For tracking a rectangular vehicle, real-world automotive radar position measurements are distributed not uniformly over the vehicle extension but typically around the edges of the vehicle, i.e., the distribution of measurements is shape-dependent. To describe this phenomenon, a shape-dependent Gaussian mixture measurement model is presented, with each mixture component being used to describe a sub-rectangle region by introducing a shape scaling factor. The shape scaling factor is also shape-dependent and can characterize the measurement spread across the corresponding edge. In this model, parameters and mixture structure are highly shape-dependent, and the rectangular shape prior information is also incorporated. Based on the proposed model, a variational Bayesian approach is derived, which recursively and efficiently estimates the kinematic, shape, shape scaling factors, and orientation states of a vehicle. Additionally, the Doppler velocity measurement can also be integrated into the variational Bayesian framework by introducing a latent variable. This approach can effectively and adaptively describe the complex measurement distribution. From the simulation and real experimental results, the proposed approach has a great improvement in the tracking performance, and the superior performance of the proposed model is more significant in estimating the centroid position compared with the state-of-the-art approaches.

For extended object tracking (EOT), the shape of an extended object (EO) is usually fixed (e.g., tracking vehicles), but the orientation varies. Thus, an accurate estimate of the time-varying orientation is important. The orientation and the heading are not always identical but highly dependent, which can be used as additional prior information to improve estimation accuracy, including the orientation. In view of this, this letter proposes a velocity-dependent orientation estimation approach to EOT utilizing this information. First, we model the quantity between the orientation and the heading as a Gaussian noise with zero mean and adaptive variance. Second, based on the proposed model and the integration of a pseudo-measurement, a variational Bayesian (VB) approach is proposed to estimate the kinematic, shape, and orientation states. The proposed approach can adapt to most dynamic scenarios without the need for a sophisticated mathematical model. The effectiveness of the proposed model and estimation approach is demonstrated by using simulated data.

Earth-based radar is a pivotal instrument in deep space exploration to obtain radar images of desired celestial bodies. However, the system performance and image resolution of conventional integrated Earth-based radars with only one radar are limited by the power-aperture product and cannot meet the higher demands of deep space exploration. Distributed coherent radar is a new radar system composed of multiple radar units and a central control system, and its system performance can be further improved by increasing the number of radar units. Distributed coherent radar provides a reliable way to build a high-performance and high-resolution Earth-based deep space exploration system. This article introduces several key technologies about distributed coherent radar astronomical imaging: 1) high-precision coherence parameter estimation, which ensures full-coherence performance of the distributed coherent radar; 2) high-precision nonideal effect compensation, which eliminates the image offset and defocusing induced by the nonideal effects; 3) fast factorization backprojection (FFBP) algorithm, which achieves high-resolution fast imaging of celestial bodies. Moreover, based on a distributed coherent radar prototype system composed of four radar units with an antenna aperture of 16 m, high-resolution imaging experiments of the moon are conducted, and the effectiveness of the distributed coherent radar is successfully validated, which could not only provide a reference for the distributed coherent radar system but also provide a reliable solution for detection and imaging of other celestial bodies in the solar system in the future.

Tomographic synthetic aperture radar (TomoSAR), as a 3-D imaging technique, is widely applied in urban mapping. In our previous work, a multi-master (MM) TomoSAR approach was proposed for the long-baseline observation configuration. This article focuses on the MM TomoSAR for a common observation configuration. The main contribution includes two aspects: theoretical complement and performance extension. For the theoretical complement, the theory of virtual difference coarray (VDCA) in array signal processing (ASP) is introduced to the MM TomoSAR. First, we prove that the MM TomoSAR processing is equivalent to constructing a VDCA in the elevation direction, which essentially explains its principle. Then, to solve the redundancy problem in MM TomoSAR, the equivalent VDCA in the MM model is constructed by selecting the elements from the covariance matrix of the observed signal. Finally, the improved MM TomoSAR processing method is proposed. For performance extension, it is demonstrated that the performance improvement of the MM model is extended from the original two aspects (sidelobe suppression and high estimation accuracy) to include the third aspect (aperture expansion, which improves elevation resolution). The computer simulation and experiment based on TerraSAR-X data verify the proposed method effectively.

The accuracy of the traditional compressed sensing (CS) based tomographic synthetic aperture radar (TomoSAR) imaging is limited by the inappropriate grid partitioning. The atomic norm based processing effectively solves this problem by implementing variable estimation in the continuous domain, that is, avoiding the undesired grid partitioning manipulation. Nevertheless, the performance of the atomic norm based TomoSAR imaging is limited in two main aspects: limited geometry adaptability caused by the uniform sampling requirement and the high computational load. In this paper, a novel atomic norm minimization (ANM) based off-grid TomoSAR imaging is proposed for the fast processing with nonuniform sampling. The main technical contributions are twofold: First, the nonuniformly sampled data is resampled to be uniform where a new geometrical projection-based interpolation is used; Second, the ANM problem is solved by using the non-symmetric cone model to speed up the processing, reducing the computational load fromO(N2) toO(N). The proposed approaches have been verified by the computer simulations and the real data experiments.

The swath of the conventional spaceborne synthetic aperture radar (SAR) is parallel-to-orbit, making it inefficient to observe long curved terrain, like coastlines, railways, etc.. Imaging long curved terrains with the terrain matching (TM) curved swath is a promising technique for efficient data acquisition. The key feature is the employment of a long curved swath matching with the orientations of the long curved terrains. This paper reports the first demonstration of the spaceborne SAR TM curved imaging with the LJ2-01 satellite. A TM curved swath of 161.7 km is imaged with an azimuth resolution of 0.6 m. The main technical contributions are: First, a new electrical-mechanical-combined beam control method is proposed to achieve uniform azimuth resolution; Second, a new non-uniform pulse repetition frequency sequence is used to mitigate the data loss caused by the violent spatial variation of the slant range; Third, a new swath-adaptive sub-aperture time-domain imaging algorithm is proposed for efficient TM curved swath imaging. These innovations contribute to successful data acquisition and imaging of the spaceborne SAR TM curved imaging with the LJ2-01 satellite.

The 3-D inverse synthetic aperture radar (ISAR) imaging technology is widely used for noncooperative targets, which can obtain precise topography, structure, and rotation information of celestial target. However, celestial target observation has the features of long observation distance, low echo signal-to-noise ratio (SNR), and complex rotation characteristic, which severely degrades the performance of traditional 3-D ISAR methods. Therefore, in order to realize high-precision 3-D ISAR imaging of celestial target, a parametric 3-D ISAR imaging method is proposed in this article. First, a 3-D imaging model is established based on the complex rotation characteristic of the celestial target, which indicates that the rotation vector and polar diameter estimation is the key to 3-D reconstruction. Second, in order to overcome the performance degradation of traditional 3-D ISAR methods under low SNR, a parameter estimation method based on hybrid generalized radon-Fourier transform (HGRFT) is proposed, and the core is to use GRFT within subaperture and noncoherent accumulation between subapertures to achieve hybrid accumulation of echo signals, so as to obtain the desired rotation vector and polar diameter fast and accurately. Consequently, the 3-D reconstruction of the celestial target under low SNR can be achieved based on the 3-D imaging model and the parameter estimation results. Moreover, two fast implementations based on parameter search space dimensionality reduction and heuristic search, respectively, are proposed, which can reduce the computational load of high-dimensional space parameter estimation and further improve the algorithm efficiency. Finally, the proposed method is validated by celestial target 3-D ISAR imaging simulation.

