In this paper, we focus on an optimization of the transmit parameters of a frequency diverse array (FDA) radar in order to reduce tracking root mean square error (RMSE) in complex environment with significant measurement errors. Since the classical interacting multiple model (IMM) method cannot effectively utilize the degrees of freedom caused by frequency offset, a cognitive tracking method based on closed-loop cognitive feedback and transmit frequency offset optimization has been developed to adapt to the changing environment. In addition, perceptual information entropy is introduced to quantify the uncertainty in the tracking process. Simulation results demonstrate that the proposed method exhibits superior tracking performance over conventional methods. • This paper presents a cognitive target tracking framework. • The proposed method demonstrates robust tracking capability. • This paper optimizes frequency offset with a minimum tracking error criterion.

Side-Channel Attacks (SCAs) now require more side-channel traces for successful execution, which places more stringent requirements on the storage capacity and computational ability of the devices on which SCAs are based. To reduce the storage and computational pressure on the local device where SCAs are performed on collected leakage traces from an attacking device, this paper proposes a secure cloud outsourcing protocol to perform Principal Component Analysis (PCA) dimensionality reduction on the side-channel traces. Secure cloud outsourcing is applied for the computationally intensive matrix multiplication and eigenvalue decomposition of the PCA process. The proposed protocol has been proven to balance privacy, efficiency, and correctness. Through experiments on CW and Grizzly datasets, it shows that 1) Correlation Power Analysis (CPA) with PCA effectively mitigates noise, improving the probability of a successful CPA; 2) Cloud-based PCA significantly reduces the computational complexity of local devices; 3) Template Attacks (TAs) are performed on leakage trace data using cloud-based PCA, client-based PCA, Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA). The attack results of cloud-based and client-based PCA are basically identical, and they achieve lower Guessing-Entropy (GE) than ICA. Both theoretical analysis and experimental results demonstrate the feasibility and advantages of this protocol.

High-quality synthetic tabular data raises severe issues about potential misuse. Existing post-processing database fingerprinting schemes relying on either Primary Key (PK) or original database ( non- blind) cannot work well in PK-free table, and easily suffer from regeneration attack using Diffusion Models (DMs). Integrating watermarking into the generation process of tabular data is an effective solution, such as Tree-Ring (TR) and Tabular Watermarking (TabWak) for latent tabular DMs. But, these schemes only address copyright protection and lack strong liability guarantees (fingerprinting) in case of unauthorized redistribution against collusion attack. Thus, we propose a framework ofPK-free,Blind andCollusion-resistant syntheticTabularFingerprinting (PBC-TabFip) with DMs by readily incorporating with symmetric Tardos codes of arbitrary alphabet sizes. PBC-TabFip solves the problem of deleted or modified PK, and maintains a blind property which does not use original data to extract fingerprint. Based on PBC-TabFip, we actually propose binary TabFip and TabFip+, quaternary TabFip* and TabFip+* schemes, where “+” means using self-cloning. To identify malicious user, we use Bit Matching (BM) and Valid Bit Matching (VBM) mechanisms for our schemes. We theoretically deduce that TabFip with BM obtains the highest expected accuracy of fingerprint bit matching against random noises. In the case of binary fingerprint, we also demonstrate the fragility of TabFip without Tardos codes, resulting in high probability of detecting innocent, and stronger robustness of TabFip with Tardos codes against inversion-based collusion attack using different strategies. Experimentally, comparing our schemes with baselines on two datasets, we show that the quality of synthetic table achieved by TabFip is higher than that achieved by other methods (TR under proportion of same rows$\lesssim$10%); and TabFip with BM achieves higher average detecting rate of correct user against five single-handed post-editing attacks. In a practical scenario, we exhibit that TabFip with Tardos codes identifies at least one of the colluders with 100% probability and without detecting innocent against two types of collusion attack.

