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Hypersonic vehicle-borne synthetic aperture radar (SAR) imaging has enormous potential in civilian and military application due to high speed and strong survivability of the hypersonic vehicle. However, owing to high temperature and high pressure, a plasma flow, called plasma sheath, covers the hypersonic vehicle. The electron density of the plasma sheath varies with space and time, and space-time varying plasma sheath will affect hypersonic vehicle-borne SAR imaging severely. In this article, the effect of space-time varying plasma sheath on hypersonic vehicle-borne SAR imaging is investigated. First, the SAR signal model coupling space-time varying plasma sheath effect is established using the transmission line method. The variation law of electron density is based on flow field theory and experiment. The double propagation for SAR signal in plasma sheath and the time sequence of electron density for transmitting and receiving period are investigated and included in the SAR signal model. Moreover, the modulation effect of SAR signal under space-time varying plasma sheath is studied, and the range compressed signal under plasma sheath is derived. Based on the SAR signal model, hypersonic vehicle-borne SAR imaging defocus phenomenon with the presence of space-time varying plasma sheath is systematically analyzed. The simulation shows that the point target imaging result under plasma sheath is composed of several azimuth defocus strips, and in each strip the range dimension defocuses. The results of point target imaging and a SAR image of an area under plasma sheath verifies theory and analysis in this article. Finally, the SAR imaging degradation under different SAR and plasma sheath parameters are studied and the condition that serious defocus occurs is analyzed. The work in this article will benefit the study of hypersonic vehicle-borne SAR imaging method under plasma sheath in the future. [ABSTRACT FROM AUTHOR]

Traditionally, inverse synthetic aperture radar (ISAR) imaging and target tracking are two separate and independent processes. ISAR imaging is usually achieved after data collecting and tracking for a long time, resulting in the problems of low imaging efficiency, information loss, and probing resource allocation difficulty. In this article, we proposed an integration method of tracking and imaging with wideband radar by constructing the relation among signal, data, and image domains, which can realize accurate tracking and high data-rate imaging at the same time. In this integrated imaging and tracking method, a complex-valued reference high-resolution range profile (HRRP) is generated and predicted by exploiting the relation among range-Doppler image, echo in time domain, and target's motion state. Then, a Bayes estimation model is built to extract range phase error between the predicted HRRP and the real-measured HRRP, and the Kalman filter can update the motion state. In addition, in order to increase the efficiency, the decentralized Kalman filter is also introduced to realize the subaperture parallel processing and data fusion in this article. Finally, the sequential motion compensation is carried out to realize accurate motion compensation and sequential imaging. The simulated and real-measured data confirm the proposed algorithm's robust imaging and tracking performance. Experimental results show that the proposed method has excellent efficiency and can simultaneously realize sequential imaging and accurate tracking. [ABSTRACT FROM AUTHOR]

In the field of synthetic aperture radar (SAR), a lot of researchers have tried to study an improved application beyond the conventional two-dimensional image. Video-SAR (ViSAR) is one of the hot issue in the SAR application. The main purpose of ViSAR is to monitor various targets in the area of interest in time order. It can offer video images to detect not only the fixed target but also the moving target. For the signal processing of ViSAR, this article introduces the improved performance by the Doppler shifting technique with fractional Fourier transform. And also, it shows that the proposed method can separate the unexpected signal of the moving target within each frame of single-channel ViSAR. Furthermore, the overall algorithm can increase efficiency and reliability in ViSAR processing. To verify the improvement of our method, we carry out the practical experiments by an X-band chirp pulse SAR system, mounted on an airplane. [ABSTRACT FROM AUTHOR]

This article describes how to form a SAR image without proper motion quantities. That is within the scope of factorized geometrical autofocus (FGA). The FGA algorithm is a fast-factorized back-projection formulation with adjustable geometry parameters. Subapertures are tuned and merged pair-by-pair (base-2) and step-by-step. With this technique, we can correct an erroneous geometry and form a focused image. The FGA algorithm has been applied on two datasets, acquired by the UWB CARABAS 3 system at UHF-band. The tracks are measured accurately by means of a DGPS. We however adopt and modify a geometry model. Equidistant linear tracks at fixed altitudes are assumed. These tracks are then regulated via a two-stage search strategy and a reverseprocessing procedure. As this is a first experiment at UHF-band, we provide GPS-based length values for the subapertures, to simplify the search. Multiple geometry solutions are tested for each subaperture pair, i.e., at each resolution level. Resulting FGA images are compared to reference images and verified to be focused. This indicates that it is feasible to form a focused image with wavelength resolution at UHF-band, i.e., with minimum support from a motion measurement system. [ABSTRACT FROM AUTHOR]

