Forward-looking Radar Imaging Research Articles

An airborne radar forward-looking imaging algorithm based on multi-scale U-shaped networks

ABSTRACT Radar forward-looking imaging has gained increasingly significance in diverse applications, including battlefield reconnaissance, target surveillance, and precision guidance. While Synthetic Aperture Radar techniques are widely employed to achieve high azimuth resolution, the domain of forward-looking imaging has its challenges. These challenges arise due to the minimal Doppler frequency changes in forward-looking areas and the symmetrical Doppler history about flight path. These constraints negatively impact the radar’s ability to accurately detect and image targets in forward-looking areas. Consequently, there has been a growing interest in the development of novel approaches to these problems. Due to the application of deep learning, image super-resolution methods based on convolutional neural networks (CNNs) have received extensive attention and demonstrated good performance. This avenue of research provides fresh insights into improving the azimuth resolution of radar imaging. In this letter, we propose a novel convolutional neural network, called MSUS-Net (Multi-scale U-shaped Network), for radar forward-looking imaging. It combines asymmetric feature fusion with a unique connection structure to replace traditional layers. Moreover, different from conventional image restoration networks that use content loss, we use the L1 distance in the frequency domain, restoring missing high-frequency details. Simulated and real radar data validate that this approach offers robust recovery and better imaging performance.

Untrained Metamaterial-Based Coded Aperture Imaging Optimization Model Based on Modified U-Net

Metamaterial-based coded aperture imaging (MCAI) is a forward-looking radar imaging technique based on wavefront modulation. The scattering coefficients of the target can resolve as an ill-posed inverse problem. Data-based deep-learning methods provide an efficient, but expensive, way for target reconstruction. To address the difficulty in collecting paired training data, an untrained deep radar-echo-prior-based MCAI (DMCAI) optimization model is proposed. DMCAI combines the MCAI model with a modified U-Net for predicting radar echo. A joint loss function based on deep-radar echo prior and total variation is utilized to optimize network weights through back-propagation. A target reconstruction strategy by alternatively using the imaginary and real part of the radar echo signal (STAIR) is proposed to solve the DMCAI. It makes the target reconstruction task turn into an estimation from an input image by the U-Net. Then, the optimized weights serve as a parametrization that bridges the input image and the target. The simulation and experimental results demonstrate the effectiveness of the proposed approach under different SNRs or compression measurements.

Radar Forward-Looking Imaging for Complex Targets Based on Sparse Representation With Regularized AK-SVD and GGAMP-VSBL Algorithm

Radar Forward-Looking Imaging for Complex Targets Based on Sparse Representation With Regularized AK-SVD and GGAMP-VSBL Algorithm

Scanning Radar Forward-Looking Imaging Under High-Speed Platform by Accurate Profile-Phase Deconvolution Method

Scanning Radar Forward-Looking Imaging Under High-Speed Platform by Accurate Profile-Phase Deconvolution Method

Enhanced Forward-Looking Missile-Borne Bistatic SAR Imaging With Electromagnetic Vortex

Bistatic radar possesses the forward-looking imaging performance that conventional synthetic aperture radar (SAR) lacks. Nevertheless, bistatic forward-looking imaging is seriously limited by the motion of the transmitter and receiver, resulting in a restrained resolution. In this work, the method for vortex missile-borne bistatic SAR (BSAR) is proposed, which combines the orbital angular momentum (OAM) beam with the traditional missile-borne BSAR technique to obtain a larger Doppler bandwidth. Higher azimuth resolution is achieved by compensating large spatial range cell migration (RCM) and serious range-azimuth coupling. First, the imaging scene of vortex missile-borne BSAR and the OAM-based echo model are established and deduced. Second, the preprocessing method for amplitude compensation is proposed with the design of the transmitted OAM beam, which improves the uncorrelation between different pulse echoes. Then, the improved range Doppler (RD) algorithm with azimuth compensation factor is proposed for vortex missile-borne BSAR to obtain the super-resolution 2-D reconstruction for the target. Simulation results show that compared with conventional missile-borne BSAR, the proposed vortex missile-borne BSAR possessing the extending Doppler bandwidth can obtain superior azimuth resolution performance with the identical synthetic aperture length situation. Furthermore, this work will contribute to the application for OAM-carrying in the field of radar forward-looking imaging and the development for high-resolution missile-borne BSAR imaging technology.

Sparse array radar forward-looking imaging based on fast atomic norm minimization

ABSTRACT The sparse array imaging radar could largely reduce the system complexity, but it also suffers from poor azimuthal resolution due to the limited aperture length and incomplete observation caused by non-uniform array configuration. In this letter, a radar forward-looking imaging method with improved imaging quality and reduced system complexity is proposed. Firstly, the matrix completion method is utilized to recover the missing signals of sparse array. Then, based on the plane-wave assumption, a fast 2-dimensional atomic norm minimization (ANM) is adopted to solve the positions and amplitudes of scatterers. As a continues compressed sensing method, the result of ANM-based method earns sharper main-lobes and much lower-level side-lobes compared to the fast Fourier transform -based method. Although the resolution of proposed method is still limited by Rayleigh resolution, the imaging quality can be greatly improved by emphasizing main-lobes and avoiding the perturbation of side-lobes. Simulations and experiments are carried out to demonstrate the performance of the proposed method.

Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints

Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods.

A Super-Resolution Scheme for Multichannel Radar Forward-Looking Imaging Considering Failure Channels and Motion Error

To obtain high-resolution images of the objects in front of platform, a super-resolution scheme for multichannel radar forward-looking imaging considering failure channels and motion error is proposed in this study. In the scheme, a failure channel detection method based on the correlation of pulse-compressed data of different channels is presented first, and then a revised steering matrix considering failure channels and motion error is constructed. Finally, the echo data are processed by the iterative adaptive approach (IAA) with the revised steering matrix. Simulation results are given to illustrate the effectiveness of the proposed scheme when dealing with failure channels and motion error.

Enhancing Forward-Looking Image Resolution: Combining Low-Rank and Sparsity Priors

Compressed sensing (CS)-based imaging technology has attracted a lot of interest because it can enhance imaging resolution. Targets of interest for forward-looking imaging radar are typically few in comparison to the entire imaging region. This sparsity allows for the natural application of CS to the reconstruction of high-resolution forward-looking images. However, conventional CS-based imaging methods can only perform well when the signal-to-noise ratio (SNR) is high. Strong noise in radar imaging prevents the CS-based methods from producing excellent imaging results. Inspired by the low-rank property of the received radar target echo and the sparsity of the forward-looking image targets, we present a combined low-rank and sparse prior restricted model for forward-looking imaging with a multichannel array radar to overcome strong noise. To solve the low-rank joint sparse double prior constraint optimization problem, an augmented Lagrange multiplier (ALM) reconstruction method under the framework of the alternating direction multiplier method (ADMM) is proposed. Finally, the results of simulation and real measurement data indicate that our presented method is fairly effective at enhancing the azimuth resolution and robustness of forward-looking radar imaging in comparison to other current methods.

Bias Analysis and Correction for Ill-Posed Inversion Problem with Sparsity Regularization Based on L1 Norm for Azimuth Super-Resolution of Radar Forward-Looking Imaging

The sparsity regularization based on the L1 norm can significantly stabilize the solution of the ill-posed sparsity inversion problem, e.g., azimuth super-resolution of radar forward-looking imaging, which can effectively suppress the noise and reduce the blurry effect of the convolution kernel. In practice, the total variation (TV) and TV-sparsity (TVS) regularizations based on the L1 norm are widely adopted in solving the ill-posed problem. Generally, however, the existence of bias is ignored, which is incomplete in theory. This paper places emphasis on analyzing the partially biased property of the L1 norm. On this basis, we derive the partially bias-corrected solution of TVS and TV, which improves the rigor of the theory. Lastly, two groups of experimental results reflect that the proposed methods with partial bias correction can preserve higher quality than those without bias correction. The proposed methods not only distinguish the adjacent targets, suppress the noise, and preserve the shape and size of targets in visual terms. Its improvement of Peak Signal-to-Noise Ratio, Structure-Similarity, and Sum-Squared-Errors assessment indexes are overall 2.15%, 1.88%, and 4.14%, respectively. As such, we confirm the theoretical rigor and practical feasibility of the partially bias-corrected solution with sparsity regularization based on the L1 norm.

Azimuth Improved Radar Imaging With Virtual Array in the Forward-Looking Sight

The restricted physical antenna aperture usually degrades the acquisition of target images with high azimuth resolution in radar forward-looking sight. To make breakthrough on this problem, this article offers a forward-looking radar imaging methodology by the illumination beam design and virtual aperture processing, which can improve the azimuth resolution. First, the imaging model is established with respect to the observation scenario, and the echo characteristics of the forward-looking virtual array are analyzed. Second, compared with the back-projection (BP)-based algorithm, the polar format imaging (PFI) algorithm with higher efficiency is proposed to obtain the focused images of target in the forward-looking locations. Finally, comprehensive simulations and real measured experiments are performed to corroborate the effectiveness of the proposals and show the imaging performance with different influencing factors.

Radar Forward-Looking Imaging for Complex Targets Based on Sparse Representation With Dictionary Learning

Radar forward-looking imaging has always been a difficult problem in the radar detection. Modulating the wavefront of radar transmitted signals provides a feasible method for radar forward-looking imaging. The existing high-resolution radar imaging algorithms assume that the target scattering coefficients are sparsely distributed. However, for complex targets, the scattering coefficients no longer satisfy the sparse prior. To solve the problems of forward-looking imaging for complex targets, in this letter, a sparse representation imaging method with dictionary learning is proposed. First, the principle and imaging model of microwave modulation are introduced to achieve radar forward-looking imaging. Second, the dictionary learning method is developed to learn adaptive transformation that exploit the edge features and structural information as well as provide a sparser presentation to further improve the quality of radar images. Third, the imaging performance for different types of complex targets under different signal-to-noise ratios are analyzed. The simulation results show that the proposed method can effectively reconstruct different types of complex targets.