Side-channel attacks (SCAs) serve as a fundamental tool for evaluating the implementation security of cryptographic devices. In real-world acquisition scenarios, however, power consumption traces are frequently degraded by device clock jitter and external noise interference, resulting in pronounced temporal misalignment and signal distortion. As a result, the efficiency and stability of attack convergence are seriously restricted. To address this, we propose a correlation power analysis (CPA) framework that integrates successive variational mode decomposition (SVMD) and peak-anchor-based alignment (PA-CPA). Firstly, the method uses SVMD to adaptively decompose and reconstruct the original power consumption traces, effectively suppressing random interference and preserving leakage-relevant features. A robust anchor point sequence is then constructed and global linear resampling and local dynamic time warping (DTW) are combined to realise the segmental fine alignment of the power consumption traces. This improves feature synchronisation and alignment accuracy. Experimental results demonstrate that the proposed method achieves higher attack success rates and faster convergence.

ABSTRACT Epilepsy is a complex chronic brain disorder that can be identified by observing brain signals. In general, the electroencephalogram (EEG) can be used to detect these brain signals. In order to produce a high‐quality model, data from numerous patients can be gathered on a central server. However, sending the patient's raw data to the central computer may lead to privacy leakage. To address this problem, this work uses federated learning and differential privacy to train the model jointly. Furthermore, the epilepsy data is unbalanced as seizure only happens for a minority of time in one day, which influences the performance of the model. Thus, this work also uses label‐distribution‐aware‐margin (LDAM) loss to solve this issue. This work is evaluated in intracranial EEG datasets, which consist of two dogs' EEG records. The global model trained jointly with LDAM loss can achieve an accuracy of 96.95%, a sensitivity of 78.9%, a specificity of 96.145%, an F1 score of 70.435%, and a geometric mean of 87.785%. Compared with the other works, the accuracy has improved by about ˜9.31%, while the specificity and the geometric mean have also improved by about ˜10.75% and ˜1.8%, respectively.

This paper studies the problem of adaptive distributed target detection for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, where the target is embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model establishes distributed target detection as a summation expression, which is different from the traditional detection models in MIMO or phase array radars that discuss only point-like target. Next, according to the rules of generalized likelihood ratio tests (GLRT), Rao and Wald tests, we designed three adaptive detectors without the training data. The numerical results validate the proposed method and all theoretical analyses.

Over past few years, the development of deep learning has greatly facilitated the SAR target recognition tasks. However, most deep learning-based SAR aircraft recognition methods suffer from a drawback of heavy reliance on large-scale labeled training data, making it difficult to apply them to actual remote sensing tasks. In term of these issues, we propose an improved prototypical network based on two stage fine-tuning for few shot SAR aircraft recognition. In this method, firstly, the deep learning image encoder is trained on large scale open source datasets supervised. This gives the encoder the ability of recognizing basic geometry. Secondly, based on the contrastive learning, the encoder is trained on SAR aircraft data in a self-supervised manner. This helps the encoder learn the ability to encode the semantics of scatterers in SAR image. Finally, the encoder is further fine-tuned on the specific SAR aircraft few shot recognition dataset in the way of prototype network. The comparative experiments shows that, the proposed method outperforms other state-of-the-art methods on public SAR aircraft dataset. Especially in the 1-shot case, the proposed method achieves a recognition accuracy of 81.34%. Simulation results show that the proposed method has the potential to be applied to real-world scenarios.

Deep Neural Networks (DNNs) have made significant progress in the field of artificial intelligence, and machine learning as a service has become a service that can be profitable. Intellectual property protection for DNNs has become an important issue. These DNNs models may be misappropriated by third-party, thus causing financial losses to the model owners. In the existing white-box and black-box approaches, protected models cannot be actively verified for ownership, and they can only be verified by sending a specific sample after the model has been stolen. In this work, a black-box DNNs watermark protection framework is proposed for protecting intellectual property of image classification models, which embeds watermarks in DNNs models. The proposed framework is applicable to models for image classification, and only images containing secret watermarks can be correctly recognized by the model. Besides, the proposed framework can correctly identify the ownership of the model, resist pruning operations of the model, and resist malicious tampering of the input image. Experimental results show that the watermarking framework is robust and effective.

