Three-dimensional Synthetic Aperture Radar Research Articles

Innovative Rotating SAR Mode for 3D Imaging of Buildings

Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high-resolution capacity, and is an important tool for the monitoring of building health. Buildings have geometric distortion in conventional 2D SAR images, which brings great difficulties to the interpretation of SAR images. This paper proposes a novel Rotating SAR (RSAR) mode, which acquires 3D information of buildings from two different angles in a single rotation. This new RSAR mode takes the center of a straight track as its rotation center, and obtains images of the same facade of a building from two different angles. By utilizing the differences in geometric distortion of buildings in the image pair, the 3D structure of the building is reconstructed. Compared to the existing tomographic SAR or circular SAR, this method does not require multiple flights in different elevations or observations from varying aspect angles, and greatly simplifies data acquisition. Furthermore, both simulation analysis and actual data experiment have verified the effectiveness of the proposed method.

Deep-Learning-Based Multiband Signal Fusion for 3-D SAR Superresolution

Three-dimensional (3-D) synthetic aperture radar (SAR) is widely used in many security and industrial applications requiring high-resolution imaging of concealed or occluded objects. The ability to resolve intricate 3-D targets is essential to the performance of such applications and depends directly on system bandwidth. However, because high-bandwidth systems face several prohibitive hurdles, an alternative solution is to operate multiple radars at distinct frequency bands and fuse the multiband signals. Current multiband signal fusion methods assume a simple target model and a small number of point reflectors, which is invalid for realistic security screening and industrial imaging scenarios wherein the target model effectively consists of a large number of reflectors. To the best of our knowledge, this study presents the first use of deep learning for multiband signal fusion. The proposed network, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$kR$</tex-math></inline-formula> -Net, employs a hybrid, dual-domain complex-valued convolutional neural network (CV-CNN) to fuse multiband signals and impute the missing samples in the frequency gaps between subbands. By exploiting the relationships in both the wavenumber domain and wavenumber spectral domain, the proposed framework overcomes the drawbacks of existing multiband imaging techniques for realistic scenarios at a fraction of the computation time of existing multiband fusion algorithms. Our method achieves high-resolution imaging of intricate targets previously impossible using conventional techniques and enables finer resolution capacity for concealed weapon detection and occluded object classification using multiband signaling without requiring more advanced hardware. Furthermore, a fully integrated multiband imaging system is developed using commercially available millimeter-wave (mmWave) radars for efficient multiband imaging. Using two mmWave radars, each with a bandwidth of 4 GHz operating at 60 GHz and 77 GHz, the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$kR$</tex-math></inline-formula> -Net is employed to achieve an effective bandwidth of 21 GHz by robustly estimating the full-band signal. Additionally, the generalizability of the proposed technique is demonstrated across multiband sensing scenarios in the mmWave and terahertz (THz) frequencies. Extensive numerical simulations and empirical experiments are conducted to illustrate the superiority of our approach over existing methods for a diverse set of realistic 3-D SAR imaging scenarios.

URBAN 3D RECONSTRUCTION OF VHR SAR IMAGES USING ITERATIVE OPTIMIZATION ALGORITHM AND LAYOVER FIXED-ORDER MODEL

Abstract. Synthetic Aperture Radar (SAR) Tomography (TomoSAR) is a three-dimensional SAR imaging technique that uses multiple passes to process complex SAR images and obtain three-dimensional spatial scattering information to derive the elevation scattering distribution. Due to its own shortcomings, the elevation obtained by the traditional spectral estimation method has low resolution in the elevation direction and is affected by noise. The imaging algorithm based on compressed sensing can achieve super-resolution reconstruction in the elevation direction while reducing the number of observations. However, the CS algorithm still faces challenges when applied to real-world tomographic SAR imaging. In particular, it often requires numerous iterations to achieve satisfactory results, which significantly reduces its processing efficiency in large-scale tomography. To address the above issues, in this paper, we proposed an urban 3D reconstruction of VHR SAR images using an iterative optimization algorithm and layover fixed-order model. The iterative optimization algorithm and the layover fixed-order model consist of two parts: The TomoSAR imaging equation is solved by the two-step iterative shrinkage/thresholding (TwIST) algorithm, and the number of scatterers K is estimated by the Bayesian Information Criterion (BIC). In this paper, the effectiveness of TwIST-BIC in TomoSAR imaging in urban areas is verified with real TerraSARX data. By comparing with the OMP algorithm based on matching tracking and the FISTA algorithm based on gradient descent. The TWIST-BIC method is less complex, converges faster, and combines both execution speed and super-resolution, which can effectively solve the processing efficiency problem in large-area tomography and acquire high-resolution tomographic analysis.