Since frequency diverse array multiple-input multiple-output (FDA-MIMO) radar possesses additional target range information for potential performance improvement, this article studies adaptive distributed targets detection for FDA-MIMO radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets occupying several secondary range cells, which is different from the classic detection models in multiple-input multiple-output (MIMO) and/or phase array (PA) radars that discuss only point-like targets. By exploiting the FDA-MIMO radar framework for distributed target detection, we propose the detector through a two-step generalized likelihood ratio test criteria without the need of training data and/or a priori covariance matrix. Moreover, closed-form expressions for the probability of false alarm and detection probability are derived, respectively. The proposed detector adheres to the property of a constant false alarm rate because its probability of false alarm is not restricted by the covariance matrix. The proposed method together with all theoretical analysis are verified by numerical results. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar ground moving target indication (SAR-GMTI) is an attractive mode for radar system to obtain high-resolution regional images and detection of moving targets simultaneously. Moreover, the 360° observation capability of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. In order to address the problem that slow-moving targets, which are buried in strong stationary ground clutter, are difficult to be detected, a framework of multichannel SAR-GMTI is presented in this article. For ground moving target detection in the presence of stationary clutter, the proposed method first adopts clutter suppression interferometry and relaxation-based cyclic algorithm to suppress clutterand retrieve parameters (e.g., radial velocity and Doppler information). Then, to further reduce the false alarm rate, multiple moving target tracking algorithm is performed in range–Doppler domain. Finally, the moving target trajectory is reconstructed by using the proposed two-stage parameter estimation method based on Doppler characteristic and CSAR geometry, and the mathematical analysis of this method is performed. The proposed method is robust and insensitive to the signal-to-noise ratio. It does not rely on priori road information, so it has wide applicability, especially in military application. In addition, the framework is performed in echo domain and independent of imaging processing. The experimental results on simulated data are presented to evaluate the estimation accuracy of the proposed method, and the real data processing results are provided to demonstrate the validity and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

To develop a 3-D imaging synthetic aperture radar(SAR) algorithm, a theoretical expression for a Fourier transform of data cube is required. In this article, we introduce a function representing the Fourier transform of 3-D SAR data cube. The function is derived using the method of stationary phase and similar to the one used for 2-D data matrix. For verification and evaluation, a 3-D range migration algorithm using this function for bulk compression is examined. The simulation results based on the parameters of an experimental terahertz indoor SAR testbed show that the algorithm can well focus the 3-D image and, hence, verify the derived function. The image quality assessments such as spatial resolutions and peak sidelobe ratio are used to further evaluate the function and the algorithm. The measured results are compared with the ones provided by a backprojection algorithm. [ABSTRACT FROM AUTHOR]

Due to the employment of frequency increment across the array elements, frequency-diverse array (FDA) produces a jointly range-angle-time-dependent beampattern, which finds wide applicability in improving the target detection performance and imaging resolution capability. By the exploitation of the multifrequency transmission across elements, this article proposes an FDA-based space–time–frequency deceptive jamming approach against spaceborne synthetic aperture radar (SAR) by using the range-Doppler algorithm (RDA) for high-resolution imaging process. We analyze the range-azimuth distribution of false targets generated by the FDA jammer and then reveal the controllability of the number and locations of false targets by adjusting the frequency increment and the number of FDA elements. In order to set the FDA jammer parameters properly, we derive the minimum/maximum allowable frequency increment, the maximum number of elements and maximum azimuth offset by the ambiguity function, and Doppler frequency analysis. The effectiveness of the proposed approach is validated by extensive numerical results. [ABSTRACT FROM AUTHOR]

A 360° video synthetic aperture radar (VideoSAR) appears as an interactive observation capability to understand the dynamic scenarios. VideoSAR system mounted on an unmanned aerial vehicle (UAV) delivers a highly flexible and cost-effective approach. In this letter, the UAV-borne repeat-pass circular stripmap (CSM) VideoSAR observation is formulated in the format of video area overflown to enhance the wide-area surveillance capability. A completely two-dimensional interpolation-free polar format algorithm is employed, especially in terms of repeat-pass CSM-VideoSAR configuration. We conduct the repeat-pass CSM-VideoSAR experiments on multirotor UAV-borne system in the interests of imaging demonstration and promoting the inspirations on the related open issues. [ABSTRACT FROM AUTHOR]

This article proposes an automatic identification system (AIS) data aided Rayleigh constant false alarm rate (AIS-RCFAR) ship detection algorithm of multiple-target environment in synthetic aperture radar (SAR) images. This method aims to improve the detection performance in complex environment with the aid of AIS data. Traditional CFAR detectors generally use all the samples in the local background window for parameter estimation. However, in multiple-target environment, clutter edges and transition areas, due to the interference of the high-intensity outliers, such as target pixels, ghosts, and other interfering pixels, the parameters are often overestimated, causing degradation of the detection performance. Aiming at solving this problem, AIS-RCFAR designs an adaptive-threshold based clutter trimming method with an adaptive-trimming-depth aided by AIS data to effectively eliminate the high-intensity outliers in the local background window while greatly sustaining the real sea clutter samples. Maximum-likelihood-estimator with a closed-form solution is proposed to precisely estimate the parameters using the adaptively-trimmed clutter samples, the probability density function of the sea clutter following Rayleigh distribution can be accurately modeled. AIS-RCFAR greatly enhances the detection rate in both homogeneous and nonhomogeneous multiple-target environment, it also achieves a very low false alarm rate. In addition, the whole procedure of AIS-RCFAR is simple and efficient. Simulated data and real SAR images with corresponding matched AIS data are used for experiments to validate the superiority and feasibility of AIS-RCFAR. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) systems, operating at L-band or lower, are very susceptible to ionospheric scintillation. The scintillation phase error (SPE) can bring about serious azimuth decorrelation and resolution loss. Compared with the motion phase error, the SPE takes on a much stronger spatial-variation both in along-track and across-track orientations, and its anisotropic feature makes the compensation more intractable. In this article, the phase gradient autofocus (PGA) is used for the first time to compensate the spatial-varying and anisotropic SPE for spaceborne SAR images. It begins with achieving a reliable SPE estimate by applying PGA to a strong scatterer. According to the preknowledge of the phase screen altitude and the anisotropic elongation heading, the emphasis is then on estimating the SPEs for other pixels from the well-estimated SPE, based on the mutual correlation and overlapping of SPEs. The pixel-by-pixel strategy is adopted for the final compensation. The proposed methodology is very appropriate for SAR images with few corner reflectors, which is validated on a simulated L-band stripmap data and a PALSAR-2 spotlight real data. It shows more powerful utility than the classical PGA. The performance analysis is implemented with regard to two necessary factors, which further confirms the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