In frequency diverse array multiple-input–multiple-output (FDA-MIMO) radar for target detection, besides range migration and Doppler migration, serious Doppler spread in the slow-time domain also will be a problem because the frequency offset between transmitting arrays is coupled with the target velocity, which results in the signal energy of each receiving channel not being coherently accumulated to reduce the detection performance. To address this issue, this letter proposes a method for FDA-MIMO radar moving target detection using planar array. The moving target returned signal of FDA-MIMO radar using planar array is established. After multi-channel matched filtering, resampling in slow-time domain and phase compensation functions of the acceleration and velocity ambiguity factor are applied to correct the range migration and Doppler migration, and compensate for the Doppler spread caused by frequency offset. In doing so, the target’s energy is coherently accumulated. Simulation results verify the effectiveness of the proposed algorithm. Under the same parameter conditions, in order to achieve a 90% detection probability, the proposed algorithm allows the signal-to-noise ratio (SNR) of input echo signal reduction of at least 3dB.

Accurate inverse synthetic aperture radar (ISAR) imaging is vital for target identification, defense, and security. Sparse aperture (SA) and phase errors can seriously affect the quality of ISAR imaging. SA auto-focusing (SA-AF) algorithms could mitigate these challenges. Leveraging deep learning technology, the SA-AF algorithms are unfolded into a deep learning model. However, the deep-unfolded models have fewer learnable parameters and may suffer limited generalization ability. To address these issues, we propose a residual-based auto-focusing algorithm for SA imaging, integrating a deep convolutional network with a deep unfolding network, termed auto-focusing ISAR imaging network that fuses deep convolution and deep unfolding (AF-DCDU-Net). The effectiveness of the proposed AF-DCDU-Net in enhancing ISAR imaging performance has been validated through simulation and real data experiments.

To address the problems of requiring a large number of labeled training jamming samples in practical application, we propose the PSPNet, an improved prototypical network with pretraining and self-supervised fine-tuning, which can achieve high-precision radar active deception jamming recognition with few shots. For the time-frequency images of radar returns including deception jamming, we construct a deep network with multiple convolutional layers as the encoder. The encoder firstly preformes supervised pretraining on the Omniglot dataset with multiple categories, to extract the geometrical characteristics through learning. After that, for domain adaptation, the encoder is fine tuned in a self-supervised paradigm on the simulated unlabeled active deception jamming data. Finally, the encoder using prototypical network is applied to active deception jamming recognition under very few samples. Comparative experiments show that, the proposed PSPNet achieves high accuracy with 97.02% and 98.49% respectively in 5-shot case on both simulation and real data, which outperforms existing methods. The ablation experiments also demonstrate that both pretraining and self-supervised fine-tuning can improve the proposed network’s performance.

Image despeckling is important to synthetic aperture radar (SAR) image restoration and related downstream tasks. Due to the fact that existing SAR image denoising algorithms are difficult to simultaneously achieve high performance, efficiency and interpretability, in this paper, we propose a new image denoising method, namely, Trans-NLM, which incorporates the Transformer architecture into traditional nonlocal means filtering (NLM) based denoising algorithm. In doing so, the SAR image despeckling performance is enhanced, while the algorithm interpretability is retained. First, each pixel and its surrounding pixels are simultaneously mapped to a high-dimensional space as a neighborhood matrix through a shallow fully connected layer. Then, positional encodings are added to the neighborhood matrix, which is further mapped to the internal vectors Key, Query and Value. The final vector representation of each pixel is also calculated according to the multi-head attention mechanism. Finally, the representation vector is passed through a shallow fully connected layer for data dimension reduction to predict the corresponding pixel value. Moreover, layer normalization and residual learning are applied to accelerate the convergence. Experiments on both simulated and real SAR data demonstrate that compared with representative denoising models, e.g., NLM, fastNLM, SAR-CNN, CNN-NLM and MONet, the proposed Trans-NLM exhibits better performance in despeckling enhancement and efficiency simultaneously, along with more explainable inference process and transfer learning capability.

