Wide-angle Synthetic Aperture Radar Research Articles

Sparse Signal Models for Data Augmentation in Deep Learning ATR

Automatic target recognition (ATR) algorithms are used to classify a given synthetic aperture radar (SAR) image into one of the known target classes by using the information gleaned from a set of training images that are available for each class. Recently, deep learning methods have been shown to achieve state-of-the-art classification accuracy if abundant training data are available, especially if they are sampled uniformly over the classes and in their poses. In this paper, we consider the ATR problem when a limited set of training images are available. We propose a data-augmentation approach to incorporate SAR domain knowledge and improve the generalization power of a data-intensive learning algorithm, such as a convolutional neural network (CNN). The proposed data-augmentation method employs a physics-inspired limited-persistence sparse modeling approach, which capitalizes on the commonly observed characteristics of wide-angle synthetic aperture radar (SAR) imagery. Specifically, we fit over-parametrized models of scattering to limited training data, and use the estimated models to synthesize new images at poses and sub-pixel translations that are not available in the given data in order to augment the limited training data. We exploit the sparsity of the scattering centers in the spatial domain and the smoothly varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of the over-parametrized model fitting. The experimental results show that, for the training on the data-starved regions, the proposed method provides significant gains in the resulting ATR algorithm’s generalization performance.

Wide angle SAR imaging method based on hybrid representation

AbstractIn this paper, the application of a hybrid representation in wide‐angle synthetic aperture radar (WASAR) imaging is investigated, addressing the challenge of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. A convolutional neural network is utilized to represent the two‐dimensional data within each azimuth aperture, while employing blind compressed sensing (BCS) to achieve sparse representation across different azimuth apertures. Convolutional neural network (CNN) excels in learning spatial hierarchies and local dependencies of two‐dimensional data but requires a large amount of training data. Isotropic targets within each azimuth aperture can be used for training conventional SAR data, while acquiring training samples for anisotropic targets poses challenges. To address this issue, BCS are integrated into WASAR imaging, eliminating the need for additional training data. By integrating these methods, a novel approach called hybrid‐WASAR is proposed, which incorporates two regularization terms into WASAR imaging and employs the alternating direction method of multipliers to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, hybrid‐WASAR improves the accuracy of reconstructing the backscattering coefficients of the targets, leading to a significant enhancement in overall imaging quality.

Superresolution Composite SAR Imaging Method by Hierarchical Pattern With Attributed Scattering Priors

Compared with the ideal point scattering model, the attributed scattering center (ASC) model provides concise and physically relevant description of the man-made targets, such as the vehicles and ships, giving an efficient way to interpret the measurements of high-frequency and wide-angle synthetic aperture radar (SAR). Since the ASC model is complicated, it gives rise to a high-dimensional model parameter estimation problem, leading to a heavy computational burden. Aimed at this problem, in this article, a novel superresolution composite SAR imaging algorithm is proposed based on sparse representation, in which the ASC attributes are estimated by a hierarchical pattern for reducing the computational cost. In the proposed algorithm, the ASC model parameters, classified into three categories, are estimated coarsely via minimum variance pursuit (MVP). Then these coarse estimates are refined jointly using the consensus alternating direction method of multipliers (CADMM) optimization for polarimetric measurements. The spectrum extrapolation and replacement are performed for superresolution and reducing the impact of model mismatch, followed by a fast Fourier transform (FFT)-based operation to efficiently obtain the final superresolution image. The proposed approach not only possesses the ability of superresolution reconstruction with the target nonideal scattering features’ enhancement but also offers the physically relevant attributes on the premise of estimation accuracy improvement and calculation capacity reduction. Extensive experiments are conducted to corroborate the effectiveness of the proposed algorithm.

Geometric calibration of helicopter-borne wide-angle SAR imagery based on the range Doppler model

ABSTRACT Helicopter-borne wide-angle synthetic aperture radar (SAR) can be used in a wide range of military and civil applications because of their high resolution and mobility. The absolute geometric positioning accuracy is an important index for SAR image quality assessment. However, the effectiveness of using traditional geometric calibration methods with helicopter-borne SAR images obtained by time-domain imaging algorithms remains to be verified. Based on the range-Doppler model, a high-precision geometric calibration method for helicopter-borne SAR is proposed in this study and verified by experiments using real data with different resolutions. The results show that after calibration, the positioning accuracy of SAR images with 20 m and 1 m resolutions is improved from 51.33 m to 4.71 m and from 62.49 m to 3.03 m, respectively. These results verify the effectiveness of the proposed method.