The multinode multibaseline interferometric synthetic aperture radar measurement system is the future development trend in the field of aviation remote sensing. The deformation error between the nodes directly affects the position and orientation error compensation of the load at the node, and the baseline length error directly affects the interferometric phase error compensation. Aiming at the particularity of the wing structure, a structural deformation measurement and baseline measurement method based on the strain mode is proposed. The fiber Bragg grating sensor is used to acquire the strain of each measuring point on the wing in real time, and the deformation of the wing is accurately measured by the modal superposition method, thereby realizing the high-precision measurement of the baseline between the nodes. The measurement result of the dual total station is the benchmark, and the validation of the method was carried out on the existing test system in the laboratory. Under static conditions, the experimental test results show that the wing deformation measurement accuracy is better than 1 mm, and the baseline measurement accuracy is better than 0.5 mm. [ABSTRACT FROM AUTHOR]

Deep learning has been gradually used to solve SAR image classification problems for its desired performance on various recognition problems. A deep variational autoencoding model (DVAEM), that constructs a multi-stochastic-layer generative network (decoder) and variational inference network (encoder), can be employed to build a flexible and interpretable model for the SAR image target recognition task. It is scalable in the training phase and fast in the testing stage, and can extract the hierarchical structured and interpretable features from SAR images. However, the current DVAEM extracts the features of SAR images unsupervisedly, without incorporating the label information, and may fail to extract discriminative representations for the recognition task. In this article, to jointly model SAR images and their corresponding labels, we further propose supervised DVAEM with Euclidean distance restriction (rs-DVAEM), which enhances the discriminative power of latent representations of SAR images. Notably, our proposed rs-DVAEM combines the flexibility of DVAEM in describing the SAR images and the discriminative power of supervised models. Experimental results on the moving and stationary target acquisition and recognition public dataset demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article deals with the problem of angle and incremental range (i.e., the target range offset with respect to the center of the cell under test) estimation with a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar exploiting a single data snapshot as observable. Starting from the observation that the maximum likelihood (ML) estimation entails a 2-D grid search over the parameters of interest, three approximated ML techniques are designed resorting to the coordinate descent algorithm and the adaptive monopulse criterion (employing either real or complex slope/bias corrections). At the analysis stage, the estimation performance of the proposed methods, including the tapered and double-step monopulse versions, is also assessed in comparison with the Cramér–Rao lower bound. Numerical results corroborate the effectiveness of the considered estimation strategies in some diverse simulated scenarios. [ABSTRACT FROM AUTHOR]

Special attention has been devoted to multi-input multioutput synthetic aperture radar (MIMO SAR) systems over the past decades. However, performances of such systems are seriously limited by the imperfect orthogonal waveforms. The nonideal orthogonality would introduce ambiguous energy, and therefore degrades the signal to clutter ratio dramatically. In order to suppress the ambiguous energy, the interpulse phase modulation (IPPM) and range-Doppler decouple filtering scheme was proposed. However, this scheme requires a multiplied pulse repeat frequency (PRF), and thus degrades the swath width and/or the azimuth resolution. In this article, the IPPM waveform scheme is further developed to avoid the required increasing of the PRF. With the developments, the MIMO SAR can simultaneously produce multiple higher signal to noise ratio image data without degrading the ambiguities. The multiple images can be used either to realize multimodal operation or to further enhance the noise equivalent sigma zero. Theoretical analysis is validated by numerous simulations. [ABSTRACT FROM AUTHOR]

