Sparse Aperture Data Research Articles

Enhancing ISAR Image Efficiently via Convolutional Reweighted l1 Minimization.

Inverse synthetic aperture radar (ISAR) imaging for the sparse aperture data is affected by considerable artifacts, because under-sampling of data produces high-level grating and side lobes. Noting the ISAR image generally exhibits strong sparsity, it is often obtained by sparse signal recovery (SSR) in case of sparse aperture. The image obtained by SSR, however, is often dominated by strong isolated scatterers, resulting in difficulty to recognize the structure of target. This paper proposes a novel approach to enhance the ISAR image obtained from the sparse aperture data. Although the scatterers of target are isolated in the ISAR image, they should be associated with the neighborhood to reflect some intrinsic structural information of the target. A convolutional reweighted l1 minimization model, therefore, is proposed to model the structural sparsity of ISAR image. Specifically, the ISAR image is reconstructed by solving a sequence of reweighted l1 problems, where the weight of each pixel used for the next iteration is calculated from the convolution of its neighbor values in the current solution. The problem is solved by the alternating direction of multipliers (ADMM) and linearized approximation, respectively, to improve the computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm is effective to enhance the ISAR image, robust to noise, and more impressively, very efficient to implement.

Micro-Doppler Effects Removed Sparse Aperture ISAR Imaging via Low-Rank and Double Sparsity Constrained ADMM and Linearized ADMM.

Inverse synthetic aperture radar (ISAR) imaging for the target with micro-motion parts is influenced by the micro-Doppler (m-D) effects. In this case, the radar echo is generally decomposed into the components from the main body and micro-motion parts of target, respectively, to remove the m-D effects and derive a focused ISAR image of the main body. For the sparse aperture data, however, the radar echo is intentionally or occasionally under-sampled, which defocuses the ISAR image by introducing considerable interference, and deteriorates the performance of signal decomposition for the removal of m-D effects. To address this issue, this paper proposes a novel m-D effects removed sparse aperture ISAR (SA-ISAR) imaging algorithm. Note that during a short interval of ISAR imaging, the range profiles of the main body of target from different pulses are similar, resulting in a low-rank matrix of range profile sequence of main body. For the range profiles of the micro-motion parts, they either spread in different range cells or glint in a single range cell, which results in a sparse matrix of range profile sequence. From this perspective, the low-rank and sparse properties are utilized to decompose the range profiles of the main body and micro-motion parts, respectively. Moreover, the sparsity of ISAR image is also utilized as a constraint to eliminate the interference caused by sparse aperture. Hence, SA-ISAR imaging with the removal of m-D effects is modeled as a triply constrained underdetermined optimization problem. The alternating direction method of multipliers (ADMM) and linearized ADMM (L-ADMM) are further utilized to solve the problem with high efficiency. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithm.

ISAR cross-range scaling based on the MUSIC technique

Cross-range scaling plays an important role in the inverse synthetic aperture radar (ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation (FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity (RV) estimation of the slow rotation target. In this paper, we propose an accurate cross-range scaling algorithm based on the multiple signal classification (MUSIC) method. We first select some range bins with the mono-component linear frequency modulated (LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.

Sparse aperture bistatic ISAR imaging under low signal-to-noise ratio condition

A sparse representation-based bistatic inverse synthetic aperture radar (ISAR) imaging method can achieve a high-resolution image of a target with sparse aperture data. However, the bistatic ISAR system is more sensitive to noise than the monostatic one because of its nonmirror reflection geometry. To overcome this drawback, we propose the sparse aperture bistatic ISAR imaging method based on joint sparse model. Considering the joint sparse information of bistatic ISAR echo, a joint sparse imaging model is constructed. Then, the dechirped sparse aperture bistatic ISAR echo after translational compensation is transformed into range fast time and azimuth slow time domains by the joint sparse imaging model, and a corresponding azimuth sparse basis is constructed. Then a joint sparse complex approximate message passing algorithm is proposed to joint sparse imaging model. The joint sparse imaging problem is converted to a block sparse imaging problem by vectorization. Using the relationship between the vectorization of the matrix and the Kronecker product, a matrix iteration structure is proposed to solve the joint sparse model efficiently and accurately. The experimental results based on both scattering point model and electromagnetic calculation model data verify the effectiveness of the proposed imaging method.