A Bayesian Method for ViSAR Image Fusion Using Effective Reflection Coefficient

Since the isotropic scattering assumption does not hold in a wide-angle synthetic aperture radar, the video synthetic aperture radar (ViSAR), a new sensing mode by sequentially forming SAR images on a series of contiguous or overlapping sub-apertures, has the promising capability to capture aspect-dependent scattering behavior of objects. In this paper, a new Bayesian method for fusing ViSAR images is proposed. First, the notion of effective reflection coefficient is defined to characterize aspect-dependent scattering behavior of objects. Based on the ViSAR images sequence, the spatial effective reflection coefficient in the area of interest (AOI) when observing at different aspect angles are estimated. A Bayesian hypothesis testing is then derived to fuse the obtained effective reflection coefficient estimates for detecting scatterers in the AOI. The performance of the proposed method is evaluated and compared with that of the conventional GLRT-based ViSAR image fusion method in a simulated scenario. Numerical results demonstrate the superiority of the proposed method in capturing the aspect-dependent scattering characteristics as well as the spatial structure of objects.

Adaptive Subaperture Integration for Wide-Angle Synthetic Aperture Radar

In this article, we present an adaptive subaperture integration method for wide-angle synthetic aperture radar (SAR) for improved imaging, with emphasis on short-to-medium range applications. In order to avoid full-aperture integration, traditional approaches use fixed-width subapertures, which may not conform to the persistence angle of the scatterers. Coherent integration gains over the aperture are possible if integration is carried out over the persistence angle of the scatterers, because integrating shorter than the persistence angle may spread the scattering response across multiple subapertures or, conversely, integrating more than the persistence angle may cause noise accumulation along with the useful signal. In this article, we propose to use change-point detection methods to estimate the persistence widths of the scatterers, and consequently enhance the coherent integration gains, resulting in improved imaging. We compare our proposed methods with the standard integration approaches as well as a recently proposed adaptive integration approach. We provide qualitative and quantitative analyses to prove that our proposed methods outperform the existing approaches. We present experimental results on the real-data of our low-terahertz radar as well as a publicly available dataset to validate our claims.

A Modified Factorized Geometrical Autofocus Method for Wide Angle SAR

Wide-Angle (WA) synthetic aperture radar (SAR) has shown remarkable performance in high-resolution mapping, and its mostly used imaging algorithm, fast factorized back projection (FFBP), is efficient, robust, and of low computational complexity. However, trajectory deviations and the system calibration error, introduced by low measurement accuracy, dramatically degrade FFBP's performance. This article proposes the modified factorized geometrical autofocus (MFGA) method for WA-SAR to address the above problems in FFBP. MFGA implements the phase gradient algorithm on defocus subimages at first. Then, a factorized geometrical error hypothesis between subimages is proposed. And the corresponding defocus factors are classified as three independent parts: image distortion, spectrum migration, and phase error. To deal with those problems, MFGA introduces image registration techniques and a maximum sharpness method to calibrate image distortion and phase error. Moreover, in MFGA, based on minimum entropy and least square method, a Doppler spectrum migration correction algorithm is proposed to correct spectrum migration. In the FFBP chain, MFGA is used on subimages refocus and fusion until obtaining a full-resolution image. In our experiments, we compared MFGA and other time-domain autofocus algorithms using simulated data and real data obtained by helicopter and airship with false trajectory and system calibration parameters. The results show that MFGA performs better in terms of the peak to side-lobe ratio, the azimuth resolution, the refocused images' entropy, and processing time consumption. The better performance demonstrates MFGA's advantages in addressing trajectory deviations and the system calibration error for WA-SAR.