Satellites clusters for Earth observation purposes entail precise orbit monitoring and control to satisfy application needs efficiently. Fast technological progress in GNSS receivers, intersatellite communications/vision links, and propulsion technologies have inspired and fostered autonomous orbit determination and control techniques, allowing more efficient ground segment operations. Moreover, new distributed satellite missions have been enabled by autonomous formation flying, with unprecedented Earth observation potential. These missions may adopt varying observational modes associated with different relative orbital configurations and also transit among these modes as required by mission control. In this article, we consider a hypothetic distributed synthetic aperture radar (SAR) mission with a main SAR illuminator, as CONAE's SAOCOM 1 satellites, and one or more companions able to adopt different bistatic SAR geometries, enabling interferometry, tomography, or specular acquisition. To approach this problem, we formulate a basic task for any actively controlled companion satellite (deputy satellite) consisting of following a reference maneuver relative to the chief satellite orbit. This task is performed by means of a feedback control law based on newly introduced relative orbital elements (ROEs) and suitable to continuous actuators (e.g., electric propulsion) with bounded thrust. As a main result, we prove the asymptotic stability of the maneuver orbit tracking task for low Earth, nonequatorial, and near-circular chief orbits. This required a reformulation of the relative orbit tracking problem and the associated ROEs. A control allocation along the orbit is also proposed to reduce the propellant consumption and the transient time between SAR modes. The theoretical results are validated with simulated controlled transitions between different acquisition modes. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel concept of cross-learning in order to improve synthetic aperture radar (SAR) images by learning from the camera images, in the manifold domain. We present multilevel abstraction approaches to materialize knowledge transfer between these two very different modalities (i.e., the radar and the camera), namely, a canonical correlation analysis-based approach and a manifold alignment-based approach. We provide experimental results on real data, along with qualitative as well as quantitative analyses, to validate the proposed methodologies. [ABSTRACT FROM AUTHOR]

Conventional SAR resolution equations cannot be applied to ultrawideband ultrawidebeam systems, particularly, bistatic ultrawideband ultrawidebeam systems. This is because the dependence of system frequency response in the range and across range is not considered. In this case, area resolution, i.e., area of the resolution cell, will be an alternative. This article introduces a new area resolution formula for bistatic ultrawideband ultrawidebeam SAR systems based on the relationship between radar signal frequency, range, and cross-range wavenumbers for bistatic synthetic aperture radar. [ABSTRACT FROM AUTHOR]

We describe the observation of a previously unreported resonance phenomenon in UWB SAR imaging and its application in a novel scheme for the retrieval of thickness and dielectric properties of scene features. The resonance was observed in a 14–50 GHz laboratory study of very high resolution SAR imagery. It presented as a periodic fluctuation in the backscatter of a scene feature in response to changes in the radar measurement frequency. The complimentary static reflectivity measurements of a range of dielectric materials showed the phenomenon could be understood as a material behaving as a resonant microwave cavity. Modeling simulations were able to accurately reproduce the observed nulling characteristics for visually homogeneous materials (acrylic, MDF, and plasterboard) and which displayed a regular frequency spacing between nulls. Those with inhomogeneous structures (chipboard and plywood) showed much more irregular nulling patterns. The spacing of the backscatter nulls is set by the product of the permittivity and thickness of the material. By exploiting diversity in viewing geometry, the two terms can be separated and measured. The scheme offers a new opportunity in the SAR surveillance and monitoring of man-made structures. [ABSTRACT FROM AUTHOR]

Understanding the decision-making process of deep learning networks is a key challenge that has rarely been investigated for synthetic aperture radar (SAR) images. In this article, a set of new analytical tools is proposed and applied to a convolutional neural network (CNN) handling automatic target recognition on two SAR datasets containing military targets. First, an analysis of the respective influence of target, shadow, and background areas on classification performance is carried out. The shadow appears to be the least used portion of the image affecting the decision process, compared to the target and clutter, respectively. Second, the location of the most influential features is determined with classification maps obtained by systematically hiding specific target parts and registering the associated classification rate relative to the images to be classified. The location of the image areas without which classification fails is target type and orientation specific. Nonetheless, a strong contribution of specific parts of the target, such as the target top and the areas facing the radar, is noticed. Finally, results show that features are increasingly activated along the CNN depth according to the target type and its orientation, even though target orientation is absent from the loss function. [ABSTRACT FROM AUTHOR]

Maximizing the coherent processing interval (CPI) is crucial when performing passive radar detection on weak signal reflections. In practice, however, the CPI is limited by the target movement. In this work, the extent of the range and Doppler migration effects occurring when using a long CPI to integrate the returns from an $L$ -band digital aeronautical communication system (LDACS) based passive radar is studied. In particular, our simulations underline the extensive Doppler migration effect that arises even for nonaccelerating targets. To this end, the Keystone transform and fractional Fourier transform techniques are combined with the standard passive radar processing to enable the compensation of both range and Doppler migration effects. This nonmodel-based approach is, however, shown to have limitations, in particular for low signal-to-noise ratios and/or multitarget scenarios. To address these shortcomings, a novel model-based framework that allows to perform joint target detection and parameter estimation is developed. For this, a super-resolution sparse Bayesian learning approach is employed. This technique uses a multitarget observation model, which accurately accounts for the underlying range and Doppler migration effects and provides super-resolution estimation capabilities. This is particularly advantageous in the LDACS case since the narrow bandwidth generally limits the separation of closely spaced targets. The simulation experiments demonstrate the effectiveness of the algorithm and the advantages it provides when compared to the standard migration compensation approach. [ABSTRACT FROM AUTHOR]