Spaceborne multi-channel synthetic aperture radar (SAR) is an effective means to realize high-resolution and wide-swath imaging. However, for spaceborne multi-channel SAR imaging of maritime moving targets, the target motion will cause undesired channel imbalance, i.e., phase error, and further introduce the spurious targets in the image. To solve this problem, this paper proposes a novel spaceborne multi-channel SAR imaging algorithm for maritime moving targets, which consists of sequential coarse imaging and accurate imaging. The key strategies are to separate different moving targets by coarse imaging and to estimate the phase error based on the relationship between phase errors and amplitudes of spurious targets. First, the quantitative relationship between phase errors and amplitudes of spurious targets is established. Second, based on coarse imaging results, different maritime targets are effectively separated. Then, based on the measured amplitudes of spurious targets, a cost function, which represents the difference between the real target velocity and estimated target velocity, is constructed and minimized to separately estimate the velocities and phase errors of targets. Moreover, to further improve the accuracy of estimation and to suppress the undesired effects caused by target defocusing, clutter, and noise, an iterative strategy is adopted. Last, by auto-focusing, a well-focused SAR image is obtained. The GF-3 dual-channel real data experiment is conducted. The results indicate that the spurious targets are well suppressed, which validates the effectiveness of the proposed algorithm.

Due to the advantage of flexible and rapid deployment, unmanned aerial vehicle (UAV)-based synthetic aperture radar (SAR) tomography (TomoSAR) is a promising technology in 3-D urban mapping. The long baseline is indispensable for P-band SAR systems to achieve high elevation resolution. It will introduce two problems. On the one hand, the unavoidable spatial decorrelation brings serious phase noise and sidelobes in 3-D imaging. On the other hand, the noticeable image distortion fails the image registration and the TomoSAR data stack (TDS) construction. Aiming at the above problems, this article proposes a multimaster (MM) TomoSAR approach via three main contributions. First, the traditional TomoSAR signal model is extended to the MM case to improve the number of baselines and the average image coherence of the TDS and suppress the sidelobes. Second, a short-baseline-recursion image registration method is proposed to achieve high-precision image registration. Third, a TDS optimization processing consisting of interferometric SAR (InSAR) phase screening and baseline sign reassignment is introduced. Moreover, a clustering-based outliers’ elimination method is also adopted to ensure the 3-D imaging quality. Computer simulation and long-baseline P-band UAV-SAR experiment validate the proposed approach.

The compressive sensing (CS)-based tomographic SAR (TomoSAR) 3-D imaging method has the shortcoming of low efficiency, mainly represented in two aspects: first, the CS solver requires iterative calculation and hence is computationally expensive; second, the CS solver needs hyperparameters’ selection, which commonly requires cost-inefficient try-and-error attempts. Recently, the iterative CS solver is suggested to be replaced by a deep learning network for a tremendous processing speed improvement. However, the existing deep-learning-based TomoSAR imaging algorithms suffer from the problem of model inadaptability, i.e., being inadaptive to the observation model and the signal energy model and hence is low accuracy. This article proposes a new model-adaptive network (MAda-Net) to implement deep-learning-based TomoSAR 3-D imaging with a much improved processing accuracy. First, a new adaptive model-solving (AMS) module is introduced to solve the problem of the observation model inconsistency between the real spatially varying one and the approximately fixed one used by the network. Second, a new adaptive threshold-activation (ATC) module is introduced to solve the problem of signal energy model inconsistency between the real backscattered echo and the simulated echo for network training. The effectiveness of the proposed method has been verified by the computer simulations and the real unmanned aerial vehicle (UAV) SAR experiments.

Dual-channel synthetic aperture radar (SAR) is a widely used technology to achieve high-resolution and wide-swath imaging in current SAR system. Due to the fact of nonuniform sampling and spectrum ambiguity, the Doppler spectrum must be reconstructed before focusing. However, the traditional spectrum reconstruction algorithms require large onboard storage memory and computational resources and hence, make it hard to realize real-time processing. To solve the problem, this letter proposes a blocked spectrum reconstruction algorithm for onboard real-time imaging. Different from the traditional ones, the new method updates parameters block by block in azimuth, and hence, reducing the storage and computational resources at the cost of sacrificing the accuracy of the spectrum reconstruction to some extent. Fortunately, the accuracy loss is acceptable under properly set focusing depth, i.e., azimuth block width. The simulation and real data experiments verify the proposed approach.

Tomographic synthetic aperture radar (TomoSAR) is a 3-D imaging technology used to overcome the layover problem faced by traditional 2-D SAR systems. The quality of TomoSAR imaging capabilities relates closely to the number of acquisitions (NOAs). However, this number is often quite limited due to cost problems. The previous studies have shown some empirical requirements for the minimum NOAs. In this letter, an analytic constraint between the minimum NOAs and signal-to-noise ratio (SNR) is presented, and this constraint is more precise than the existing empirical one. The signal model is first analyzed, followed by the solution of the Fisher matrix, and finally leads to the constraint between the SNR and the minimum NOAs. The presented approach is evaluated via computer simulations.

Ground-based multiple-input multiple-output (GB-MIMO) radar is capable of performing structural health monitoring through vibration monitoring. However, when a target’s vibration frequency is comparable to the image acquisition frequency of MIMO radar, its vibration can affect the vibration parameters estimation based on the pixel’s phase series. In this letter, the impact of target vibration on the pixel series is analyzed. A robust method for vibration parameter estimation is proposed by operating iteration between the series compensation and estimation. The correctness and effectiveness of proposed method is verified by both simulation and calibrator experiments.

Unmanned-aerial-vehicle-based (UAV-based) ultrawideband and ultrawidebeam (UWB) synthetic aperture radar (SAR) is very sensitive to atmospheric turbulence and suffers from serious 2-D space-variant motion errors (SVMEs) caused by the ultrawide beam and frequency-dependent phase errors caused by the ultrawideband. This article proposes an autofocus approach for UAV-based UWB SAR data based on the quasi-polar grid fast factorized backprojection (FFBP) imaging framework, multiple subband local autofocus (MSBLA), and trajectory deviation estimation. First, based on an improved weighted phase gradient autofocus (WPGA) method for subband-division local images, MSBLA is introduced to solve the local motion error estimation problem with frequency-dependent phase errors. Then, trajectory deviation estimation based on the weighted least square (WLS) method is performed to solve the 2-D SVME problem. Finally, the subaperture trajectory deviations are fused into a full-aperture trajectory deviation by an improved fusion strategy based on piecewise weighting. This approach is applied to real data from a new UAV-based UWB SAR. The results of both simulation and real data experiments are presented and verify the effectiveness of the proposed approach.

Multifrequency (MF) interferometric synthetic aperture radar (InSAR) phase unwrapping (PU) technology is proposed for PU in steep terrains where the phase changes of adjacent pixels exceed the commonly required threshold of$\pi $. Traditional MF PU methods will fail in the case of extremely steep terrain such as artificial buildings due to insufficient quality of phase noise suppression (PNS). In this article, we propose a refined MF-InSAR PU method that can be robustly applied for extremely steep terrain PU via two main contributions. First, an additional steep edge extraction step is introduced for geological local PU window generation to prevent inaccurate PNS across the extracted steep edges. Second, the traditional linear phase model is extended to a nonlinear one for more accurate MF local fringe frequency estimation in PNS. The computer simulations and the real dual-frequency airborne experiment validate the presented approach.

Linear array (LA) multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) has the capacity of achieving 3-D images in a single pass. If processed by matched filtering-based linear 3-D imaging, sidelobe suppression is often required for image quality enhancement. However, in the case of imaging a large target in a short range, sidelobes of the target will become spatially variant and curved, and traditional sidelobe suppression methods will fail. This article proposes a new spatially variant curved sidelobe suppression method for LA MIMO SAR short-range 3-D imaging. The key technique is the employment of a new pseudopolar coordinate system where both the spatial variance and the curvature of 3-D sidelobes can be removed. Specifically, a new 3-D spatially variant apodization (SVA) kernel is applied for sidelobe suppression to maintain the resolution performance. The validity of the presented approach has been demonstrated via computer simulations, the tower crane experiment, and the unmanned ground vehicle experiment.