Database sharing may bring about privacy disclosure and illegal redistribution. A previously proposed entry-level Differential Privacy FingerPrinting mechanism (DPFP) for relational database achieves privacy and liability guarantees simultaneously. However, it is only robust against common attacks from a vicious Data Analyzer (DA) and lacks robustness against logical AND or OR collusion attack even if Anti-Collusion Code (ACC) is used to trace who the colluders are. In this work, we propose a Collusion-Resilient entry-level DP FingerPrinting mechanism (CRDPFP) for uniquely identifying colluders by directly using ACCs. Specifically, we firstly theoretically and experimentally demonstrate the vulnerabilities of existing fingerprinting schemes by identification of logical AND/OR collusion attack. To survive 5 types of collusion attacks and identify colluders, a Group-oriented Concatenated (GC) ACC based on I-code and Cover Free Family code is constructed and a catch-all detector is designed. By leveraging the randomization nature of fingerprint, we transform GC code into provable entry-level DP guarantees on the entire database. We also show that CRDPFP inherits the same connection properties between privacy, fingerprint robustness, and database utility from DPFP. Via experiments on two real-world relational databases, we exhibit that our mechanism supplies stronger robustness against 50% random flipping attack from a vicious DA, achieves higher and lower detecting rates of at least one colluder and innocent, uniquely traces all colluders for logical AND or OR collusion attack and obtains near-optimal utility with fingerprint parameter being close to 2 compared to existing schemes.

Since the deep learning based SAR despeckling models rely heavily on the labeled training data, and struggle to process noisy images with varying noise distribution, this paper proposes an unsupervised SAR despeckling model based on the diffusion model which consists of a forward and a reverse processes. In the forward process, the noise with Gaussian distribution is gradually added to the clear image in the logarithmic domain until the image is heavily contaminated. Then in the reverse process, the noise of the image is gradually predicted and removed by the U-net like neural network until the image is close to the clear image. Furthermore, this paper proposes a shifting and averaging based algorithm for processing high resolution image in patches separately, which gets rid of the dependence on high video memory GPUs. Experiments results demonstrate that the proposed unsupervised despeckling model can be adopted to despeckle SAR images with varying noise intensities simply by adjusting the external parameter values. Though the model’s training does not depend on any clear SAR images, it has close performance compared with advanced supervised models.

Large-scale matrix determinants and linear equations are two basic computational tools in science and engineering fields. However, it is difficult for a resource-constrained client to solve large-scale computational tasks. Cloud computing service provides additional computing resources for resource-constrained clients. To solve the problem of large-scale computation, in this article, a secure and efficient framework is proposed to outsource large-scale matrix determinants and linear equations to a cloud. Specifically, the proposed framework contains two protocols, which solve large-scale matrix determinant and linear equations, respectively. In the outsourcing protocols of large-scale matrix determinants and linear equations, the task matrix is encrypted and sent to the cloud by the client. The encrypted task matrix is directly computed by using LU factorization in the cloud. The computed result is returned and verified by the cloud and the client, respectively. The computed result is decrypted if it passes the verification. Otherwise, it is returned to the cloud for recalculation. The framework can protect the input privacy and output privacy of the client. The framework also can guarantee the correctness of the result and reduce the local computational complexity. Furthermore, the experimental results show that the framework can save more than 70% of computing resources after outsourcing computing. Thus, this article provides a secure and efficient alternative for solving large-scale computational tasks.