Statistical modeling of synthetic aperture radar (SAR) data is a crucial step in SAR image processing. In this context, the generalized gamma (GGamma) distribution, which generalizes many common distributions, has been applied to model SAR image statistics. Parameter estimation remains, however, a challenging step that conditions the quality of model fitting to data and, thus, of the required processing. In this article, we propose a novel parameter estimation method for GGamma distribution in the log-domain, named as the maximum likelihood and logarithmic cumulants (ML–LC) method. The ML–LC method constructs a novel scale-independent shape parameter estimator in the log-domain based on the Mellin transform and maximum-likelihood estimation and estimates the distribution parameters based on the multistart local search, gradient descent, and bisection methods, rather than solving the system of highly nonlinear equations in the traditional estimations. The ML–LC method is able to estimate the GGamma distribution parameters more accurately. To assess the performance of our estimation method, we perform the goodness-of-fit test on simulated data and real SAR images. In addition, we apply the ML–LC method in some SAR image processing tasks covering image segmentation, classification, and change detection. The results obtained confirm the interest of the proposed ML–LC method. [ABSTRACT FROM AUTHOR]

The piecewise constant Doppler (PCD) algorithm is a novel radar imaging process recently proposed for the generalized continuous-wave synthetic aperture radar (GCW-SAR). This article presents a detailed theoretical analysis on the PCD algorithm's performance and proposes a further complexity-reduced PCD algorithm with motion compensation (MOCO) suitable for practical applications. First, the difference between conventional SAR imaging and PCD imaging, i.e., the zeroth-order versus the first-order slant range approximation, is revealed. Exact ambiguity function expressions of the PCD imaging in range and azimuth directions, respectively, are then derived. An error function of the PCD imaging as compared with the ideal matched filtering method is further defined and shown to be a function of an image quality factor, which can be used to quantify the PCD imaging performance. Finally, a faster and more flexible imaging process, called decimated PCD algorithm, is proposed, by which the image azimuth spacing can be easily extended, and hence, the computational complexity can be significantly reduced. The decimated PCD implementation incorporated with the MOCO is developed for practical GCW-SAR applications, and its imaging error lower-bounded by the PCD imaging error function is analyzed accordingly. Simulation and experimental results validate the theoretical analysis of the PCD imaging and show that the decimated PCD algorithm can achieve a high imaging quality at low cost. [ABSTRACT FROM AUTHOR]

Recent developments in electronics have pushed miniaturized satellites to the femto-scale, with masses between 10 and 100 g. Although femtosatellites have been proven as a feasible concept, most designs are limited in mission capacity and lifetime due to the lack of environmental protection and onboard propellant. In this article, a novel concept for femtosatellites for earth remote sensing is proposed. In particular, a swarm of femtosatellites are used as elements of a sparse array in orbit to receive radar echoes. They also feature active orbit control enabled by solar radiation pressure to extend their lifetime. A simple active orbit control algorithm has been demonstrated. A mission concept based on a sun-synchronous circular orbit is proposed to maximize the benefit for both earth remote sensing and active orbit control. A synthetic aperture radar mission has been used to characterize their performance. [ABSTRACT FROM AUTHOR]

Low-bit quantization of echo improves storage and leads to more efficient downlink transmission of spaceborne synthetic aperture radar (SAR) systems. In this paper, a new parametric quantized iterative hard thresholding (PQIHT) algorithm is proposed to refocus the images of moving targets with low-bit quantized SAR data, based on the combination of quantized iterative hard thresholding (QIHT) and the parametric sparse representation. The blurred and quantization-error-involved subimage of the region of interest (ROI) containing the moving target is represented in a sparse fashion through an adaptive parametric dictionary. The QIHT with a pruned searching method is performed for efficiently estimating the motion-adaptive parameter inside the dictionary, refocusing the ROI image and suppressing the quantization-induced error in an iterative way. Different from the conventional QIHT algorithm with a fixed dictionary that can only represent stationary targets, the proposed method exploits a parametric dictionary with a parameter related to target motion status, which is capable of adaptively representing the radar echo from a moving target with unknown motion status and, therefore, is suitable for moving target refocusing. Simulations and experiments on real GF-3 satellite SAR data demonstrate that, compared with the conventional parametric sparse representation framework for moving target refocusing based on purely precise data, the proposed algorithm can provide satisfactory quality of moving target refocusing with remarkably reduced data volume. [ABSTRACT FROM AUTHOR]