A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Target recognition is an important area in Synthetic Aperture Radar (SAR) research. Wide-angle Synthetic Aperture Radar (WSAR) has obvious advantages in target imaging resolution. This paper presents a vehicle target recognition algorithm for wide-angle SAR, which is based on joint feature set matching (JFSM). In this algorithm, firstly, the modulus stretch step is added in the imaging process of wide-angle SAR to obtain the thinned image of vehicle contour. Secondly, the gravitational-based speckle reduction algorithm is used to obtain a clearer contour image. Thirdly, the image is rotated to obtain a standard orientation image. Subsequently, the image and projection feature sets are extracted. Finally, the JFSM algorithm, which combines the image and projection sets, is used to identify the vehicle model. Experiments show that the recognition accuracy of the proposed algorithm is up to 85%. The proposed algorithm is demonstrated on the Gotcha WSAR dataset.

High‐resolution SAR altimeter with wide‐angle echoes coherent accumulation

Wide-angle synthetic aperture radar (SAR) altimeters, which are able to obtain higher azimuth resolution and better height measurement result, demonstrate a promising prospect of application. Accumulating scattering echoes received from different angles can greatly suppress noise and promote estimation accuracy. However, traditional methods of accumulation cannot work well in wide-angle SAR altimeters due to variety of scattering characteristics. To address this problem, this study first researches on basic principles, scattering function and signal models of wide-angle SAR altimeters. Based on these models, echoes of various angles are coherent accumulated to estimate the parameters. A cylindrical target on the sea surface is used as an example in the simulation. The results indicate that the precision of parameter estimation is improved by two to four orders of magnitude if the scattering characteristics varying with azimuth angles are taken into account in the step of echoes accumulation.

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.

Accurate Wide Angle SAR Imaging Based on LS-CS-Residual.

Wide angle synthetic aperture radar (WASAR) receives data from a large angle, which causes the problem of aspect dependent scattering. regularization is a common compressed sensing (CS) model. The regularization based WASAR imaging method divides the whole aperture into subapertures and reconstructs the subaperture images individually. However, the aspect dependent scattering recovery of it is not accurate. The subaperture images of WASAR can be regarded as the SAR video. The support set among the different frames of SAR video are highly overlapped. Least squares on compressed sensing residuals (LS-CS-Residuals) can reconstruct the time sequences of sparse signals which change slowly with time. This is to replace CS on the observation by CS on the least squares (LS) residual computed using the prior estimate of the support. In this paper, we introduce LS-CS-Residual into WASAR imaging. In the iteration of LS-CS-Residual, the azimuth-range decoupled operators are used to avoid the huge memory cost. Real data processing results show that LS-CS-Residual can estimate the aspect dependent scatterings of the targets more accurately than CS based methods.

Wide Angle SAR Subaperture Imaging Based on Modified Compressive Sensing

In wide angle synthetic aperture radar (WASAR), the scattering behavior varies with aspect angle. Subaperture methods can be adopted to accommodate the anisotropic behavior. Modified compressive sensing (MCS) focuses on reconstructing a sparse signal from a limited number of its linear projections when a part of its support is known. It can achieve the accurate recovery with less samples compared with the traditional compressive sensing (CS). Since the support sets of different subaperture images are highly overlapped across the whole aperture, MCS can be adopted in WASAR subaperture imaging. Iterative hard thresholding with partial known support (IHT-PKS) is adopted to recover the MCS model. Azimuth-range decouple operators based on backprojection are implemented in IHT-PKS to avoid huge memory cost of the measurement matrix. Compared with the traditional CS-based WASAR subaperture imaging method, the proposed method can reconstruct the aspect dependent scattering more accurately and is more robust to noise. The proposed method can also reconstruct the scene with less undesirable artifacts at downsampling rate. The simulation and experiment results validate the proposed method.

Analysis of Azimuthal Variations Using Multi-Aperture Polarimetric Entropy with Circular SAR Images