In this paper, the non-negligible high-order phases of space-borne synthetic aperture radar (SAR) echo signal in 2-D frequency domain are considered. Based on the advanced hyperbolic range model (AHRM), the analytic method is utilized to obtain the scaling factors together with the range frequency modulation (FM) rate involved in the processing functions. In doing so, a modified nonlinear chirp scaling (M-NCS) algorithm is proposed for high-resolution spaceborne spotlight SAR imaging. Point targets and scene imaging simulations validate the effectiveness of the proposed algorithm.

In this paper, we study the moving target detection problem for frequency diverse array multiple input multiple output (FDA-MIMO) radar under the assumption of Doppler spread (DS) and range migration (RM). Doppler spread is caused by the frequency offset between the arrays being closely coupled with the target velocity. Due to the range migration in each re-ceiving channel in a coherent processing interval and Doppler spread in the joint spatial-time domain, the degraded coherent integration gain will reduce the target detection performance. Even, false targets will appear and interfere with detection when the frequency offset or target velocity is large. To ad-dress this problem, a quadratic scale transform combined with resampling method is proposed to eliminate the DS problem caused by frequency offset and the RM caused by velocity in moving target detection. The effectiveness of the proposed approach is validated by numerical results.

With the worldwide spreading of Coronavirus disease 2019 (Covid-19) pandemic, besides the traditional diagnosing approach, Artificial Intelligence provides additional support for the pre-diagnosis of Covid-19 by using data such as patients' images, and sounds, etc. Being able to recognize Covid-19 positive patients quickly and correctly is the key to preventing the expansion of the disease. However, the existing Covid-19 diagnosis models still face challenges due to the complex network structure and additional medical examination. It takes much time to return a diagnosis result. In this paper, a diagnostic model is proposed as an early work for Covid-19 diagnosis using sound samples. The features of sound signals are expressed by Mel Frequency Cepstral Coefficients, which are input into the Online Sequential Extreme Learning Machine for normal/abnormal detection. Data from an open-source database were used to train the proposed model, the experiments show that using vowel pronunciations the model can achieve an accuracy of 96.4% on average, with about 10 times faster for testing than the Support Vector Machine.

In this letter, we present an adaptive Bayesian detection framework for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The targets are detected in Gaussian clutter with unknown but stochastic covariance matrix. We designed two detectors in the Bayesian framework, namely, Bayesian Rao (BRao) detector and Bayesian Wald (BWald) detector without training data. Numerical results reveal that the proposed detectors outperform conventional non-Bayesian counterparts. It is necessary to note that the BWald detector requires higher computational complexity than the BRao detector.

In this letter, a frequency diverse array (FDA) is introduced into synthetic aperture radar ground moving target indication (SAR GMTI) to mitigate both the blind velocity and Doppler ambiguity problems. Due to the$2\pi $periodicity of the echo phases, notches will occur periodically in clutter cancelers at nonzero velocities, leading to the blind velocity problem. In the proposed scheme, the dependence of the measured blind velocity on the transmission frequency is considered, and a new clutter canceler unaffected by the blind velocity problem is constructed via the integration of multiple cancelers with diverse frequencies. Moreover, to resolve the Doppler ambiguity, along-track interferometry (ATI) based double interferometry is proposed to extend the maximum unambiguous radial velocity (RV). Additionally, search-based clustering is adopted to enhance the precision of RV estimation. Finally, a moving target can be brought into focus and correctly relocated using the estimated RV. Numerical results verify the effectiveness of the proposed method.

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.

In this letter, we consider a target with high-order motion and propose two long-time coherent integration algorithms with low computational cost for target detection via phase compensation and time-reversal transform. Due to the even/odd characteristic, the coupling effect between the range frequency and slow time can be eliminated by constructing the phase compensation function and doing the time-reversal transform. In doing so, the target energy can be accumulated by inverse fast Fourier transform (IFFT) in range frequency and fast Fourier transform (FFT) in slow time. Simulations are given to demonstrate the effectiveness of the proposed algorithms. Moreover, compared with the generalized Radon–Fourier transform (GRFT) and generalized dechirp-keystone transform (GDKT), the proposed algorithms have much lower computational burden.