Existing effect analyses of antenna vibration (AV) on synthetic aperture radar (SAR) image, which are all aimed for low-earth-orbit SAR and based on the linear trajectory, second-order azimuth spectrum, and range time-invariant as well as azimuth time-variant antenna gain, are not feasible in geosynchronous SAR (GEO SAR), because GEO SAR has an obviously curved trajectory, high-order azimuth spectrum, and two-dimensional (2-D) time-variant antenna gain. To overcome these problems and analyze the effects of AV on GEO SAR image, a modified analysis is proposed in this paper. First, based on the curved trajectory, the high-order ideal echo signal model and the ideal point spread function (PSF) of GEO SAR are, respectively, established and deducted, and the AV that consists of antenna translational vibration (ATV) and antenna rotation motion (ARV) and the 2-D time-variant antenna gain in squint mode are also, respectively, modeled. Then, based on the series expansion, the paired echo theory, and the aforementioned models, the echo signal model affected by ATV, the corresponding PSF affected by ATV, the antenna gain model affected by ARV, and the corresponding PSF affected by ARV are obtained, and the effects of ATV and ARV on GEO SAR image are, respectively, analyzed in detail. Compared with existing effect analyses of AV on SAR image, the modified analysis considers the influences of curved trajectory, high-order azimuth spectrum, and 2-D time-variant antenna gain simultaneously, and it has much more accuracy and adaptability correspondingly. Finally, computer simulations validate the correctness of the proposed analysis. [ABSTRACT FROM AUTHOR]

A deceptive jamming method against terrain observation by progressive scans synthetic aperture radar (TOPSAR) that considers the effect of the changing relative position between a jammer and a TOPSAR platform is presented. The method can generate flexible false targets with varying Doppler centroids. The jamming problem is decomposed into simpler subproblems of the same type, according to the relative position between the jammer and the satellite. The partial deceptive jamming modulation function can be pregenerated before the radar parameter reconnaissance. The partitioned scheme produces multiple deceptive jamming batches in parallel on the TOPSAR image to protect large regions of interest. [ABSTRACT FROM AUTHOR]

In this paper, we extend the forward-scanning synthetic aperture radar (SAR) methodology to reconstruct images of the moving targets, for a forward-looking automotive radar. We adapt a matrix decomposition approach to forward-scanning SAR in order to separate moving targets from clutter/stationary objects. To solve our optimization problem, we propose an iterative solution based on augmented Lagrangian method. Image focusing, over the synthetic aperture, is achieved through spatial segmentation and cross-correlation maximization. Our proposed method results in well-focused imaging of the moving targets with enhanced angular resolution. Experimental results from simulation as well as real-data corroborate our proposed methodology. [ABSTRACT FROM AUTHOR]

A new matching algorithm is proposed by fusing the histogram of oriented gradients’ and the Zernike moments’ descriptors extracted from the real-time synthetic aperture radar (SAR) images and the real-time elevation maps (REMs), respectively. Both the SAR images and the REMs are acquired by an on-board interferometric SAR simultaneously. The two reciprocal descriptors are fused by weighting their Canberra distances. The numerical experiments demonstrate the advantage of the proposed matching algorithm preliminarily. [ABSTRACT FROM AUTHOR]

To compensate the range cell migration and Doppler frequency spread of a maneuvering target during the integration time, this paper proposes a new parameter estimation method based on sub-band dual-frequency conjugate (SDFC)–Radon chirp rate–quadratic chirp rate algorithm. In this method, SDFC process is first applied to reduce the equivalent frequency and Radon chirp rate–quadratic chirp rate algorithm is then performed to obtain the estimates. Both simulated and real-data results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We propose a new compressive sensing scheme that enables fully polarimetric radar imaging from a subset of channel measurements. Previous sparse recovery methods for polarimetric radar have not modeled channel coupling. We recognize that mixing of information through coupling is key to dropping a channel. Our method complements fast and slow time sampling and may reduce hardware and communications costs. This paper provides the signal model and examples of successful four-channel recovery when only three are measured. [ABSTRACT FROM AUTHOR]

We propose a new deceptive jamming method using networked receivers to perform deceptive jamming against multimode synthetic aperture radar. Using multiple receivers to measure the time difference of beam arrival, we solve the instantaneous modulation parameters precisely via linear equations. We then solve the time delays and frequency offsets of the deceptive signal without approximation to avoid accumulating electronic reconnaissance errors with traditional deceptive methods. Additionally, we design the distribution of receivers to minimize the condition number, reducing our method's sensitivity to measurement error. Simulation results prove the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Noncoherent signal models are widely applied to angular superresolution in forward-looking imaging radars. However, the relative phase influences the resolvability of the model in coherent applications. We reveal the reason why phase shifts affect deconvolution and propose a coherent signal model by replacing the antenna power pattern with the radiation pattern. REGU, TSVD, and IAA are utilized in numerical tests and simulations. Experimental results have demonstrated significant resolution improvements for forward-looking imaging in coherent scanning radars. [ABSTRACT FROM AUTHOR]

The homodyned-K (HK) distribution is a three-parameter density function used to study mixed-species radar and ultrasound scattering phenomena. We analyze the distribution that results from corrupting the HK with additive Gaussian noise (HKN). This modification enables analytic study of the HK in the presence of system thermal noise in radar and ultrasound systems. The moments of intensity of the HKN are derived and a new moment method parameter estimation algorithm is tested in simulation. [ABSTRACT FROM AUTHOR]