Due to the extremely high orbital height and long synthetic aperture time, the geosynchronous synthetic aperture radar (GEO SAR) will inevitably suffer from different types of undesired errors, including atmosphere, orbital measurement error, antenna vibration, and scenery height fluctuation; moreover, because of the extremely large imaging swath, these undesired errors also have severe 2-D spatial variance. Thus, the autofocus processing plays a very important role in GEO SAR. However, current autofocus algorithms cannot handle all of the aforementioned complicated and 2-D spatial-variant errors simultaneously. In this article, an autofocus back projection (BP) method for GEO SAR based on minimum entropy is proposed. First, the BP algorithm based on a digital elevation model (DEM) is adopted to deal with the scenery height fluctuation. Then, the 2-D image segmentation is conducted to solve the spatial variance of the undesired errors. Subsequently, without the assumption of error type and considering both the amplitude error and phase error, the autofocus processing based on minimum entropy and adaptive moment estimation (Adam) is conducted to estimate the undesired errors iteratively and precisely. Moreover, the aperture division and sub-aperture fusion will also be utilized to alleviate the image quality degradation or even defocus, which could also improve the precision of error estimation. Finally, computer simulation results validate the effectiveness of the proposed method.

Recently, frequency-modulated continuous-wave (FMCW) radar-based hand gesture recognition (HGR) using deep learning has achieved favorable performance. However, many existing methods use extracted features separately, i.e., using one of the range, Doppler, azimuth, or elevation angle information, or a combination of any two, to train convolutional neural networks (CNNs), which ignore the interrelation among the 5-D time-varying-range-Doppler-azimuth-elevation feature space. Although there have been methods using the 5-D information, their mining of the interrelation among the 5-D feature space is not sufficient, and there is still room for improvements. This article proposes a new processing scheme of HGR based on 5-D feature cubes that are jointly encoded by a 3-D fast Fourier transform (3-D-FFT)-based method. Then, a CNN is proposed by building two novel blocks, i.e., the spatiotemporal deformable convolution (STDC) block and the adaptive spatiotemporal context-aware convolution (ASTCAC) block. Concretely, STDC is designed to cope with hand gestures’ large spatiotemporal geometric transformations in the 5-D feature space. Moreover, ASTCAC is designed for modeling long-distance global relationships, e.g., relationships between pixels of the feature at the upper left corner and lower right corner, and exploring the global spatiotemporal context, in order to enhance the target feature representation and suppress interference. Finally, our presented method is verified on a large radar dataset, including 19 760 sets of 16 common hand gestures, collected by 19 subjects. Our method obtains a recognition rate of 99.53% on the validation dataset and that of 97.22% on the test dataset, which is significantly better than state-of-the-art methods.

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area.

Linear-array multiple-input–multiple-output (LA-MIMO) synthetic aperture radar (SAR) can obtain 3-D radar images by only one pass. However, it is sensitive to time-variant measurement errors of curved track and time-variant attitude angles, meaning that autofocus processing for the LA-MIMO SAR tomography is necessary. The existing autofocus methods cannot be used to estimate thetime-variantand3-D space-variantmotion errors (3-D SVME) of the LA-MIMO SAR. To solve this problem, a new autofocus approach based on multiple local autofocusing and the LA-MIMO SAR time-variant motion error estimation is proposed. First, the local motion error estimation based on the fast local spectral analysis (SPECAN) 3-D imaging and the maximum contrast optimization 2-D local autofocusing is performed to estimate the local time-variant motion errors. Then, based on the linear-array motion error model, the time-variant 3-D trajectory deviations of the array center and attitude angles are estimated by the weighted least square estimation (WLSE) to solve the 3-D SVMEs. Last, the 3-D fast factorized backprojection (FFBP) is performed to obtain the well-focused 3-D image of the whole beam. The proposed approach has been applied for the tomography of a new crawler-type unmanned-ground-vehicle (UGV) LA-MIMO SAR. Both the simulation and real data experiments verify the effectiveness of the proposed approach.

A distributed configuration is a promising realization of tomographic synthetic aperture radar (TomoSAR) 3-D imaging. It is able to implement single-pass tomographic imaging in a very short time and, hence, outperforms the traditional time-consuming multipass TomoSAR. It also outperforms the traditional single-platform TomoSAR by achieving a higher resolution in elevation by forming a much larger spatial baseline. However, there has been little research reported on the distributed TomoSAR so far. In this article, we, for the first time, experimentally demonstrate the great potential of the single-pass distributed TomoSAR 3-D imaging. The main contributions are threefold. First, a new distributed TomoSAR 3-D imaging model is built, characterized by using both inner monostatic and bistatic spatial configurations. Second, a new multistatic synchronization scheme is developed for accurately correcting both multistatic time and phase synchronization errors. Finally, a P-band distributed unmanned-aerial-vehicle (UAV) TomoSAR prototype with four separate stations is built with an elaborately designed time-division waveform for full data acquisition. To the best of our knowledge, this is the first distributed TomoSAR system. We have also implemented the first single-pass TomoSAR 3-D imaging experiment and successfully achieved a meter-level 3-D image in Pinggu, Beijing, China.

In this article, a high-frequency motion error compensation algorithm for mini-unmanned aerial vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR) system is proposed. Compared with the traditional SAR system platform, the mini-UAV is more vulnerable to interference from the external environment. Thus, the motion error of mini-UAV-based BiSAR systems always contains both low- and high-frequency components. Meanwhile, the independent transmitter and receiver sources would introduce more than one high-frequency motion error component, and the variance of the high-frequency motion error also needs to be considered. This article first establishes a high-frequency motion error model and the corresponding Doppler phase signal for the mini-UAV-based BiSAR system. Then, multiple forms and multiple components errors are separated and estimated on the azimuth phase. Next, the complex spatial variance is accurately modeled and compensated by estimating the platforms’ motion errors. It is suggested from the simulation and experimental results that the proposed algorithm can compensate for the motion error with complex forms under the mini-UAV-based BiSAR system condition.

Mini-unmanned aerial vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR) is widely used due to its flexibility of system topology selection and simplicity. However, the motion compensation (MOCO) of the system is more difficult because the perturbance of mini-UAV platforms is greater and the spatial variance is more complex. In addition, the degrees of freedom of motion error are twice as those of the monostatic SAR. In this article, a novel MOCO algorithm based on a motion sensitivity model is proposed. First, a BiSAR system motion sensitivity model is established to assess the importance of each motion component error. Second, a novel MOCO method is proposed based on the determination of the main motion errors. Third, the proposed algorithm is validated by both simulations and real data sets. The final well-focused image suggests that the variant phase error after compensation with the new algorithm is much smaller than that with the traditional one.

Dynamic deformation measurement is an important approach to monitor the structure stability. Due to its rapid imaging capabilities, ground-based multi-input multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This paper proposes a dynamic deformation estimation method based on radar image series. Firstly, an improved clutter suppression method is utilized to overcome the estimation error in complex working status. Secondly, a two-step pixel extraction method is adopted to ensure both quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This paper systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency and amplitude on the bridge structure are obtained by image domain measurement. And for the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions.

Tomographic synthetic aperture radar (TomoSAR) is used to achieve three-dimensional imaging. Existing methods mainly focus on super-resolution without ambiguity. However, the baseline is uncontrollable in ground-based lunar mapping, which leads to long sparse non-uniform baseline with altitude ambiguity. To suppress the altitude ambiguity, this paper proposes an innovative dual-frequency de-ambiguity method. The mechanism is to enhance real targets and suppress the spurious targets by multiplying the images at different frequency points. Specifically, genetic algorithm (GA) is applied to achieve the optimum frequencies with the lowest peak sidelobe ratio. The computer simulation and real data experiments verify the effectiveness of the proposed approach.