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.

To address the problem of Doppler ambiguity in low pulse repetition frequency (PRF) radar, we propose the use of frequency diverse array multiple-input–multiple-output (FDA-MIMO) radar to detect high-speed moving targets. This method does not require the radar to transmit multiple-PRF pulses. The possible Doppler ambiguity is resolved by exploiting the multicarrier characteristics of FDA-MIMO radar. The effectiveness of the proposed method is verified by simulation results.

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.

Spaceborne bistatic synthetic aperture radar (BiSAR) system with geosynchronous-earth-orbit (GEO) transmitter and a low-earth-orbit (LEO) receiver has many advantages which are fine resolution, higher signal-to-noise (SNR), shortest revisit cycle. However, BiSAR data are often azimuth variant because of the sum of two hyperbolic range equation which bring technical challenges to image focus. We address an improved nonlinear chirp scaling (NLCS) algorithm in this paper. The key is introduced a cubic perturbation function in the azimuth domain and used an modified NLCS algorithm to complete azimuth compression, the method of series of reversion (MSR) to obtain an accurate signal spectrum. Simulation results shows the effective of our proposed method.

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.

In this study, the authors develop a new algorithm for passive source localisation using time‐difference‐of‐arrival (TDOA) and frequency‐difference‐of‐arrival (FDOA) measurements. In this algorithm, the position and velocity of the target source are successively estimated by formulating two alone semidefinite programming problems, where the variances of the TDOA and FDOA measurement noises can be separated and neglected. Unlike the traditional methods, the authors’ approach does not require the prior knowledge of the TDOA/FDOA measurement noise power. Simulation results indicate that the proposed algorithm outperforms the existing methods under unknown noise power knowledge.

In this letter, we propose a scattered-wave deceptive jamming approach using a frequency diverse array (FDA) radar against spaceborne synthetic aperture radars (SARs). As an emerging array radar, FDA transmits multiple carrier frequencies across its elements, and thus, a multiple deceptive jamming is potentially produced in the range dimension of the SAR image. Moreover, we consider the scattered-wave jamming, since it has low complexity when used to create spurious scenes and control the jammer locations. The theoretical analysis and numerical simulations show that the scattered-wave jamming with FDA can generate multiple fake targets with the number of targets being dependent on the number of FDA carrier frequencies.

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 defocused 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 handle the mosaic SAR imaging. Simulation results validate the effectiveness of the proposed approach.

The weighted structure tensor (ST) can be usually used on edge detection of multi-channel images, including polarimetric synthetic aperture radar (polSAR) images. The traditional weighting method is average weighting, each channel considered to provide the same edge information. But in fact different channels tend to contain different amount of edge information, so the traditional weighting method cannot be adopted to extract full edge information of the multi-channel images. This letter proposes a novel weighting method for ST, in which the weight of each channel is obtained by the eigenvalue-measured way. Experimental results show that the proposed method outperforms the traditional method on edge detection.

Different from traditional single-element and phased-array antennas, frequency diverse array (FDA) uses a slight frequency offset across the elements to produce angle-, range- and even time-dependent transmit beampattern, which provides promising potentials to develop new jamming techniques on synthetic aperture radar (SAR) imaging. In this paper, we propose a novel deception jamming method against SAR imaging, which uses FDA to quickly generate false targets. Moreover, the position of the false target is controlled by the jammer, and the number of false targets is determined by the number of FDA elements. The proposed methods are validated by extensive simulation results with range-Doppler imaging algorithm.

Compared to the monostatic geosynchronous synthetic aperture radar (GEO SAR), GEO bistatic SAR can provide finer spatial resolution and higher signal-to-noise ratio (SNR) with lower system complexity, which leads the GEO-LEO (low earth orbit) SAR be a hot research topic. In this paper, an imaging scheme using cooperative high-orbit and low-orbit satellites is proposed. Firstly, the bistatic range history is extended by Taylor series, and then two-dimensional non-uniform fast Fourier transform (2D-NUFFT) is presented to formulate a bistatic SAR image in the range-velocity domain. The effectiveness of the proposed imaging scheme for GEO-LEO bistatic SAR is verified by simulation data for multi-point targets.