Recently, deep neural networks (DNNs) have been the subject of intense research for the classification of radio frequency signals, such as synthetic aperture radar imagery or micro-Doppler signatures. However, a fundamental challenge is the typically small amount of data available due to the high costs and resources required for measurements. Small datasets limit the depth of DNNs implementable, and limit performance. In this work, a novel method for generating diversified radar micro-Doppler signatures using Kinect-based motion capture simulations is proposed as a training database for transfer learning with DNNs. In particular, it is shown that together with residual learning, the proposed DivNet approach allows for the construction of DNNs and offers improved performance in comparison to transfer learning from optical imagery. Furthermore, it is shown that initializing the network using diversified synthetic micro-Doppler signatures enables not only robust performance for previously unseen target profiles, but also class generalization. Results are presented for 7-class and 11-class human activity recognition scenarios using a 4-GHz continuous wave software-defined radar. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient method for maneuvering targets with arbitrary migrations. This method first employs bisection range frequency conjugate to blindly align the envelopes. Then, the recursively parametric scaled correlation transform is applied to obtain the estimates. The proposed method is free of searching, thus significantly improves the computational efficiency on the premise of retaining the performance. Both simulated and real data verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Using low-frequency (UHF to L-band) ultrawideband synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g., bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (natural and manmade) clutter sources in the scene. The problem becomes exacerbated in the presence of noisy responses from rough ground surfaces. In this paper, we present three novel sparsity-driven techniques, which not only exploit the subtle features of raw captured data, but also take advantage of the polarization diversity and the aspect angle dependence information from multichannel SAR data. First, the traditional sparse representation-based classification is generalized to exploit shared information of classes and various sparsity structures of tensor coefficients for multichannel data. Corresponding tensor dictionary learning models are consequently proposed to enhance classification accuracy. Finally, a new tensor sparsity model is proposed to model responses from multiple consecutive looks of objects, which is a unique characteristic of the data set we consider. Extensive experimental results on a high-fidelity electromagnetic simulated data set and radar data collected from the U.S. Army Research Laboratory side-looking SAR demonstrate the advantages of proposed tensor sparsity models. [ABSTRACT FROM AUTHOR]

This paper derives closed-form expressions for bistatic synthetic aperture radar (SAR) spatial resolution of a generalized system from the $k$ -space or the wavenumber domain. These spatial resolution equations have not previously appeared in the literature. From these equations, significant resolution is found in range and cross range, forecasting a forward-scatter bistatic SAR image when the elevation angles of each bistatic platform are significantly different. Simulation and laboratory tests demonstrated the forward-scatter resolution. [ABSTRACT FROM AUTHOR]

In this paper, we propose a forward scanning synthetic aperture radar methodology for a forward-looking automotive (low-terahertz) radar, which combines scene scanning with synthetic aperture processing, resulting in enhanced angular resolution and improved imaging. We propose two algorithms, first, a modified back-projection (BP) algorithm and, second, a compressed sensing-based BP algorithm. We suggest techniques to reduce computational complexity of the proposed algorithms. Results of simulation and real-data experiments corroborate the validity of our proposed methodology and algorithms. [ABSTRACT FROM AUTHOR]

This paper reports a method to analyze surface clutter discrimination for radar sounding interferometry. Nadir-looking low-frequency radar sounders cannot easily resolve off-nadir surface echoes from subsurface nadir echoes. Sounders equipped with multichannel receivers can use interferometric phase to discriminate whether a feature is at nadir or not. We developed a method to evaluate the performance of this application of interferometry and used it to analyze the selected Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) instrument on NASA's Europa Clipper mission to Jupiter's moon Europa. [ABSTRACT FROM AUTHOR]

In the context of radar spaceborne imaging, the novel multichannel staggered synthetic aperture radar (SAR) mode represents a promising solution for the mapping of ultrawide swaths with very high azimuth resolution. This paper extends the analysis of multichannel staggered SAR to planar antenna architectures and provides system design examples both for planar and reflector antenna architectures. Furthermore, a first proof of concept using measured data acquired by an experimental ground-based system is provided to demonstrate the approach's feasibility. [ABSTRACT FROM AUTHOR]

This study proposed a novel algorithm for coastline detection in single-polarization synthetic aperture radar (SAR) images based on the local spectral histogram (LSH) and the level set method (LSM). The proposed algorithm includes two main steps. In the processing step, a rough land/sea segmentation is done by utilizing a texture-based segmentation using LSH. In the postprocessing step, the region-based LSM is used to refine the previous segmentation and extract the coastline accurately. The level set (LS) function is initialized using the results of LSH segmentation. This prevents the development of spurious contours, becoming trapped in the local minimum, eliminates the manual support, and also speeds up the LS evolution. A hierarchical LS regularization is proposed using two Gaussian kernels, which is compatible with noisy images and able to detect very narrow regions. The proposed algorithm is able to detect a coastline at the full resolution of the input SAR image and is also robust to noise. A criterion to quantify the accuracy of the results was also proposed. The experimental results for a number of real high-resolution single-polarization SAR images demonstrate that the proposed method is robust to noise and efficient for the coastline detection in different coastal and sea environments. [ABSTRACT FROM AUTHOR]

Traditional synthetic aperture radar (SAR) systems tend to discard phase information of formed complex radar imagery prior to automatic target recognition (ATR). This practice was historically driven by available hardware storage, processing capabilities, and data link capacity. Here, we characterize the value of phase in SAR imagery when estimating target features utilized in ATR algorithms for target discrimination. [ABSTRACT FROM AUTHOR]