Main approaches of observing asteroids rely especially on ground-based optical telescopes, yet not capable of detecting detailed structures of the surface. However, ground-based radar observations can provide delay-Doppler images which help to reconstruct a more accurate shape model. To form the shape model of asteroids, we propose a shape reconstruction method via multi-aspect delay-Doppler radar images and describe the surface through spherical harmonic coefficients. In order to reconstruct shape model as accurate as possible, an optimal solution of the coefficients is required. Three traditional optimization algorithms are applied and performances are compared. When positive decision is made, suitable parameters can achieve effectively with these algorithms followed with an accurately recovered shape of asteroids. Numerical simulations have been conducted to demonstrate the effectiveness of the proposed method.

The spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is a new operation mode for efficiently imaging the regions squinted with satellite orbit. By nonuniformly implementing azimuth sampling, the SS-RSSAR is able to avoid the problem of transmission blockage when the slant range varies seriously. However, this advantage is achieved at the cost of inducing time-varying nadir echo and makes the valid and the nadir echoes inseparable in the time domain. In this study, we propose to suppress the nadir echo of the SS-RSSAR via waveform diversity on transmission. The key mechanism is to always make the valid and the nadir echoes in the same pulse interval have different waveforms characterized by high autocorrelation but low cross correlation performances. In this way, the nadir echo can be effectively suppressed by performing matched filtering with respect to the valid echo. The main contributions of this study are twofold: the analyses for the lowest required waveform number and the strategy of achieving sufficient waveform patterns. The validity of the presented approaches is evaluated via the extended-target computer simulations.

Applying a continuous varying pulse interval (CVPI) sequence is of significance for an extremely high-resolution (EHR) spaceborne sliding spotlight synthetic aperture radar (SAR) for complete data acquisition. An optimum CVPI sequence should be the one leading to the minimum movement of the echoes of interest in a pulse train. However, such an optimization can be hardly conducted using the traditional timing diagram. Aiming at solving this problem, this article introduces a new graphic tool, referred to as the strip layering diagram, to achieve an optimum CVPI sequence for an EHR spaceborne sliding spotlight SAR. The basic rationale is to explicitly employ a new parameter$\eta $to directly control the movement of the echoes of interest in a pulse train. The new strip layering diagram consists of two layered subdiagrams: one is the candidate subdiagram, consisting of the candidate strips presenting all the CVPI sequence candidates restricted by the data acquisition geometry; the other is the feasibility subdiagram, consisting of the feasibility strips marking all the feasible regions where the echoes of interest are not submerged by the transmitted pulses or contaminated by the nadir echoes. An optimum CVPI sequence can be accurately and efficiently achieved by sketching a line segment satisfying four conditions inside the strip layering diagram. The detailed comparisons between the new diagram and the traditional timing diagram are also provided, followed by computer simulations to validate the presented approaches.

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets. Second, the hyperparameters in these methods are hard to tune to optimal values. Third, due to a large amount of calculation, these methods are difficult to apply in practice. In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively. At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions. In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, i.e., the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum. Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net. Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods.

The edges of a target provide essential geometric information and are extremely important for human visual perception and image recognition. However, due to the coherent superposition of received echoes, the continuous edges of targets are discretized in synthetic aperture radar (SAR) images, i.e., the edges become dispersed points, which seriously affects the extraction of visual and geometric information from SAR images. In this article, we focus on solving the problem of how to recover smooth linear edges (SLEs). By introducing multiangle observations, we propose an SAR parametric image reconstruction method (SPIRM) that establishes a parametric framework to recover SLEs from SAR echoes. At the core of the SPIRM is a novel physical characteristic parameter called the scattering-phase-mutation feature (SPMF), which reveals the most essential difference between the residual endpoints of a disappeared SLE and points. Numerical simulations and real-data experiments demonstrate the robustness and effectiveness of the proposed method.

Linear array multiple-input multiple-output synthetic aperture radar (LA-MIMO SAR) tomography can provide 3-D radar images without layover and geometric distortion effects. However, suffering from the channel and motion error, and large computation, the real LA-MIMO-SAR data is difficult to be well focused quickly. Therefore, the 3-D imaging results of real LA-MIMO-SAR data are rarely shown in existing literature. In this paper, real LA-MIMO-SAR data imaging processing method based on 3-D fast factorized Backprojection is proposed to well focus the echoes with channel and motion errors, and large size of data. Then, our experiments of LA-MIMO-SAR 3-D imaging are demonstrated. The LA-MIMO radar is installed to a special tower crane and works in downward-looking and forward-looking geometry. Some 3-D imaging results are demonstrated to verify the downward-looking and forward-looking LA-MIMO-SAR modes and the processing method.

Synthetic Aperture Radar (SAR) tomography is an important technique for target elevation information inversion and reconstructs the 3D structure of the target via multi-pass observations. At present, SAR tomography is mainly used in large-scale, low-resolution scenes where the range between the radar and the target is far larger than the target size and the resolution only meters. Therefore, the high-order phase residue is small and will not affect the elevation recovery. However, in the case of the small-scale, high-resolution scenes, the traditional TomoSAR processing will introduce quadratic phase residuals and affecting the quality of recovery. In order to solve the problem, this paper proposes a new method to reconstruct the 3-D structure in high-resolution scenes via elevation segmentation recovery. The main idea is to establish a reference signal at different height sections of the target area so that the quadratic phase residual is less than π/2. Finally, the estimated result of each section is projectively transformed into a unified coordinate system to achieve stable and accurate recovery. Besides, the algorithm has been verified by simulation data and measured data.

The traditional synthetic aperture radar (SAR) is an active acquisition instrument that generates radiation and captures signals backscattered from the illuminated scene. However, when the phases of each reflection path are highly similar, the received echoes may sum in a destructive way. In this way, only a few strong scattered points of targets can be preserved in the traditional images. To recover the disappeared geometric features of the targets in SAR images, this paper proposes a parametric imaging method for circular-plate targets. Through analyzing the scattering models of various targets, the scattering feature of the circular-plate target in image domain is first presented. And by identifying the target type in image domain, the geometry information lost in the traditional image is recovered based on the scattering models. Numerical simulations have been conducted to demonstrate the effectiveness of the proposed method.

The spaceborne stripmap range sweep synthetic aperture radar (SAR) (SS-RSSAR) is a new-concept SAR mode proposed to efficiently image regions of interest (ROI) squinted with respect to satellite orbit. The most distinct feature of the SS-RSSAR is the beam sweeping in the elevation for continuous squinted ROI illumination. A wide nonlinear chirp scaling algorithm (WNLCSA) has been proposed to directly implement image formation along the ROI spreading direction. However, the algorithm efficiency is limited due to its time-consuming time-domain interpolation. In this letter, a new interpolation free WNLCSA (IF-WNLCSA) is proposed for more efficient SS-RSSAR imaging by substituting the interpolation with a much faster chirp scaling approach. Different from the interpolation, the chirp scaling induces additional range migration and Doppler modulation, both of which, however, are neglected due to their weak influences on imaging. The derivation of the chirp scaling is presented in detail, followed by the analyses on the algorithm accuracy and efficiency. The presented approach is evaluated by both the point- and extended-target simulations.

Penetrating radar systems are widely used to image the objects that are buried inside mediums (such as walls, ground, and so on). However, due to the phase error caused by the refraction of mediums, the object images obtained by directly applying the imaging methods that ignore the existence of the mediums are defocused, which affects the recognition of small objects. Conventional focus imaging algorithms typically obtain a focused image by iteratively searching for optimal compensation, which is very time-consuming and can result in overfocusing. To solve this problem, a near-field phase cross correlation (PCC) focusing imaging algorithm is proposed in this article. First, a free-space imaging model is established to replace the unknown half-space (air-to-medium) imaging model. Then, by analyzing the relationship between the free-space and the unknown half-space imaging models, the phase error and a reference depth in free-space imaging model are determined. The focused image can then be obtained by compensating for the phase error, which is directly estimated by the proposed PCC method. The permittivity and object depth can then be estimated based on the slope of the phase error and the reference depth. Furthermore, the proposed algorithm can be extended to the multi-layered model in a straightforward manner. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed PCC algorithm can effectively compensate the phase error and obtain high-quality images, and the permittivity and object depth can be accurately estimated in single-layer medium cases.