Iterative Optimization-Based ISAR Imaging With Sparse Aperture and Its Application in Interferometric ISAR Imaging

Recently, high-resolution inverse synthetic aperture radar (ISAR) imaging with sparse aperture (SA) data has attracted increasing attention. The theory of compressive sensing (CS) suggests that an unknown sparse signal can be accurately recovered by taking advantage of very limited samples. For ISAR images, the number of the resolution cells occupied by the strong scattering points of the target is usually much smaller than that of the resolution cells of the image plane, revealing the strong sparsity trait of the ISAR signal. This trait of ISAR signal creates the conditions for incorporating CS into high-resolution ISAR imaging. In this paper, a novel iterative optimization-based SA-ISAR imaging approach is proposed. First, the SA-ISAR signal model is established and the envelope alignment is executed on the one-dimensional range profiles of the SA data. Next, the gradient-based algorithm is exploited to recover the complete signals. Then, by iteratively performing the procedures of the envelope alignment and the signal recovery, the accuracy of signal recovery can be significantly improved and a high-quality ISAR image can be obtained. Ultimately, the extension of the iterative optimization-based SA-ISAR imaging to the three-dimensional (3-D) interferometric ISAR (InISAR) imaging is successfully implemented via the traditional ISAR imagery pair interferometric method. The experiments based on the measured and simulated data are carried out to validate the superiority of the novel algorithm.

A Super-Resolution Sparse Aperture ISAR Sensors Imaging Algorithm via the MUSIC Technique

The conventional range-Doppler (RD) technique uses fast Fourier transformation (FFT) to generate focused images. However, the spectrum of FFT has high sidelobes and wide main lobes and the resolution of RD is limited by the radar parameters so that the RD method cannot generate super-resolution images especially with sparse aperture (SA) data. A super-resolution SA inverse synthetic aperture radar (SA-ISAR) imaging algorithm via the multiple signal classification (MUSIC) technique is proposed in this paper. The proposal uses the MUSIC algorithm to estimate the location and employs the least-squares technique to calculate the intensity of each scatterer. Then, the scatterers can be precisely depicted in the images without the interference of sidelobes and wide main lobes. The resolution of the proposal depends less on the radar parameters, and it can be further improved by using a smaller search step. Experiments obtained by processing of simulated and raw data demonstrate that the proposal can efficiently generate super-resolution images with full aperture (FA) or SA data.

High-Resolution Bistatic ISAR Imaging of a Space Target with Sparse Aperture

Due to the large size of space targets, migration through resolution cells (MTRC) are induced by a rotational motion in high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) systems. The inaccurate correction of MTRC degrades the quality of Bi-ISAR images. However, it is challenging to correct the MTRC where sparse aperture data exists for Bi-ISAR systems. A joint approach of MTRC correction and sparse high-resolution imaging for Bi-ISAR systems is presented in this paper. First, a Bi-ISAR imaging sparse model-related to MTRC is established based on compress sensing (CS). Second, the target image elements and noise are modeled as the complex Laplace prior, and the Gaussian prior, respectively. Finally, the high-resolution, well-focused image is obtained by the full Bayesian inference method, without manual adjustments of unknown parameters. Simulated results verify the effectiveness and robustness of the proposed algorithm.

Bistatic ISAR Imaging and Scaling of Highly Maneuvering Target With Complex Motion via Compressive Sensing

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

Radar pulse completion and high‐resolution imaging with SAs based on reweighted ANM

In actual condition, array elements deficiency or transmission errors lead to incomplete data, which is called sparse aperture (SA) data. In inverse synthetic aperture radar (ISAR) imaging, this large-gaped data produces poor-quality ISAR images when using traditional range–Doppler algorithm. Recently, imaging algorithms based on compressed sensing (CS) theory alleviate this problem effectively because CS theory indicates that sparse signal can be reconstructed from incomplete measurements. However, the basis mismatch problem in CS-based algorithms may degrade the ISAR image. In this study, a reweighted atomic-norm minimisation (ANM) (RAM)-based imaging method is proposed. RAM is a gridless sparse method, which can enhance sparsity and resolution. RAM formulates an optimisation problem and iteratively carries out ANM with a sound reweighting strategy. By reformulating the RAM as a semi-definite programme, the echoes with full aperture (FA) are reconstructed from SA data. After that, ISAR imaging with the reconstructed FA data is achieved via the conventional azimuth compression method. Simulated and real data results demonstrate the effectiveness and superiority of the proposed method.