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.

Frequency diverse array (FDA) is referred to an array transmitting linearly increasing carried frequencies across the array elements. Compared to traditional phase-arrays (PA), the difference in multi-frequency characteristics will result in that the effective signal-to-noise ratio (SNR) changes with time, angle and distance. This paper also demonstrates that the S-NR of FDA is does not outperform PA under the same conditions in most of time. The decrease of SNR will greatly degrade the estimation accuracy of Frequency Different of Arrival (FDOA) and Time Different of Arrival (TDOA). The significantly degraded FDOA and TDOA estimation accuracy will make the intercepted emitter position being significantly deviated from its actual position, which also means the achievement of localization deception capability.

A uniform linear motion is generally assumed in traditional synthetic aperture radar(SAR) processing algorithms. However, it is inevitable that if there is 3-D acceleration, the image formulation processing complexity will be significantly increased, especially in highly squinted situations. This paper aims to handle the imaging problems in the highly squinted SAR with three-dimensional acceleration, which utilizes the Taylor formula to expand the distance equation and further resolve it. Based on the formulated range formula, we further utilize the two- dimensional non-uniform Fast Fourier Transform (2D-NUFFT) to obtain focused SAR imagery. The proposed methods are verified with simulation results.

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.

Based on the synthetic aperture radar (SAR) geometric model, a novel method of jamming space-borne SAR by utilizing the array beam-pattern characteristics of frequency diverse array (FDA) is proposed. A small frequency increment, as compared to the carrier frequency, is applied between the FDA elements to produce multiple false targets in the final space-borne SAR imaging. The separations between the false targets are determined by the frequency offset. Based on the imaging geometry in side-looking mode, this paper derives a range-Doppler (RD) algorithm for the space-borne SAR imaging under FDA jamming signals. Simulation results demonstrate the effectiveness of the proposed approach.

High speed MIMO radar system, especially for distributed space-borne radar system, brings large Doppler shifts to radar echoes. In order to search for a set of orthogonal waveforms with low Doppler sensitivity in a shared bandwidth, a waveform set design method by using multiple frequency slopes(MFS) is presented. Firstly, we investigate the autocorrelation function (ACF) and cross-correlation function (CCF) for MFS waveforms. It is deduced that the CCF of MFS waveforms changes distinctly with different step of frequency slope. Secondly, we employ ambiguity function to analyze Doppler sensitivity of the proposed waveforms. The analytical expressions between the waveform parameters and the Doppler shift are derived. Finally, the MFS waveforms for distributed space-borne radar system are designed and it is shown from the simulation results that the designed waveforms can meet the requirements of good orthogonality and low Doppler sensitivity simultaneously.

In this paper, a new feature characterization algorithm based on mutative trend of the received signal amplitude is proposed for recognition of Active Electronically Scanned Array (AESA) radar. In this method, the intercepted model of the reconnaissance receiver is established. With the model of the reconnaissance receiver, signal amplitude is mainly decided by antenna patterns and scanning modes of the different radar system, which consist of mechanical scanning radar, one-dimensional phase sweeping radar and Active Electronically Scanned Array radar. According to the mentioned theories, We design the time window near the extracted peak, extract the amplitude variation feature within the time window, and finally accomplish the identification of AESA radar through the obtained feature. In addition, the following simulation results fully demonstrate the superior performance of our proposed method under the parameter variation.