In low-contrast synthetic aperture radar (SAR) images, the contrast between roads and surrounding objects is low; therefore, many false roads will be introduced in the process of extracting road networks. To solve this problem, a two-step road network extraction framework is proposed. In the first step, the edge information of the road is extracted using a linear detector. To reduce false edges, a false edge removal algorithm based on the directional information of the edges is proposed. In the second step, an improved region growing (RG) algorithm is proposed, which can substantially improve the integrity of the road network extraction compared with the traditional RG algorithm. Finally, the proposed algorithm is validated by GF-3 satellite SAR images.

Existing ship inverse synthetic aperture radar (ISAR) imaging algorithms are not applicable, when the signal-to-noise ratio (SNR) is low, for the translational motion that cannot be well compensated by existing algorithms. To achieve ship ISAR imaging with low SNR, a ship ISAR imaging algorithm based on the generalized radon-Fourier transform (GRFT) is proposed in this paper. Considering not only the rotational motion but also the translational motion between the radar and the ship, the proposed algorithm uses the GRFT to simultaneously compensate the time-variant range envelopes and the Doppler phase. Thus, the signal coherence is fully utilized, and the coherent integration of the ship's multicomponent echo signal is realized. Subsequently, to overcome the problem of the heavy computational load and improve the efficiency of the proposed algorithm, the scheme of cascaded GRFTs that consists of the coarse GRFT and the subsequent fine GRFT is adopted. The coarse GRFT with large search ranges and intervals is aimed at obtaining the real ranges of ship scatter points' motion parameters. Based on the coarse GRFT result, the fine GRFT with small search ranges and intervals is performed to efficiently obtain the coherent integration result. Then, based on the coherent integration result, the constant false alarm rate (CFAR) detection is performed to obtain the desired scatter points and their amplitudes and motion parameters, and the multicomponent signal is reconstructed. Finally, based on the reconstructed multicomponent signal, the high-quality instantaneous ship ISAR image can be obtained. Computer simulations and experiment results validate the effectiveness of the proposed algorithm.

Due to the inherent shortcomings of the hardware, the immersion of visual interaction between the user and the virtual reality (VR) equipment is greatly reduced. In this paper, effects of eye-around vibration haptics on improving the VR immersion were studied. The vibration was generated by flexible vibrators whose performance was evaluated by a laser vibrometer. Fitting the vibrators on the human eye area at different positions and derived by different waveforms and frequencies of the input signal, the effects of vibration on the human vision and comfort of the users were verified. Then, with the selected input signals and fitting locations, different kinds of vibration were applied on the eye area cooperating with virtual reality images or videos to evaluate the changes of immersion. Research results provide references to the modeling of eye tactile feedback and the design of relevant tactile device in improving the VR immersion.

Ground-based synthetic aperture radar (GB-SAR) technique has been widely applied for the deformation monitoring and measurement of the natural and engineered slopes. To extend the 2-D deformation measurement from the conventional 1-D measurement along the radar-target line of sight (LOS), multiple aperture interferometry (MAI) techniques based on phase differences between interferograms of the forward-looking and backward-looking subapertures are tackled in this letter. The optimal subaperture selection is analyzed considering the typical signal-to-noise ratios and correlations in GB-SAR applications. Simulations prove that the coherent integration (CIM) can be utilized to improve the measurement accuracy of the MAI method. Besides, GB-SAR experiments are carried out to validate the feasibility and effectiveness of the 2-D deformation measurement method based on MAI. Accuracy comparison of deformation measurement with the MAI and cross correlation methods is also taken. Experimental results show that the accuracy of deformation measurement along the perpendicular direction to LOS based on MAI and CIM can reach millimeter level for displaceable corner reflector.

The detection of a power transmission tower in a synthetic aperture radar (SAR) image has been widely studied. However, few works consider the geometric features of the power transmission tower. In a high-resolution SAR image, the geometric features of the power transmission tower are more obvious and can be used to further reduce the false-alarm probability. In this letter, a new power transmission tower detection method is proposed, which takes into account the geometric features of the target and obtains lower false-alarm probability than the traditional methods. First, the polar coordinate semivariogram is proposed, which has the advantages of low computational complexity and high sensitivity to the shape of the targets. Then, a three-layer neural network is employed to detect the power transmission tower, taking the geometric features as the input vector. Finally, the validity of the proposed method is illuminated by the experimental measurement results of the airborne data with 0.5-m resolution.

Echolocation is the ability to use sound-echoes to infer spatial information about the environment. Some blind people have developed extraordinary proficiency in echolocation using mouth-clicks. The first step of human biosonar is the transmission (mouth click) and subsequent reception of the resultant sound through the ear. Existing head-related transfer function (HRTF) data bases provide descriptions of reception of the resultant sound. For the current report, we collected a large database of click emissions with three blind people expertly trained in echolocation, which allowed us to perform unprecedented analyses. Specifically, the current report provides the first ever description of the spatial distribution (i.e. beam pattern) of human expert echolocation transmissions, as well as spectro-temporal descriptions at a level of detail not available before. Our data show that transmission levels are fairly constant within a 60° cone emanating from the mouth, but levels drop gradually at further angles, more than for speech. In terms of spectro-temporal features, our data show that emissions are consistently very brief (~3ms duration) with peak frequencies 2-4kHz, but with energy also at 10kHz. This differs from previous reports of durations 3-15ms and peak frequencies 2-8kHz, which were based on less detailed measurements. Based on our measurements we propose to model transmissions as sum of monotones modulated by a decaying exponential, with angular attenuation by a modified cardioid. We provide model parameters for each echolocator. These results are a step towards developing computational models of human biosonar. For example, in bats, spatial and spectro-temporal features of emissions have been used to derive and test model based hypotheses about behaviour. The data we present here suggest similar research opportunities within the context of human echolocation. Relatedly, the data are a basis to develop synthetic models of human echolocation that could be virtual (i.e. simulated) or real (i.e. loudspeaker, microphones), and which will help understanding the link between physical principles and human behaviour.

The tropospheric time variance within the ultra-long integration time of Geosynchronous Synthetic Aperture Radar (GEO SAR) needs to be considered for imaging. Meanwhile, because of the curved trajectory and the very complex geometry relationship between the target position and the satellite, the traditional analysis method of troposphere on low earth orbit (LEO) SAR imaging will no longer be suitable in GEO SAR. In this paper, GEO SAR signal phase errors induced by the time variation of troposphere is obtained. Then, a GEO SAR signal's two-dimension frequency spectrum under the effects of the troposphere is derived for the first time. The GEO SAR 2-D image shift and image defocusing phase errors are derived analytically. At last, the atmospheric refractivity profile (ARP) data provided by the Chinese meteorological satellite FengYun-3 (FY-3) are used and the focusing results are presented to verify the aforementioned analysis.

Low signal power density limits the performance of space-surface bistatic synthetic aperture radar (SS-BiSAR) using Global Navigation Satellite System (GNSS) satellites as illuminators. To tackle this problem, in this letter, a novel bistatic SAR image enhancement technique based on repeat-pass coherent fusion is proposed. The works in this letter include three aspects. First, repeat-pass experiments are designed to ensure the best resolution. Second, a modified CLEAN technique is applied to remove the direct signal interference from the focused BiSAR images. Third, a coherence-processing method is proposed to implement coherence of each repeat-pass BiSAR image and then they are coherently fused to obtain a quality-improved BiSAR image. Twenty-two days of repeat-pass BiSAR experiments with Beidou-2/Compass-2 inclined geosynchronous orbit satellites as illuminators have been designed and conducted. The data were processed by the proposed method. The results show that the method can obtain better image quality compared with the traditional noncoherent fusion method and the single-day imaging result, which validates the proposed method and proves the huge potential in realizing local area monitoring with SS-BiSAR using GNSS satellites as illuminators.