Fast ISAR Cross-Range Scaling Using Modified Newton Method

This paper proposes a fast and novel cross-range scaling algorithm for inverse synthetic aperture radar (ISAR) imaging. The rotational motion of the target unavoidably results in high-order phase errors that blur the ISAR image. To achieve the cross-range scaling and compensate the quadratic phase error, the rotational velocity and rotational center of the target are jointly estimated by optimizing the ISAR image quality in terms of either entropy or contrast. Since it is a two-dimensional nonlinear optimization problem, the grid search is generally computationally inefficient and inaccurate. To improve the computational efficiency, a modified Newton method is introduced by adjusting the Hessian to be positively definite to ensure the iterative optimization process in a correct direction. The proposed algorithm offers the following desirable advantageous features. First, it automatically compensates the quadratic phase errors jointly with the scaling process to improve the image quality. Second, it is a data-driven, rather than image-driven, process that does not depend on the quality of ISAR image. It also performs satisfactorily for the sparse aperture data, while most other algorithms are invalid. The modified Newton method ensures fast convergence. For example, our numerical experiments achieve a precision of $ \bf {10^{-6}}$ with less than ten iterations. Last but not least, the proposed algorithm is robust to noise because our experiments show that it is still effective when signal-to-noise ratio is as low as $-$ 10 dB.

ISAR imaging and cross‐range scaling of high‐speed manoeuvring target with complex motion via compressive sensing

For targets with extreme manoeuvres, inverse synthetic aperture radar (ISAR) imaging suffers from translational motion (TM), which is modelled as a one-dimensional (1D) phase error, and non-uniform rotational motion (RM), which is a multidimensional (MD) phase error that causes severe blurring in ISAR images. Full-aperture data collection is often unachievable because of interference with other radar activities, resulting in sparse-aperture (SA) data. In this study, the authors present a new framework for SA-ISAR imaging and cross-range scaling for manoeuvring targets based on compressive sensing. Instead of solving conventional optimisation problems constrained by a sparsity of signals, the proposed method utilises the sensing-matrix estimation technique for ISAR image reconstruction using parametric signal-model reconstruction. To do this, it looks for basis functions that best represent the behaviour of a sensing-dictionary matrix comprising the observed SA data. The sensing-matrix reconstruction is based on a modified orthogonal matching pursuit-type basis function-searching scheme. Finally, they generate a well-focused and scaled ISAR image from the recovered complete ISAR signal using the conventional Fourier transform after the removal of signals corresponding to 1D TM and MD RM phase errors. They utilise both simulated and real measured datasets to confirm the effectiveness of proposed method.

Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning.

Interferometric inverse synthetic aperture radar (InISAR) imaging for sparse-aperture (SA) data is still a challenge, because the similarity and matched degree between ISAR images from different channels are destroyed by the SA data. To deal with this problem, this paper proposes a novel SA–InISAR imaging method, which jointly reconstructs 2-dimensional (2-D) ISAR images from different channels through multiple response sparse Bayesian learning (M-SBL), a modification of sparse Bayesian learning (SBL), to achieve sparse recovery for multiple measurement vectors (MMV). We note that M-SBL suffers a heavy computational burden because it involves large matrix inversion. A computationally efficient M-SBL is proposed, which, proceeding in a sequential manner to avoid the time-consuming large matrix inversion, is denoted as sequential multiple sparse Bayesian learning (SM-SBL). Thereafter, SM-SBL is introduced to InISAR imaging to simultaneously reconstruct the ISAR images from different channels. Numerous experimental results validate that the proposed SM-SBL-based InISAR imaging algorithm performs superiorly against the traditional single-channel sparse-signal recovery (SSR)-based InISAR imaging methods in terms of noise suppression, outlier reduction and 3-dimensional (3-D) geometry estimation.

High-Resolution Inverse Synthetic Aperture Radar Imaging and Scaling With Sparse Aperture

In high-resolution radar imaging, the rotational motion of targets generally produces migration through resolution cells (MTRC) in inverse synthetic aperture radar (ISAR) images. Usually, it is a challenge to realize accurate MTRC correction on sparse aperture (SA) data, which tends to degrade the performance of translational motion compensation and SA-imaging. In this paper, we present a novel algorithm for high-resolution ISAR imaging and scaling from SA data, which effectively incorporates the translational motion phase error and MTRC corrections. In this algorithm, the ISAR image formation is converted into a sparsity-driven optimization via maximum a posterior (MAP) estimation, where the statistics of an ISAR image is modeled as complex Laplace distribution to provide a sparse prior. The translational motion phase error compensation and cross-range MTRC correction are modeled as joint range-invariant and range-variant phase error corrections in the range-compressed phase history domain. Our proposed imaging approach is performed by a two-step process: 1) the range-invariant and range-variant phase error estimations using a metric of minimum entropy are employed and solved by using a coordinate descent method to realize a coarse phase error correction. Meanwhile, the rotational motion can be obtained from the estimation of range-variant phase errors, which is used for ISAR scaling in the cross-range dimension; 2) under a two-dimensional (2-D) Fourier-based dictionary by involving the slant-range MTRC, joint MTRC-corrected ISAR imaging and accurate phase adjustment are realized by solving the sparsity-driven optimization with SA data, where the residual phase errors are treated as model error and removed to achieve a fine correction. Finally, some experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Sparse aperture inverse synthetic aperture radar imaging of manoeuvring targets with compensation of migration through range cells