A SAR Image-Despeckling Method Based on HOSVD Using Tensor Patches

Coherent imaging systems, such as synthetic aperture radar (SAR), often suffer from granular speckle noise due to inherent defects, which can make interpretation challenging. Although numerous despeckling methods have been proposed in the past three decades, SAR image despeckling remains a challenging task. With the extensive use of non-local self-similarity, despeckling methods under the non-local framework have become increasingly mature. However, effectively utilizing patch similarities remains a key problem in SAR image despeckling. This paper proposes a three-dimensional (3D) SAR image despeckling method based on searching for similar patches and applying the high-order singular value decomposition (HOSVD) theory to better utilize the high-dimensional information of similar patches. Specifically, the proposed method extends two-dimensional (2D) to 3D for SAR image despeckling using tensor patches. A new, non-local similar patch-searching measure criterion is used to classify the patches, and similar patches are stacked into 3D tensors. Lastly, the iterative adaptive weighted tensor cyclic approximation is used for SAR image despeckling based on the HOSVD method. Experimental results demonstrate that the proposed method not only effectively reduces speckle noise but also preserves fine details.

3D Sparse SAR Image Reconstruction Based on Cauchy Penalty and Convex Optimization

Three-dimensional (3D) synthetic aperture radar (SAR) images can provide comprehensive 3D spatial information for environmental monitoring, high dimensional mapping and radar cross sectional (RCS) measurement. However, the SAR image obtained by the traditional matched filtering (MF) method has a high sidelobe and is easily disturbed by noise. In order to obtain high-quality 3D SAR images, sparse signal processing has been used in SAR imaging in recent years. However, the typical L1 regularization model is a biased estimation, which tends to underestimate the target intensity. Therefore, in this article, we present a 3D sparse SAR image reconstruction method combining the Cauchy penalty and improved alternating direction method of multipliers (ADMM). The Cauchy penalty is a non-convex penalty function, which can estimate the target intensity more accurately than L1. At the same time, the objective function maintains convexity via the convex non-convex (CNC) strategy. Compared with L1 regularization, the proposed method can reconstruct the image more accurately and improve the image quality. Finally, three indexes suitable for SAR images are used to evaluate the performance of the method under different conditions. Simulation and experimental results verify the effectiveness of the proposed method.

Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

An RCS Measurement Method Using Sparse Imaging Based 3-D SAR Complex Image

Three-dimensional (3-D) synthetic aperture radar (SAR) near-field imaging provides a novel radar cross section (RCS) measurement method, which has been widely concerned in the microwave field. However, the accuracy of RCS measurement based on image is easily affected by sidelobes of matching filtering (MF) imaging and background clutter. Although the existing observation-matrix-based sparse SAR imaging algorithm can suppress sidelobes and clutter, it requires huge computational cost. To solve the above problems, an RCS measurement method using sparse imaging based 3-D SAR complex image is proposed. First, 3-D sparse imaging based complex image is used to suppress sidelobe and clutter of MF image. After that, the far-field RCS of the target is obtained by the compensation factor. Compared with the observation-matrix-based sparse SAR imaging algorithm, the proposed algorithm can reduce the computation time while retain the phase information of scene distribution. Compared with the RCS measurement based on MF image, the RCS measurement performance of the proposed method is improved. The simulation and real data of the system results validate the proposed method.

Bistatic 3D SAR for wall parameter extraction in cluttered environments

Through-wall radar is an emergent technology rooted in urban surveillance, a key component being synthetic aperture radar (SAR). Accurate through-wall SAR relies on knowledge of the refractive index and thickness of any obscuring walls. Such information is rarely known beforehand and is subject to change on a sample-by-sample basis. It is therefore necessary to obtain the material properties in conjunction with any SAR measurement. In this letter, a remote data-driven asymmetric bistatic SAR approach is taken by means of matching the range to the direct back face reflection with an explicit geometry-based model. The proposed method relies on an accurate knowledge of the bistatic measurement geometry. Using the bright reflection from the front face of the wall, a method for refining an estimate of the bistatic measurement geometry is proposed. This approach is extended to three-dimensions to improve usability in heavily cluttered environments. This method is empirically validated using three-dimensional SAR measurements of both a wall-only, and a heavily cluttered scene. The method is shown to accurately extract both the refractive index and thickness of a concrete wall, with both measurements in agreement with each other and an independent validation measurement.