The background ionosphere time variance within the extremely long integration time of Geosynchronous Synthetic Aperture Radar (GEO SAR) needs to be considered for GEO SAR focusing. Meanwhile, because of the curved trajectory and the very complex geometry relationship between satellite motion and earth rotation, the traditional analysis method of background ionosphere on LEO SAR imaging will no longer suitable in GEO SAR. In this paper, a GEO SAR signal model is proposed to describe the effects of total electron content (TEC) variance within the long integration time. Then, a GEO SAR signal two-dimensional spectrum under the effects of background ionosphere is derived for the first time. The GEO SAR defocusing phase errors are derived analytically. At last, the US Total Electron Content (US-TEC) data are used and the focusing results are presented, verifying the analysis.

Geosynchronous Synthetic Aperture Radar (GEO SAR) will be affected by ionosphere scintillation inevitably because it usually works at L band. In this paper, GEO SAR signal model in presence of ionospheric scintillation is proposed. The scintillation sampling model is employed to simulate ionospheric scintillation data. Several GEO SAR imaging simulations in different scintillation cases and a real ionospheric scintillation measurement in Zhuhai district of China are conducted to study the impacts of ionospheric scintillation on GEO SAR in azimuth. The results suggest that ionospheric scintillation will worsen the azimuth resolution, and rise azimuth peak sidelobe ratio (PSLR), and severely deteriorate azimuth integrated sidelobe ratio (ISLR).

Elliptical orbits are widely used in the system design of geosynchronous synthetics aperture radar (GEO SAR) to improve the temporal resolution. The orbit height variation in the elliptical orbit can cause the variation of nadir interference and beam illumination region. This shortens the available time of a designed pulse repetition interval (PRI) for a specific beam pointing, and multiple PRIs have to be used at different orbital positions. To lengthen the available time, a continuous PRI variation method is introduced in this paper. By varying the PRI with a constant time interval, the acquisition window and transmit interference changes with the variant beam illumination region, which loosen the constraint of the transmit interference. A design example of the proposed method is provided with significantly lengthened available time.

In this paper, a creative hybrid adaptive filtering algorithm is designed to combine a modified mode filter with an adaptive median filter for the suppression of interferometric phase noise which is a quality insurance of the following phase unwrapping operation. This combination provides the hybrid filtering algorithm with remarkable flexibility to terrain. At first, this paper presents a modified mode filter with an adaptive shortest sub-interval estimator. Then, it uses the mode filtering result as the variable local phase center of the median filter and improves it with an adaptive filtering window varying with the residue density and correlation coefficient. Finally, a hybrid adaptive filtering algorithm is proposed for the reduction of interferometric phase noise. The studies utilize the simulated Peak data and the real Ayers Rock data as data support. Through analyzing the phase residues and the PSD values of the filtering results, we finally prove the validity of this method.

The magnitude error and phase error (MEPE) in the transfer function of a stepped-frequency synthetic aperture radar (SAR) system results in a periodic MEPE in the synthesized wideband waveform, which induces the grating lobes in the high-resolution range profile. In this letter, a novel grating lobe suppression method based on the SAR image is proposed. In the paired-echo theory, a single sinusoidal term of the periodic MEPE in the frequency domain induces a pair of grating lobes in the time domain. Based on the magnitudes and phases of a strong scatterer and its grating lobes in the SAR image, the sinusoidal terms in the periodic MEPE can be estimated using the proposed method. By compensating for the estimated sinusoidal terms in the spectrum reconstruction, the corresponding grating lobes can be suppressed to the background level of the SAR image. The validity of the proposed method has been demonstrated using computer simulations and experiments based on real data.

Geosynchronous synthetic aperture radar (SAR) (GEO SAR) has the characteristic of long integration time; thus, the time-freezing model assumption of background ionosphere for traditional low Earth orbit (LEO) SAR no longer holds in GEO SAR. Furthermore, the background ionosphere variation within the integration time cannot be omitted either. In this letter, the variation of total electron content within integration time is analyzed and described in detail by using polynomial approximation, and a new GEO SAR signal model influenced by background ionosphere is also proposed. In view of this novel model, the analytical expression of image shift and defocusing phase error are derived in the first place. Then, a quantitative analysis for the image shift and image defocusing in the range and azimuth directions is conducted, and the performance bounds of time-varying parameters of background ionosphere effects on focusing are obtained. Finally, the U.S. Total Electron Content measured data are used to verify the theoretical results of background ionosphere effects on GEO SAR focusing.

The aircraft altitude relative to the illuminated region is a significant parameter for the geometric correction in squint mode synthetic aperture radar (SAR) imaging. Error in the parameter causes geometric distortion, which degrades the image quality. To circumvent this problem, a relative altitude estimator (RAE) is proposed based on the relationship between the relative aircraft altitude and the range variation of the Doppler centroid. The high performance of the RAE is investigated compared to the Cramér–Rao lower bound of the relative aircraft altitude. Finally, the effectiveness of the RAE in practical applications is verified by real data processing.

This paper analyzes the multiangle imaging results for bistatic synthetic aperture radar (BSAR) based on global navigation satellite systems (GNSS-BSAR). Due to the shortcoming of GNSS-BSAR images, a multiangle observation and data processing strategy based on BeiDou-2 navigation satellites was put forward to improve the quality of images and the value of system application. Twenty-six BSAR experiments were conducted and analyzed in different configurations. Furthermore, a region-based fusion algorithm using region-of-interest (ROI) segmentation was proposed to generate a high-quality fusion image. Based on the fusion image, typical targets such as water area, vegetation area, and artificial targets were compared and interpreted among single/multiple-angle images. The results reveal that the multiangle imaging method was a good technique to enhance image information, which might extend the applications of GNSS-BSAR.

The challenge in the imaging of high-squint-mode synthetic aperture radar (SAR) mounted on maneuvering platforms is the azimuth dependence of both the range migration and the azimuth focusing parameters (the azimuth frequency-modulation rate and higher order coefficients), which are caused by range walk correction and acceleration. In order to accommodate the dependence, a modified subaperture imaging algorithm is proposed. Based on the fact that the azimuth times corresponding to the same Doppler frequency are different for targets in the same range gate, after making blocks in the azimuth frequency domain, the azimuth-dependent range cell migration correction is performed in the azimuth time domain for each block. Considering that the regions of support in the azimuth frequency domain are different for targets in the same range gate, the equalization of the azimuth focusing parameters is achieved by a new azimuth nonlinear chirp scaling method in the azimuth frequency domain. In order to verify the effectiveness of the proposed algorithm, the simulation of a point target array is presented. Furthermore, the real SAR data with a squint angle of 70° are processed, and high-quality images with a resolution of 1 m are provided.

This paper presents the theory, algorithm, and results of a new bistatic interferometry synthetic aperture radar (InSAR) method. It employs the data acquired in an innovative bistatic configuration, which uses the orbital sensors as transmitters of opportunity and the stationary receivers on the ground, to generate a digital elevation model (DEM). In the bistatic spaceborne/stationary InSAR configuration, the interferometric phase only depends on the target-receiver range, which could not be obtained directly from the measured bistatic range. Therefore, the conventional transforming relationship between the interferometric phase and the topographic height is no longer practical. In order to solve the problem, we introduce a new conversion relationship between the interferometric phase and the topographic height, which is derived by the model of the ellipsoidal projection in the bistatic configuration. Meanwhile, the error analysis of the new conversion is carried out through a simulation. Both the simulated and measured data are used to test and verify the feasibility of the new bistatic InSAR method. In the spaceborne/stationary InSAR experiment, YaoGan-3 (an L-band spaceborne SAR system launched by China) was selected as the transmitter and two stationary receivers were mounted on the top of a tall building. The generated DEM of high quality shows that the presented method performs very well in the bistatic InSAR data process.