Three dimensional (3-D) synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range and elevation co-ordinates. For 3-D image focusing multiple signals, acquired along different orbits, are required. In order to obtain the tomographic resolution, this paper apply a novel strategy into geosynchronous synthetic aperture radar (GEO SAR), which allow generating 3-D images, which for each azimuth and range position provide an estimation of the scatters distribution along the elevation direction. In this way, a multi-baseline geosynchronous synthetic aperture radar model is used for several multi-pass acquisitions on the same scene. Experimental results on simulated data show the good performance of the method.

Distributed passive radar imaging has been an emerging topic in radar imaging society because of its low-cost, increased-survivability and robustness. In the inverse problem of distributed passive imaging, location of receivers affects imaging quality a lot, while illuminators of opportunity remain to be uncontrollable. Therefore, we investigate the problem of receiver disposition optimization and propose the optimal scheme to locate those receivers by combining genetic algorithm(GA) with compressive sensing(CS) based imaging technique. Simulation results validate the effectiveness of the proposed algorithm.

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.

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.

Geosynchronous synthetic aperture radar (GEO SAR) data focusing is a more challenging and difficult task than the low earth orbit (LEO) SAR due to the strong 2-D coupling of echo signal induced by the orbital trajectory curvature. The range cell migration (RCM) in the GEO SAR configuration is space variant in both range and azimuth directions, hence standard RCM correction (RCMC) functions developed for LEO SAR are inadequate for GEO. In this letter, a curved trajectory model is proposed, taking into consideration the impacts of “stop-and-go” assumption. Based on the range model, a new data transform is derived to deal with the complicated coupling in GEO SAR. From the derivation, we find that the original Stolt mapping is a special case of the proposed “generalized Omega-k” algorithm. In comparison with the original Omega-k algorithm, this new algorithm can correct more complicated RCM effectively. Finally, simulation results show that the proposed imaging algorithm performs well for large scene focusing in an L-Band GEO SAR system.

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.

In non-carrier Ultra-Wide Bandwidth (UWB) radar, the signal detection under the situation of range-walking and low Signal-to-Noise Ratio (SNR), is a big challenge. For example, the coherent integration will lose the effect and the performance of traditional energy detection algorithm, using the target's range-extended characteristic, will also decrease. Hence, an energy detection algorithm based on cross-correlation accumulation for non-carrier UWB radar is proposed in this paper. The new technique, the cross-correlation processing, makes use of the similarity of the envelopes between the adjacent echoes to carry out motion compensation. Besides, the false alarm probability of the proposed algorithm is analyzed in detail in this paper. The simulation results show that the SNR can be increased by non-coherent integration after motion compensation. And the performance of the proposed method is better than the traditional energy detection algorithm in low SNR situation.

Focusing on the issue of echo data acquire approach in bistatic SAR system. In this paper, the key technology of echo sampling window synchronization is introduced, the effect of echo sampling window synchronization error to the echo data and image performance are analyzed, an adaptive echo sampling window synchronization approach based on amplitude demodulation and phase compare is proposed, which could automatically adapt for the echo pulse repeat interval(PRI), and establish a high accurate synchronized echo sampling window, then a high precise phase bias compare algorithm for the proposed approach is introduced. At last the performance and residual error for the presented approach are analyzed to illustrate the efficiency of the proposed approach. This paper provides a theoretical and engineering reference for the design of echo sampling window synchronization in bistatic SAR system.

Based on an advanced hyperbolic range equation (AHRE), this study presents an advanced Range Doppler (RD) algorithm for Synthetic aperture radar on geosynchronous satellites (GEO SAR) cases. Since the orbit altitude reaches up to about 36,000 km in GEO SAR, the integration time becomes longer and curved flight path is generated accordingly. Thus, the AHRE is introduced in this paper, which can compensate the error of the conventional hyperbolic range equation (CHRE) in GEO SAR. Then, the two dimensional (2-D) frequency spectrum (PTFS) expression of a point target is obtained by the method of series reversion (MSR). Finally the advanced RD algorithm based on the new spectrum is proposed. Point target simulation results show that the presented algorithm obtains fine performance in GEO SAR cases.