TPSSI-Net: Fast and Enhanced Two-Path Iterative Network for 3D SAR Sparse Imaging.

The emerging field of combining compressed sensing (CS) and three-dimensional synthetic aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate and improve image quality. However, the conventional CS-driven algorithms are always limited by huge computational costs and non-trivial tuning of parameters. In this article, to address this problem, we propose a two-path iterative framework dubbed TPSSI-Net for 3D SAR sparse imaging. By mapping the AMP into a layer-fixed deep neural network, each layer of TPSSI-Net consists of four modules in cascade corresponding to four steps of the AMP optimization. Differently, the Onsager terms in TPSSI-Net are modified to be differentiable and scaled by learnable coefficients. Rather than manually choosing a sparsifying basis, a two-path convolutional neural network (CNN) is developed and embedded in TPSSI-Net for nonlinear sparse representation in the complex-valued domain. All parameters are layer-varied and optimized by end-to-end training based on a channel-wise loss function, bounding both symmetry constraint and measurement fidelity. Finally, extensive SAR imaging experiments, including simulations and real-measured tests, demonstrate the effectiveness and high efficiency of the proposed TPSSI-Net.

Downward-Looking Linear Array Three-Dimensional SAR Imaging Based on the Two-Dimensional Mismatch Compensation

For downward-looking linear array (DLLA) three-dimensional (3-D) synthetic aperture radar (SAR), it is necessary to realize the super-resolution in both azimuth and cross-track direction due to the limited lengths of the synthetic aperture and the linear array. As all the scatterers are assumed on the uniform grids, the cross-track super-resolution can be achieved by 1-D compressed sensing. In the real imaging system, however, the gridding error should be considered because the biased scatterers lead to the mismatch of the measurement matrix and affect the imaging performance. The 1-D mismatch in cross-track direction has been solved by atomic norm minimization and off-grid sparse Bayesian inference. With the development of the super-resolution methods, the 2-D super-resolution in both azimuth and cross-track direction is realized by the 2-D compressed sensing (CS) algorithms. To solve the 2-D mismatch problem, a novel 2-D mismatch compensation method for DLLA 3-D SAR is proposed. Instead of converting the 2-D matrix signals to the 1-D vectors, the proposed method directly processes the 2-D mismatch with 2-D joint model. Furthermore, the 2-D joint model with 2-D mismatch is simplified as a normal sparse linear model, which is suitable for most of the CS reconstruction algorithms. It can not only provide better reconstruction performance but also reduce the memory cost and computation load. Finally, the simulation experiments are shown to demonstrate the validity of the proposed method.

Three-Dimensional Ground-Based SAR Imaging Algorithm Based on Keystone Formatting and Subblock

Three-dimensional (3D) ground-based synthetic aperture radar (GB-SAR) draws attention because of the ability to obtain high accuracy 3D information of the monitoring terrain. Besides, the 3D GB-SAR system is welcome due to the flexibility and large coverage in angle and range. However, existing 3D imaging algorithms for GB-SAR data focusing have limits of high computational complexity or narrow applicable scope. To realize 3D displacement monitoring with high spatial resolution and short revisit time, in this paper, a novel 3D imaging algorithm is proposed. Based on characteristics of the model of echo data from the large range and wide-view angle scenario, the proposed method uses keystone formatting to complete range migration correction and subblocks dechirping to realize horizontal focus. With the method, the reflectivity of the monitoring scenario is obtained by only one time of linear interpolation and several times of fast Fourier transforms. The main advantages of this algorithm are high imaging precision and low computational cost, and in addition, it is applicable for large illuminating coverage including the near-field and the far-field of the radar aperture. The imaging results are sampled on pseudo-spherical grid, aiming to simplify the formulation. Finally, this method is extensively validated with simulations.

