homogeneous and inhomogeneous time-delays are discussed respectively. The appropriate simulation circumstance is set up, and the obtained results are in agreement with the theoretical deduction, which show the effectiveness of the proposed flocking algorithm.

Many anti-jamming techniques have been proposed such as adaptive temporal filtering (ATF), spatial adaptive processing (SAP), space-time adaptive processing (STAP), power spectrum excision, and etc. The performance of each specific anti-jamming technique is often evaluated in terms of tracking thresholds not in terms of range or position error. And several techniques can be combined together to provide better anti-jamming performance. How to judge the anti-jamming performance of these algorithms has become a new problem. In order to solve this problem, the anti-jamming performance of SAP and STAP based on linear constraint minimum variance (LCMV) criterion will be evaluated and compared through many indicators in this paper. Simulation results show that STAP with L constraints brings more degrees of freedom to constrain the weight coefficients and thus improve C/N0, especially when many jammers impinge on the array.

The accuracy of navigation and positioning directly depends on satellite clock bias. Because of different abnormal models, predicting and integrity monitoring of satellite clock bias is a great challenge. This paper described the satellite clock bias model, analyzed the reasons of abnormal satellite clock bias, and established three satellite clock models. Then, this paper validated the accuracy of two prediction model. At last, this paper proposed an integrity monitoring method using Shewhart Control Chart. Based on IGS products, through simulation analysis, we found that this method can successfully detect abnormal clock bias, monitor integrity of clock bias and replace outliers.

The extraction of radar emitter identification is very important to distinct the correct target. Up to now, many corresponding methods are proposed. But most of it has the problems of low recognition rate and not adapting to low SNR environment. In the paper, a novel method is proposed. The approach utilizes time-frequency analysis methods and singular value distribution(SVD) to extract the singular values of signal, making it be the feature vector., and neural network based classifiers were designed to identify radar emitter signals automatically. The experimental results show that it can achieve a satisfying accurate recognition rate when signal-to-noise rate varies in a large range. It is proved to be valid and practical approach. (4 pages)

The traditional phase compensation method based on parametric model always needs to make a tradeoff between the accuracy and the computational complexity. Given this, a new method for compensating phase based on Discrete Polynomial Transform (DPT) and CLEAN technique is proposed. To reduce the computation power further, the imaging region estimation is applied. Furthermore, the limitation and the restricted condition of DPT are analyzed for practical scenarios in this paper. In the end, simulation results are given to validate this method.

The Fourier transform has been widely used in ISAR signal and image processing. In most cases, ISAR signals exhibit time-varying behavior in the Doppler spectrum. Thus, a joint time-frequency analysis (JTFA) for ISAR signals is more useful. In this paper, we introduce a new WVD algorithm jointed CLEAN technique. Meanwhile, the simulations of LFM signal in ISAR are adapted to compare their performance. It is shown that our algorithm is better than typical WVD and SPWVD algorithms.

High speed A/D sampling and large scale data storage are two basic challenges of the high resolution SAR system. The developing of radar system is limited by these two challenges under the Nyquist sampling theory. Compressed sensing (CS) is a new approach of sparse signals recovered beyond the constraints of Nyquist sampling technique. With the consideration of these problems that might happen and the advantage of CS theory, a novel SAR image processing algorithm based on compressive sensing was proposed in this paper. Using the data whose sampling rate is lower than the required Nyquist sampling rate, the CS-based algorithm operates at range and azimuth dimensional respectively. Experimental results show the presented algorithm based on compressed sensing have a better performance than the conventional SAR algorithm even with only smaller samples, and also indicate that the presented algorithm is robustness with existence of serious noise

Estimating cross-range motion parameter is one of the most important steps while using antenna array to get targets' 3-D ISAR images. A method which can estimate cross-range motion parameter based on DCFT is proposed in this paper. It can not only lower the amount of computation, but also insure the imaging precision. The validity of the method would be proved by simulation.

In this paper, we consider the problems of reconstructing the spectrum based on Fractional Fourier Transform (FrFT) from periodic non-uniformly sampled signal in multiple azimuth beams SAR system. First, based on the theory of the paired echoes, the influence of periodic non-uniform sampling for SAR imaging has been derived. Second, signal model of non-uniformly sampling in azimuth direction has been established. Finally, detailed analysis of spectrum reconstruction algorithm in the fractional Fourier domain has been performed. In addition, the simulations are carried out to verify the correctness of the results.

In multi-target ISAR imaging, if the returned signals are range non-separable and the relative radial velocity is large. We can neither resolve them in range domain nor the Doppler frequency domain. In order to solve this problem, the use of the Radon transform and a modified envelope correlation method for multi-target ISAR imaging is presented. The radon transform is used to align the range profiles coarsely. For more accuracy alignment, a modified envelope correlation method is proposed. After range alignment, phase compensation is fulfilled by Discrete chirp-Fourier transform (DCFT). The simulation results show that the method proposed in this paper is effective.

A novel algorithm for SAR(Synthetic Aperture Radar) raw signal simulation based on sub-aperture processing is proposed. The fundamental of the algorithm is analyzed, the signal model is deduced, as well as the simulation procedures are given. Simulation results of extended scene have shown that in the condition of meeting simulation precision, the simulation speed of the algorithm is higher than that of the echo simulation algorithm based on 1D FFT(Fast Fourier Transform).

Complicated Electromagnetic Environment Simulation still puzzles the researchers who are engaged in System Design of large-scale electron system. Even electromagnetic simulation for a less dielectric has great iterative numeration, and complicated environment will refer to more and more calculation units belong to intricate electron equipment and national element. Mass compute, data alternation transfers and complex electromagnetic mechanism actualization will let researcher to go mad. This paper presents a novel system framework to build a complicated electromagnetic environment simulation based on ChinaGrid, which is agile and distributed federations. It can achieve the goal of the simulation system and administer to system design for large electron system.

A real-time DBS imaging technique based on diving model is proposed in order to solve the problem of imaging at the stage of missile's diving attack. The signal model under the diving mode is established, and the imaging algorithm for diving mode and the flow chart of hardware processing are given. The validity of DBS imaging under the diving mode is illustrated through simulation. (4 pages)

A echo simulation algorithm for natural scene is presented. It is based on the model of equivalent scatterer for the scene, the approximate expression of SAR system transfer function, and one-dimensional fast Fourier transform (FFT) for data processing. The validity and correctness of the algorithm are verified through raw echo data simulation of point target and natural scene.

In pulse Doppler (PD) radar, a novel motion compensation method based on keystone transform is proposed for handling with the data of a low signal-to-noise ratio (SNR). The keystone transform is used to correct the linear range migration which results from the radial velocity of targets. The SNR is improved through coherently accumulating all pulses. The simulation results show that the performance of the method based on keystone transform is better than that of the conventional coherent integration method using fast Fourier transform (FFT).

In order to research imaging algorithm problems of space-borne bistatic SAR, it is necessary to simulate echo data of actual scene. On the basis of point target simulation, a method of simulating space-borne bistatic SAR raw echo data utilizing gray image data of airborne SAR is presented in this paper. First of all, gray data of airborne SAR image is added and averaged to compute different targets' RCS. Then, raw data of actual scene is generated using the model of scene echo signal of f space-borne bistatic SAR. Finally, imaging processing is fulfilled utilizing BP algorithm toward echo data. The imaging result verifies the correctness of this simulation method.