We present a theoretical analysis for InSAR height measurement in sand-covered arid areas based on two-layer model. The influence of radar wave's penetration on height estimation is mainly considered. If the returned signal from the subsurface is dominant, InSAR is potential to measure the subsurface height. Taking the refraction at the interface and the change of propagation velocity in the sand layer into account, a modified InSAR method is proposed in this paper. Simulations are performed to validate the proposed subsurface InSAR method.

For satellites in geosynchronous orbit (GEO) using synthetic aperture radar (SAR), the direction of the satellite velocity vector in the Earth-centered, Earth-fixed (ECEF) coordinate system varies widely in one orbital period. When the velocity vector is approximately parallel to the range direction at equatorial latitudes, the 2-D sidelobes of the generalized ambiguity function (GAF) on the ground are non-orthogonal, which results in a significant deterioration in the ground resolution. To improve the ground resolution, this study investigates an optimal resolution steering (ORS) method for GEO SAR. The ORS method differs from the total zero Doppler steering (TZDS) method, which attempts to minimize the Doppler centroid. In the ORS method, a beam pointing direction is derived based on the constraints of optimal ground resolution. Then, a 2-D roll-pitch strategy is derived to obtain small steering angles, and the beam is steered to the optimal beam pointing direction. Finally, the ORS method is verified with simulations.

Geosynchronous (GEO) SAR has been proposed as a means for obtaining observations of the Earth with finer temporal sampling than possible with a single satellite from a lower orbit. However, standard algorithms developed for low-Earth-orbit SAR imaging are inadequate for GEO, where the typical assumptions of quasi-linear trajectory and “stop-and-go” transmit/receive propagation break down because of the long integration time and the very long range between the satellite and the Earth. This paper proposes a curved trajectory model to overcome these limitations and considers the impact of the “stop-and-go” assumption. According to the proposed range model, an accurate 2-D analytical spectrum is deduced under the curved trajectory model based on a series reversion method, leading to an improved frequency domain imaging algorithm involving a high-order-phase coupling function and a range migration correction function. An adaptive azimuth compression function overcomes the space variance for large-scene focusing. Simulation results validate that the improved imaging algorithm performs well over the expected range of applicability for GEO SAR.

Synthetic aperture radar (SAR) simulation of 3-D forest canopies is a powerful tool for studying the interaction between radar and forest, for testing new applications, and for devising inversion algorithms of forest structures. SAR raw-signal generation is frequently used in point-target simulation but is rarely used in 3-D forest simulation. The existing simulators directly produce SAR images based on an impulse response function (IRF) without involving raw-signal generation and various nonideal factors. In this paper, a novel simulator to produce SAR images of 3-D forest canopies is proposed. It incorporates a SAR raw-signal generation process taking account of various nonideal factors such as trajectory deviation of radar platforms and complexity of natural environments, which is more faithful to realistic remote sensing systems. Furthermore, an approach to speed up the raw-signal generation is put forward based on the equivalent scattering model consisting of a few virtual scatterers with specially calculated positions and backscattering matrices. Thus, the raw signals received from the entire forest canopy can be equivalent to those from virtual scatterers in the case of tiny slant-range errors. The error sensitivity of equivalent conditions is analyzed, and the optimum selection of equivalent parameters is derived considering the compromise between precision and efficiency. The results of simulation and forest height inversion demonstrate the feasibility and potential utilities of the proposed simulator.

This paper proposes an autofocus method based on minimum-entropy criterion for synthetic aperture radar imaging. Through minimizing the entropy, an iterative method is derived to obtain the phase error, which corrupts the image and increases the image entropy. In addition, dominant-scatter areas are selected as the input of the phase-error estimation. Note that there is not any model assumption for the phase error and the dominant-scatter area isn't required to only contain one point scatter. The phase estimation accuracy and efficiency are also analyzed in this paper. Finally, the simulation and experimental results are used to validate the feasibility and effectiveness of the proposed method.

Airborne synthetic aperture radar (SAR) raw data are affected by motion errors due to atmospheric turbulence and aircraft properties, and this makes motion compensation (MOCO) an essential step. In order to decrease the influence of variable motion information, a kind of MOCO approach called azimuth-subaperture processing is widely used, which causes the azimuth-signal aliasing in squint SAR mode. Usually, this aliasing can be ignored, since its effect on final results is not worth mentioning. However, when the squint angle grows, it becomes serious and drops the quality of final images severely. To address this problem, an improved MOCO approach is proposed in this paper. Its main idea is to calculate the extended number of subapertures and eliminate the azimuth aliasing by extending the subaperture length. After theoretic analyses, simulations and experimental results are provided to demonstrate our proposed approach.

Aiming at the structural feature and polarimetric property preservation in speckle reduction of polarimetric synthetic aperture radar images, an improved speckle reduction algorithm based on structural judgment and hybrid four-component polarimetric decomposition (HPD) is developed. To protect the structural feature, a new structural judgment based on edge masks and pixels' sort is used for dividing pixels into three structural categories: bright, dark, and common targets. To protect the polarimetric property, a new scattering classification with HPD is employed to divide further the dark and common targets into four scattering classes. HPD inherits the physical significance of Pauli and Yamaguchi decomposition with its advantage of less time consumption. After that, without bright targets, a dark or a common pixel centered in a sliding window is filtered. To achieve better filtering results, pixels in its same and three neighboring scattering clusters from the same structural category are selected to filter. The experimental results of the UAVSAR and RADARSAT-2 data illuminate the validity of our proposed method.

This paper proposes an extended nonlinear chirp scaling (CS) image formation algorithm for the bistatic synthetic aperture radar systems with the squinted transmitter and a fixed receiver. Since the transmitter with the squint mode was adopted in the system, two main problems, i.e., the spatial variance of the frequency-modulation rate and cubic phase terms, were introduced in the image formation algorithm. The former problem was solved by the linearity approximation of parameter$p$and deduced$q$(the second- and third-order coefficients of CS factors in range, which could be used to remove the spatial variation and high-order phase in the range direction) along the range domain while the latter one was compensated by a cubic analytical phase term in the frequency domain. A corresponding experimental hardware system and the bistatic experiments were also described in this paper. Both the simulation and experimental results validated the proposed algorithm.

The determination of Doppler ambiguity number (DAN) is indispensable for the generation of high-quality synthetic aperture radar (SAR) images. An incorrect DAN leads to lower image signal-to-noise ratio and degradation in the system impulse response function. Based on the relationship between DAN errors and the image quality, a novel DAN estimation algorithm named image-quality-based Doppler ambiguity resolver is presented. In the proposed algorithm, the DAN-dependent linear range cell migration correction and DAN-dependent azimuth compression are carried out in subscenes to obtain 2-D compressed SAR images, which are further employed to estimate DAN via minimizing the entropy. The presented algorithm is more robust than conventional methods to cope with demands of both low- and high-contrast scene applications. In addition, the approach is computationally efficient because it can be properly applied to segments of range-compressed data in small size and only a few sets of short fast Fourier transforms are required. Finally, experiments over real data of airborne X-band and spaceborne C-band are carried out to demonstrate the performance of the proposed approach.

This paper proposes a method of focusing the bistatic synthetic aperture radar (SAR) (BiSAR) data in spaceborne/stationary configuration. The key problem for imaging is the space variance of Doppler phase. The stationary platform induces additional and different range offsets to the range migration of targets. It causes targets with the same Doppler history, which are determined only by the moving platform, to shift into different bistatic range cells in the echo data. Therefore, the processing is not the same as monostatic SAR imaging which can be fast performed by the uniform matched-filter function in the frequency domain. In this paper, a modified nonlinear chirp scaling (NLCS) algorithm based on series reversion is formulated, which could achieve different range cell migration correction and the equalization of effective range and azimuth frequency modulation rates. The proposed algorithm is validated by simulated and real BiSAR data. In the spaceborne/stationary BiSAR experiment, the YaoGan-1 (an L-band spaceborne SAR system launched by China) is selected as the transmitter, and the stationary receiver is mounted on top of a tall building. The results show that modified NLCS algorithm can effectively focus BiSAR data with serious space variance in spaceborne/stationary configuration.