We propose a multimodal and multidiscipline data fusion strategy appropriate for automatic target recognition (ATR) on synthetic aperture radar imagery. Our architecture fuses a proposed clustered version of the AlexNet convolutional neural network with sparse coding theory that is extended to facilitate an adaptive elastic net optimization concept. Evaluation on the MSTAR dataset yields the highest ATR performance reported yet, which is 99.33% and 99.86% for the three- and ten-class problems, respectively. [ABSTRACT FROM AUTHOR]

We propose radar image classification via pseudo-Zernike moments based sparse representations. We exploit invariance properties of pseudo-Zernike moments to augment redundancy in the sparsity representative dictionary by introducing auxiliary atoms. We employ complex radar signatures. We prove the validity of our proposed methods on the publicly available moving and stationary target acquisition and recognition dataset. [ABSTRACT FROM AUTHOR]

The 3-D Zernike descriptors are investigated for an efficient terrain matching algorithm. The maximum order of the 3-D Zernike moments and the template size are determined. The odd-order descriptors are compared with the even-order ones in sensitivity to terrain elevation fluctuation and robustness to noise and rotation. Numerical experiments show that the matching with the odd-order descriptors can achieve as good as or better than that with both the odd-order and the even-order descriptors. [ABSTRACT FROM AUTHOR]

This paper introduces a new concept of slow-time spatial frequency space, also known as “slow-time $k$ -space,” and redefines the associated cross-range bandwidth, which arises naturally from Doppler radar tomography. The slow-time $k$ -space is the most suitable for narrow-band radar imaging, and can be augmented for higher resolution imaging by appropriate signal processing. The respective relationships with the more traditional concepts of fast-time $k$ -space and signal (or range) bandwidth are explained. We also discuss image resolution limits and demonstrate the concepts with simulated data examples. [ABSTRACT FROM AUTHOR]

The bistatic configuration used for inverse synthetic aperture radar (ISAR) imaging has attracted considerable attention because of its potential for overcoming certain monostatic ISAR imaging limitations. However, translational motion compensation (TMC) and rotational motion compensation (RMC) are the most fundamental requirements for both bistatic ISAR (Bi-ISAR) and monostatic ISAR imaging to obtain the high levels of image resolution desired. Moreover, range and cross-range scaling (RCRS) and bistatic distortion correction (BDC) are essential functionalities for the efficient use of Bi-ISAR images in applications. In this paper, a novel approach to sparse aperture (SA) Bi-ISAR imaging is presented for simultaneously performing TMC, RMC, RCRS, and BDC on maneuvering targets based on compressive sensing. Instead of solving conventional optimization problems constrained by the sparsity of the signals, the proposed method utilizes sensing matrix estimation technique for Bi-ISAR image reconstruction using parametric signal model reconstruction by looking for the basis functions that best represent the behavior of sensing dictionary matrix comprised of the observed SA data. The reconstruction of the sensing matrix is based on a modified orthogonal matching pursuit (MOMP)-type basis function-searching scheme. Finally, we generate a focused and scaled Bi-ISAR image from the complete Bi-ISAR signal recovered using the proposed method. Several simulation results reveals that the proposed method is very efficient in forming Bi-ISAR images of high-speed maneuvering targets in terms of the Bi-ISAR signal reconstruction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, an extended fast factorized back projection (FFBP) algorithm is proposed to focus the missile-borne bistatic forward-looking synthetic aperture radar (BFSAR) data. It is also based on the subaperture processing technique to reduce the computational burden, but represents the subimages on the elliptical polar coordinates referenced to the tracks of both transmitter and receiver. First, the imaging geometry and signal model of the missile-borne BFSAR are established. Then, considering the special configuration of the missile-borne BFSAR, a novel elliptical polar coordinate system whose origin is determined by the positions of both transmitter and receiver is established. The subaperture processing approach based on this coordinate system is presented. Successively, based on the range error analysis, the sampling requirements of the elliptical polar subimages are derived to provide a tradeoff between the imaging quality and efficiency. Finally, the computational burden of the proposed algorithm is discussed, and the speed-up factor of the proposed algorithm with respect to the direct back projection algorithm is derived. The experimental results with simulated data prove the validity and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

One-dimensional (1-D) adaptive noise canceler (ANC) has been used for false alarm reduction in low-frequency SAR change detection. The paper presents possibilities to process 2-D data by a 1-D ANC. Beside concatenating the rows of 2-D data in a matrix form to convert it to 1-D data in a vector form, two conversion approaches are considered—concatenating the columns of 2-D data and local concatenation, i.e., the conversion to 1-D is performed locally on each block of the 2-D data. A ground object can occupy more than one row and/or more than one column of 2-D data. In addition, the properties in cross range and range of an image are not the same. Thus, different conversion approaches may lead to different performance of an 1-D ANC and hence different change detection results. Among the considered approaches, the local concatenating approach is shown to provide slightly better performance in terms of probability of detection and false alarm rate. [ABSTRACT FROM AUTHOR]