In conventional synthetic aperture radar (SAR), sensors with a fixed look angle are assumed, and the scattering properties are considered as invariant in the azimuth. In some new SAR modes such as wide-angle SAR and circular SAR (CSAR), the azimuthal angle of view is much larger. Anisotropic targets which have different physical shapes from different angles of view are difficult to interpret in the traditional observation model if variations remain unconsidered. Meanwhile, SAR polarimetry is a powerful tool to analyze and interpret targets’ scattering properties. Anisotropic targets can be precisely described with polarimetric signatures from different angles of view. In this paper, polarimetric data is separated into sub-apertures to provide polarimetric properties from different angles of view. A multi-aperture observation model which contains full polarimetric information from all angles of view is then established. Based on the multi-aperture observation model, multi-aperture polarimetric entropy (MAPE) is defined and is suggested as an extension of polarimetric entropy in multi-aperture situations. MAPE describes both targets’ polarimetric properties and variations across sub-apertures. Variations across the azimuth are analyzed and anisotropic and isotropic targets are identified by MAPE. MAPE can be used in many polarimetric wide angle and CSAR applications. Potential applications in target discrimination and classification are discussed. The effectiveness and advantages of MAPE are demonstrated with polarimetric CSAR data acquired from the Institute of Electronics, Chinese Academy of Sciences airborne CSAR system at P-band.

Automatic target recognition in missing data cases

Automatic target recognition (ATR) is the process where computer algorithms are used to detect and classify objects or regions of interest in sensor data [1]. ATR algorithms have been developed for a broad range of sensors, including electro-optic (EO), infrared (IR), and microwave sensors (radar). A key advantage of microwave radar is its utility for all weather and time of day compared with other types of sensors. One limitation of many radar systems' ATR performance is the poor spatial resolution in the range and cross-range dimensions. While the range resolution of the microwave radar system can be straightforwardly improved by increasing the system's radio frequency bandwidth, superior cross-range (or azimuth) resolution requires an increase in antenna size. In order to achieve resolution similar to EO or IR systems, an enormous antenna must be used for transmission and reception, hindering microwave radar system's general applicability to ATR problems. However, by operating a synthetic aperture radar (SAR), the large antenna requirement can be overcome, and very fine cross-range resolution can be obtained with a modest real aperture antenna. Indeed, SAR has been used to generate high-resolution 2-D or 3-D object images at a variety of different microwave frequencies. In particular, wideangle SAR images can contain important features of an object from a diverse set of observation angles collected by a radar system orbiting around a target or scene. These feature-rich wide-angle SAR images are useful for ATR due to their high resolution and comprehensive coverage of the target. Interference, jamming, or data dropouts, however, are commonplace in practical SAR environments, and result in an incomplete data set. These missing and corrupted data cause substantial degradation in the generated SAR imagery, hampering their utility for subsequent ATR processing especially when data-independent image formation algorithms are used. A prime example of these difficulties is operating a SAR system in the very high frequency (VHF) or ultrahigh frequency (UHF) bands, where the spectrum is frequently crowded [2].

A Video-SAR Imaging Technique for Aspect-Dependent Scattering in Wide Angle

In a wide-angle synthetic aperture radar (SAR), as the isotropic scattering assumption becomes invalid, scattering characteristics of targets tend to be aspect-dependent. In this paper, based on the high center frequency characteristic of Video SAR (ViSAR), a ViSAR aspect-dependent scattering imaging algorithm (VADSIA) is proposed, which includes two main procedures of persistent polar format algorithm (PPFA) and generalized likelihood ratio test (GLRT). Through PPFA, sequential ViSAR sub-aperture images (sub-images) can be formed to the same orientation with the same image resolution efficiently, providing the advantage of easy extraction and display of the aspect-dependent scattering characteristic. Different from conventional algorithms that require 2-D aspect-dependent interpolation procedures, PPFA utilizes a uniform lookup table scheme and a 2-D Chirp-Z transform (2D-CZT) to realize persistent sub-aperture imaging. As PPFA includes only 2-D fast Fourier transforms and multiplications, it is suitable for parallel implementation to realize real-time imaging. Besides, with the persistent imaging ability of PPFA, an efficient approximate GLRT process can be applied on the formed sub-images directly, which can preserve both the aspect-dependent scattering characteristics and the azimuth resolution of targets at the same time. Combining PPFA with GLRT, VADSIA is an effective and efficient way for the aspect-dependent imaging. The ground-based experimental results validate the effectiveness of the proposed algorithm.

Target decomposition and recognition from wide-angle SAR imaging based on a Gaussian amplitude-phase model

Wide-angle synthetic aperture radar (W-SAR) imaging accounts for multi-azimuthal scattering and is feasible for retrieving more comprehensive features of complex targets. Because a typical target is seen as composed of its components (typically, some simple geometric objects), a Gaussian amplitude-phase (GAP) model has been developed for the analysis of multi-azimuthal scattering from these objects. Based on the time-frequency analysis of wide-angle scattering, the parameters of the GAP model were estimated, including the Gaussian variance, the surface curvature, and the number of objects in all imaged pixels. Numerical simulations and real measurements demonstrate the capability of the GAP model for decomposing and recognizing complex electric-large targets.

Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing.

The multichannel or wide-angle imaging performance of synthetic aperture radar (SAR) can be improved by applying the compressed sensing (CS) theory to each channel or sub-aperture image formation independently. However, this not only neglects the complementary information between signals of each channel or sub-aperture, but also may lead to failure in guaranteeing the consistency of the position of a scatterer in different channel or sub-aperture images which will make the extraction of some scattering information become difficult. By exploiting the joint sparsity of the signal ensemble, this paper proposes a novel CS-based method for joint sparse recovery of all channel or sub-aperture images. Solving the joint sparse recovery problem with a modified orthogonal matching pursuit algorithm, the recovery precision of scatterers is effectively improved and the scattering information is also preserved during the image formation process. Finally, the simulation and real data is used for verifying the effectiveness of the proposed method. Compared with single channel or sub-aperture independent CS processing, the proposed method can not only obtain better imaging performance with fewer measurements, but also preserve more valuable scattering information for target recognition.

A novel adaptive wide-angle SAR imaging algorithm based on Boltzmann machine model

Scattering dependency often exists in both the spatial location and the viewing angle. Based on the assumption of isotropic point scattering model, however, conventional narrow-angle synthetic aperture radar (SAR) imaging algorithms have been no longer suitable to the scattering dependency model. To improve azimuth resolution and capture richer observation information, sparsity-driven (SD) wide-angle SAR (WSAR) imaging algorithms have been developed. Actually, existing SD-based WSAR imaging algorithms are sensitive to the regularization parameters which are required to adjust manually. These methods indeed limit their practical applications. To solve this problem, in this paper, we propose an adaptive WSAR imaging algorithm based on the Boltzmann machine (BM) model. In particular, we model the spatial sparsity and high azimuth correlation of scattering energy by virtual of a special BM prior. Then, the support of sparse representation and imaging parameters including BM parameters, noise variance and the variance of each sparse representation element are jointly estimated by a block-coordinate descent process. Finally, the proposed WSAR imaging algorithm is performed adaptively via sparse representation. Experiments are conducted by synthetic scene and simple tank dataset of high-frequency electromagnetic scattering calculation software. Extensive empirical results demonstrate that the proposed algorithm can achieve better imaging performance than the conventional algorithms in terms of relative mean squared error and support identification error.

Wide-Angle Synthetic Aperture Radar Imaging: Models and algorithms for anisotropic scattering

Advances in radar hardware have enabled the sensing of ever-wider synthesized apertures. In this article, radar video?a sequence of radar images indexed on subaperture?is discussed as a natural, convenient, and revealing representation to capture wide-angle scattering behavior of complex objects. We review the inverse problem of recovering wide-angle scene reflectivity from synthetic aperture radar (SAR) measurements, survey signal processing approaches for its solution, and introduce a novel Bayesian estimation method. Examples from measured and simulated scattering data are presented to illustrate scattering behavior conveniently revealed by the SAR video framework.

Wide-Angle High Resolution SAR Imaging and Robust Automatic Target Recognition of Civilian Vehicles

This paper focuses on wide-angle synthetic aperture radar (SAR) imaging and automatic target recognition of civilian vehicles. A recently proposed hybrid data adaptive method is applied to generate accurate and sparse SAR images of civilian vehicles. We combine projection slice theorem (PST) with 2-D FFT to obtain a more accurate pose estimation than the established PST. Given the so-obtained pose estimates, the horizontal and vertical cumulative-sum-vector (CSV) profiles are utilized to focus the SAR image only on the vehicle of current interest. The corresponding vertical CSV is used as a simple feature for automatic target recognition (ATR). We adopt the local learning based feature selection for ATR. The effectiveness of the entire chain of imaging, pose estimation, feature extraction, and ATR methods is verified using the experimentation results based on the publicly available GOTCHA SAR data set. We demonstrate that the high resolution SAR imaging results in much improved ATR performance compared to the conventional SAR imaging.