In geosynchronous (GEO) synthetic aperture radar (SAR), the synthetic aperture time (SAT) is very long and the trajectory mode should be considered as curve. This paper mainly analyzes the impacts of target azimuth position displacement on GEO SAR image formation. Because GEO SAR imaging need be based on the curved trajectory, targets' azimuth position displacement on GEO SAR image formation can affect targets' defocusing. This point will be analyzed by comparing two-dimensional (2-D) spectrum in GEO SAR and low earth orbit (LEO) SAR and some simulations. In the imaging formulation, to focusing a large scene, we present an improved secondary range compression (SRC) algorithm. In this proposed SRC algorithm, the azimuth compression function can adapt itself by changing the azimuth displacement. Finally, a large scene's imaging under the long SAT is realized.

Synthetic Aperture Radar (SAR) has been proved as an effective instrument for detecting vertical structures of forest canopies. However, the quality of SAR images could decline because of phase errors caused by the residual motion deviations after motion compensation or autofocus process. Then, the accuracy of forest height inversion will be affected. In this paper, the relationship between residual phase errors and the accuracy of the retrieved height is established via three-dimensional forest canopies simulation of SAR. The residual motion deviations with different amplitudes and frequencies are considered in simulation experiments. Reasonable levels of estimated phase errors to reach high accuracy of retrieved height are achieved. It is demonstrated that higher accuracy of estimated phase errors is demanded for forest height inversion based on SAR data.

This paper mainly focuses on synthesizing high resolution profile for stepped-frequency radar signal. A novel method based on correlation coefficient is presented. Compared to traditional methods, the limitation of the frequency step size is relaxed. It means that the same range resolution can be achieved with less pulse number, thus reducing the complexity of the system design. Finally, the simulation data is used to demonstrate the performance of this proposed method.

This paper proposes a highly accurate bistatic range migration algorithm (RMA) for space-surface bistatic synthetic aperture radar (BiSAR) systems with asymmetric configurations when the transmitter moves along a rectilinear trajectory and the receiver stays at a fixed location. The main work here includes three aspects. First, by introducing a 2-D change of frequency variables, an analytical 2-D spectrum formula without any approximation was derived at the first time. Second, with the linearity approximation of the derived phase, a highly accurate bistatic RMA was proposed and guaranteed that the algorithm could be applied to focus the echo from a large target scene. Furthermore, the nonlinear phase of the linearity approximation was analyzed to avoid the defocus effect in the final BiSAR image. Both simulation and indoor experimental results are presented to validate our analysis.

The paper firstly analyses the error caused by the linear trajectory model and the Fresnel approximation because of the long synthetic aperture time in Geosynchronous Synthetic Aperture Radar (GEO SAR), and then proposes an improved Secondary Range Compression (SRC) focusing algorithm to overcome the effect of linear trajectory model and the Fresnel approximation. The improved focusing algorithm adopts the curved trajectory model on basis of Norm operator to remove the error caused by the linear trajectory, derives and compensates the third order phase in two-dimensional frequency domain to overcome the large range migration. Finally, imaging results verify the improved focusing algorithm.

Yaw steering is a method of Doppler centroid compensation performed by attitude steering of the satellite platform. However, the conventional yaw steering method does not work well as the satellite orbit height becomes higher, such as with the geosynchronous Earth orbit synthetic aperture radar (GEO SAR) which has 36 000-km orbit height. This letter primarily analyzes the Doppler properties of the GEO SAR in the satellite local coordinate systems, and gives the calculation formulas of the Doppler centroid frequency and instantaneous Doppler bandwidth. Based on the vector analysis, a new calculation method is proposed for the 2-D attitude steering angles in GEO SAR. The new method, considering Earth's rotation and elliptical orbit effects, can accurately compensate the Doppler centroid to zero. Then, according to the 2-D attitude steering angles and the characteristics of GEO SAR, the 2-D phase scan method is put forward to carry out the highly accurate compensation of Doppler centroid, which can avoid the difficulty of rotating and stabilizing the GEO SAR system. The simulations validate the conclusion in this letter.

In Geosynchronous SAR (GEO SAR), because of the increase of orbit height, the signal propagation delay time reaches up to hundreds of milliseconds, and the synthetic aperture time also reaches up to hundreds of seconds which will result in a curved synthetic aperture trajectory, thus the “Stop-and-Go” assumption and conventional imaging methods of low earth orbit SAR (LEO SAR) will lose effect in GEO SAR. In addition, because the angular velocity of earth rotation is approximately equal to that of satellite rotation, the Doppler parameter and resolution analysis in LEO SAR cannot be directly used too in GEO SAR either. In this paper, firstly, the accurate slant range model in GEO SAR is created based on the consideration of the error of “Stop-and-Go” assumption, and then the improved imaging method is proposed to compensate for the error of “Stop-and-Go” assumption and the effect of the curved synthetic aperture trajectory. Finally, based on the generalized ambiguity function (GAF) and projection theory, the accurate Doppler gradient vector is analytically obtained based on the consideration of earth rotation in GEO SAR, and the accurate resolution calculation in arbitrary direction is also derived in detail. All the simulation results verify the correctness and effectiveness of the proposed imaging method and resolution analysis method.

In this paper, considering the relative motion between satellite and earth during signal propagation time, the accurate analysis method for propagation slant range is presented in geosynchronous SAR. Furthermore, the difference between accurate analysis method and `Stop-and-Go' assumption is analytically obtained. Meanwhile based on the derived accurate slant range model, it is found that the corresponding range migration correction and azimuth reference function must consider the high order term, and therefore the modified SPECAN algorithm is proposed. The simulation results verify the correctness of `Stop-and-Go' assumption error derivation and SPECAN algorithm modification.

This paper presents a method to focusing the general bistatic synthetic aperture radar (BiSAR) data. First, using the extended Taylor series, the general BiSAR data are transformed into the azimuth-invariant BiSAR one. Second, the monostatic frequency scaling algorithm is extended to the bistatic one. The numerical experiment indicates that our methodology is valid and the proposed algorithm can process the data of the general BiSAR.

The key problem of the imaging processing in geosynchronous earth orbit (GEO) SAR system is the space-variance of the Doppler parameters, which will result in the defocus of the SAR image with classical imaging algorithm. In this paper, a Modified Range Migration Algorithm (RMA) is proposed to overcome the space-variance of Doppler parameters by compensating the velocity change along the location of the target. Furthermore the simulation results fully verify the effectiveness of derived algorithm after velocity compensation.

Interference fringes will be generated in SAR image when distributed targets' echo signal is simulated because of the uniform model of targets. The fringes can be suppressed by adding random height in space or random phase on backscatter coefficient of the distributed targets. However, the former method will introduce location error and the latter method will increase speckle noise twice in simulated image. In this paper, we develop a high accuracy method for fringes suppression based on targets' position randomizing and RCS re-sampling via bilinear interpolation. The validation of method is successfully proved by simulation results and the performance of the method is discussed on accuracy and computational complexity at the end of this paper.

The purpose of this paper is to solve the imaging problems of one-stationary bistatic synthetic aperture radar (BiSAR) squint mode. Through the analysis of the target's two-dimensional spectrum, an improved chirp scaling algorithm (CSA) is got. It can deal with the space variability of this kind of BiSAR and the simulation is done to verify the validity and feasibility of this algorithm.

The concept and preliminary analysis of the Interferometric SS-BSAR system is discussed in this paper.

The tool for avalanche prediction based on the Space Surface Interferometric Bistatic Synthetic Aperture Radar (SS-InBSAR) is discussed in this paper. The forthcoming Galileo navigation satellites signals are considered as the electromagnetic source of the novel bistatic SAR.