In inverse synthetic aperture radar (ISAR) imaging of a target with significant manoeuvres, severe migration through range cells (MTRCs) and time-varying Doppler usually involve in the echoed signal. Both of them may challenge the conventional motion compensation and imaging methods, which are usually based on the assumption of small rotational angle with short coherent time duration. Moreover, for a multi-functional ISAR, full aperture (FA) data collection might be unachievable because of the conflict with other radar activities, resulting in sparse aperture (SA) data. In this study, an ISAR imaging method of manoeuvring targets with SA data is proposed, where MTRC compensation and time-varying Doppler are well accounted. By using a sparse chirp-Fourier basis with first-order range–azimuth coupling term, an improved orthogonal match pursuit algorithm is implemented to solve the sparsity-driven optimisation for FA data reconstruction from SA-ISAR measurements. As a primary item, MTRC compensation is seamlessly embedded into the FA signal reconstruction for ISAR imaging of manoeuvring targets. Both simulated and real data are utilised to confirm the effectiveness of the proposed method.

An algorithm for inverse synthetic aperture imaging lidar based on sparse signal representation

In actual applications of inverse synthetic aperture imaging lidar, the issue of sparse aperture data arises when continuous measurements are impossible or the collected data during some periods are not valid. Hence, the imaging results obtained by traditional methods are limited by high sidelobes. Considering the sparse structure of actual target space in high frequency radar application, a novel imaging method based on sparse signal representation is proposed in this paper. Firstly, the range image is acquired by traditional pulse compression of the optical heterodyne process. Then, the redundant dictionary is constructed through the sparse azimuth sampling positions and the signal form after the range compression. Finally, the imaging results are obtained by solving an ill-posed problem based on sparse regularization. Simulation results confirm the effectiveness of the proposed method.

Phase adjustment and isar imaging of maneuvering targets with sparse apertures

A multifunctional radar system can only acquire limited and discontinuous wideband pulses, which form sparse aperture (SA) observations of a target. To carry out radar activities (detection, tracking, and imaging) simultaneously for multiple targets, inverse synthetic aperture radar (ISAR) imaging exploiting these SA data is essential for multifunctional radar. In this paper, we study the phase adjustment and full-aperture (FA) reconstruction for SA-ISAR imaging of maneuvering targets. A modified eigenvector-based autofocus approach is proposed to correct phase errors within SA measurements of maneuvering targets. After phase correction, the FA data are reconstructed from SA measurements via sparse representation under a redundant chirp-Fourier dictionary. An efficient algorithm is developed to solve the sparse decomposition optimization, and ISAR images of the maneuvering target are obtained by adaptive joint time-frequency imaging approaches with the reconstructed data. Both simulated and real data sets are used to confirm the effectiveness of the proposed methods.

Single Frequency Inverse Obstacle Scattering: A Sparsity Constrained Linear Sampling Method Approach

The linear sampling method (LSM) offers a qualitative image reconstruction approach, which is known as a viable alternative for obstacle support identification to the well-studied filtered backprojection (FBP), which depends on a linearized forward scattering model. Of practical interest is the imaging of obstacles from sparse aperture far-field data under a fixed single frequency mode of operation. Under this scenario, the Tikhonov regularization typically applied to LSM produces poor images that fail to capture the obstacle boundary. In this paper, we employ an alternative regularization strategy based on constraining the sparsity of the solution's spatial gradient. Two regularization approaches based on the spatial gradient are developed. A numerical comparison to the FBP demonstrates that the new method's ability to account for aspect-dependent scattering permits more accurate reconstruction of concave obstacles, whereas a comparison to Tikhonov-regularized LSM demonstrates that the proposed approach significantly improves obstacle recovery with sparse-aperture data.