Near-Field 3D SAR Imaging Using a Scanning Linear MIMO Array With Arbitrary Topologies

For near-field synthetic aperture radar (SAR) imaging, frequency domain imaging algorithms typically require the array elements to be uniformly arranged to facilitate the application of fast Fourier transform (FFT) operations; this greatly restricts the array types that can be used for practical applications. For arrays with non-uniformly positioned antennas, no general and efficient imaging algorithm is available yet. To address this issue, an effective algorithm for efficient three-dimensional (3D) SAR imaging with arbitrary linear multi-input-multi-output (MIMO) array topologies is proposed. Specifically, non-uniform FFT (NUFFT) technology is utilized to tackle the non-uniform sampling issues in both the spatial and the wavenumber domain. Despite the intuitive idea, to achieve satisfied imaging, some issues are non-trivial and should be carefully addressed. Both simulations and experiments show the superiority of the proposed algorithm on computational complexity while maintain the same high imaging precision compared with the back-projection algorithm (BPA). Moreover, we suggest that the concept of NUFFT-based imaging can also be generalized to other imaging regimes with arbitrary array configurations.

Polarimetric SAR Signatures for Characterizing Geological Units in the Canadian Arctic

This study investigates the polarimetric radar signatures of geological units in the Canadian Arctic to characterize their physical surface properties. It focuses on the Tunnunik and Haughton meteorite impact structures using RADARSAT-2 quad polarimetric synthetic aperture radar (SAR) data. The geological units show different three-dimensional (3-D) polarimetric SAR signature plots (i.e., radar backscattering responses according to the orientation and the ellipticity of a polarization state). We analyzed the quantitative relationship between the polarimetric SAR signatures of the geological units and their surface roughness. The pedestal height and the standard deviation of linear copolarization responses (SDLP) were calculated from the 3-D copolarization power signature plot, and then compared to in situ surface roughness measurements derived from LiDAR scanned high-resolution surface topography. The results show that the pedestal height has a positive correlation coefficient of ∼0.6 with surface roughness, while the SDLP has a negative correlation coefficient of ∼0.8 with surface roughness. The variation between the different polarization responses is highly dependent on the surface roughness of the geological units. The SDLP is thus suggested as a promising parameter to characterize surface roughness, in addition to the pedestal height that has been commonly used.

Sub-Aperture Partitioning Method for Three-Dimensional Wide-Angle Synthetic Aperture Radar Imaging with Non-Uniform Sampling

For a three-dimensional wide-angle synthetic aperture radar (SAR) with non-uniform sampling, it is necessary to divide its large aperture into several small sub-apertures before imaging due to the anisotropic characteristics of the target. The existing sub-aperture partitioning methods divide the aperture with equal intervals. However, for the non-uniformly sampled SAR, those equal-interval partitioning methods may have a bad effect on the resolution of the SAR imaging result. In view of this, a sub-aperture partitioning method for three-dimensional wide-angle SAR imaging with non-uniform sampling was proposed in this paper. First, we analyzed the relationship between the three-dimensional resolution and the sampling distribution in K-space based on the Cramer–Rao lower bound. Subsequently, according to the distribution of K-space sampling, the optimum size of each sub-aperture was found and the aperture was divided non-uniformly. Furthermore, the proposed method was validated by electromagnetic simulation data. The proposed sub-aperture partitioning method ensured that the resolution of each sub-aperture was high and consistent. By comparing with the equal-interval partitioning method, the experimental results showed that our proposed method had a higher resolution imaging result.

Precision Downward-Looking 3D Synthetic Aperture Radar Imaging with Sparse Linear Array and Platform Motion Parameters Estimation

The downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3D SAR) has attracted a great deal of attention, due to the ability to obtain three-dimensional (3D) images. However, if the velocity and the yaw rate of the platform are not measured with enough accuracy, the azimuth signal cannot be compressed and then the 3D image of the scene cannot be obtained. In this paper, we propose a method for platform motion parameter estimation, and downward-looking 3D SAR imaging. A DLSLA 3D SAR imaging model including yaw rate was established. We then calculated the Doppler frequency modulation, which is related to the cross-track coordinates rather than the azimuth coordinates. Thus, the cross-track signal reconstruction was realized. Furthermore, based on the minimum entropy criterion (MEC), the velocity and yaw rate of the platform were accurately estimated, and the azimuth signal compression was also realized. Moreover, a deformation correction procedure was designed to improve the quality of the image. Simulation results were given to demonstrate the validity of the proposed method.

Three‐dimensional SAR imaging of sea targets with low PRF sampling

Three-dimensional (3D) synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. A common 3D SAR focusing approach is compressed sensing (CS)-based SAR tomography, but this technique brings image quality problems because of the undesired side-lobes in the focused two-dimensional (2D) images. Moreover, the amount of raw data, which is used for 3D imaging, is still very large. To reduce the amount of raw data and achieve satisfying 3D resolving ability, a novel 3D imaging method based on multidimensional CS (MCS) is proposed for multi-baseline SAR. In this study, the multi-baseline SAR 3D raw signals are presented in space-time domain; and the proposed 3D imaging algorithm based on MCS is used to reduce the amount of raw data. The range, azimuth and elevation profiles can be reconstructed with an extremely low Pulse Recurrence Frequency (PRF) with this algorithm. Comparisons with the existing CS-tomographic focusing method are also presented. Experimental results demonstrate that the proposed algorithm can efficiently solve the 3D imaging task with limited PRF sampling.

Estimation of the total rotational velocity of a non‐cooperative target with a high cross‐range resolution three‐dimensional interferometric inverse synthetic aperture radar system

This study reports on a novel technique for the estimation of the target total rotational velocity of a non-cooperative target. The technique makes use of the second-order local polynomial Fourier transform to estimate the hitherto unknown component of the velocity vector in the radar line-of-sight direction, and three-dimensional (3D) interferometric inverse synthetic aperture radar (ISAR) imaging the target. The aspect-independent 3D size and shape of a target – an important metric for automatic target recognition – can thus be estimated. Cross-range focus in the component 2D ISAR images is also improved in the process. Results obtained from extensive simulations demonstrate the feasibility of the proposed approach.

基于贝叶斯学习的下视三维合成孔径雷达成像方法

基于贝叶斯学习的下视三维合成孔径雷达成像方法

Measurement Matrix Optimization and Mismatch Problem Compensation for DLSLA 3-D SAR Cross-Track Reconstruction.

With a short linear array configured in the cross-track direction, downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of an imaging scene. To improve the cross-track resolution, sparse recovery methods have been investigated in recent years. In the compressive sensing (CS) framework, the reconstruction performance depends on the property of measurement matrix. This paper concerns the technique to optimize the measurement matrix and deal with the mismatch problem of measurement matrix caused by the off-grid scatterers. In the model of cross-track reconstruction, the measurement matrix is mainly affected by the configuration of antenna phase centers (APC), thus, two mutual coherence based criteria are proposed to optimize the configuration of APCs. On the other hand, to compensate the mismatch problem of the measurement matrix, the sparse Bayesian inference based method is introduced into the cross-track reconstruction by jointly estimate the scatterers and the off-grid error. Experiments demonstrate the performance of the proposed APCs’ configuration schemes and the proposed cross-track reconstruction method.

Superresolution Downward-Looking Linear Array Three-Dimensional SAR Imaging Based on Two-Dimensional Compressive Sensing

For downward-looking linear array 3-D synthetic aperture radar (SAR), the azimuth and cross-track resolution are unacceptable due to the length limitation of synthetic aperture and linear array. Hence, superresolution reconstruction algorithms are desired. Since the signal to be reconstructed is sparse on the 2-D azimuth–cross-track plane, it is quite suitable to apply the compressive sensing theory to obtain the images. The existed imaging algorithms for downward-looking linear array 3-D SAR are based on 1-D compressive sensing, which could bring the couple effect between different directions. To solve this problem, a novel 3-D imaging algorithm based on 2-D compressive sensing is proposed in this paper. Instead of converting the sparse reconstruction of 2-D matrix signals to the sparse reconstruction of 1-D vectors, the proposed algorithm directly reconstructs the 2-D sparse signals on overcomplete dictionaries with separable atoms. It not only provides superresolution performance, but also reduces the storage of data acquisition and processing. Furthermore, a definition of joint sparse sampling strategy is given to reconstruct the measurement matrices for further improving the computational efficiency of the imaging algorithm. Moreover, in order to investigate the limits of the proposed algorithm, the theory analysis of Cram $\acute{e}$ r–Rao bound is derived and compared with the standard deviation. Finally, numerical simulations under the noise scenarios and the principle prototype experiment on real data are shown to demonstrate the validity and the limits of the proposed algorithm.