Microwave Coincidence Imaging Based on Attributed Scattering Model

In this letter, a novel microwave coincidence imaging (MCI) method based on the attributed scattering model (ASM) is proposed. Unlike the classical MCI model which assumes the target as a set of discrete scatterers, the ASM-based MCI equation contains three kinds of reference matrices corresponding to the point-scatterers (PSs), the line-segment-scatterers (LSSs) and the rectangular-plate-scatterers (RPSs), respectively. Hence the ASM-based MCI could resolve richer information about the object geometries. By solving the imaging equation via the alternating direction method of multipliers (ADMM) algorithm, the scattering coefficients will be obtained and the target can be reconstructed according to the presetting parameter sets. Meantime, the ASM-based MCI also earns the superresolution ability like the classical MCI, which is brought in by the temporal-spatial orthogonal radiation field. Simulations and experiment are carried out to demonstrate the performance and superresolution ability of proposed method. The ASM-based MCI makes contributions to the progress of radar forward-looking imaging theory and technology.

Real Aperture Radar Forward-Looking Imaging Based on Variational Bayesian in Presence of Outliers

Real Aperture Radar Forward-Looking Imaging Based on Variational Bayesian in Presence of Outliers

A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.

Resolution Analysis of Vortex Electromagnetic Radar Imaging

The vortex ElectroMagnetic (EM) wave, whose phase wavefront is modulated by the Orbital Angular Momentum (OAM), has received immense attention, especially in the field of forward-looking radar imaging. Based on the fundamental principle and imaging method of the vortex EM radar, the azimuth resolution was studied in this paper. First, the circumstance of considering the Bessel amplitude term was analyzed, indicating that the azimuth resolution was determined by the effective scope of OAM modes. Then, an effective method for calculating the scope of OAM modes was proposed, including the expressions of azimuth resolution, spatial resolution, and super-resolution were characterized. Finally, the fundamental resolution performance with different influencing factors was analyzed via simulations. The analysis showed that changing the wavelength aperture ratio and imaging elevation could increase the effective scope of OAM modes, which improved the azimuth resolution. Through data fitting, the approximate expressions of the effective scope of OAM modes and the resolution of the super-real aperture radar with respect to the wavelength aperture ratio and imaging elevation were obtained separately, providing a reference for the parameters design and optimization of vortex EM wave radar.

3-D Object Imaging Method With Electromagnetic Vortex

The electromagnetic (EM) vortex imaging has demonstrated superior performance in target detection and imaging with azimuthal super-resolution. However, the restricted elevation resolution degrades the acquisition of target spatial information, which limits the development of the radar imaging technology based on orbital angular momentum (OAM). This article offers a solution to achieve the 3-D EM vortex imaging, by effectively utilizing relative motion between radar and target in the line-of-sight (LOS) direction. First, the forward-looking radar imaging scenario is presented, the 3-D echo model is derived, and the characteristics are analyzed as well. Second, the imaging method, based on the back-projection (BP) and spectrum estimation method, is proposed to obtain the target’s 3-D focused image. Furthermore, the influence factors about the elevation resolution are analyzed by the point spread function (PSF). Finally, simulations are carried out to verify the effectiveness of the theoretical analyses.

A Superfast Super-Resolution Method for Radar Forward-Looking Imaging

The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg-Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from to . It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice.

Superresolution of Radar Forward-Looking Imaging Based on Accelerated TV-Sparse Method

Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging. However, due to the existence of matrix inversion, it suffers from high computational complexity, which restricts the ability of radar real-time imaging. In this article, an Gohberg-Semencul (GS) decomposition-based fast TV-sparse (FTV-sparse) method is proposed to reduce the computational complexity of TV-sparse method. The acceleration strategy utilizes the low displacement rank features of Toeplitz matrix, realizing fast matrix inversion by using a GS representation. It reduces the computational complexity of traditional TV-sparse method from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), benefiting for improvement of the computing efficiency. The simulation and experimental data processing results show that the proposed FTV-sparse method has almost no resolution loss compared with the traditional TV sparse method. Hardware test results show that the proposed FTV-sparse method significantly improves the computational efficiency of TVsparse method.

TV-Sparse Super-Resolution Method for Radar Forward-Looking Imaging

Real-aperture radar can be utilized to realize forward-looking imaging by antenna scanning the imaging region. However, low azimuth resolution seriously affects its practical application. Although traditional super-resolution methods could enhance azimuth resolution to a certain extent, effective preservation of contour information for important targets still remains to be a problem. In this article, a method of total variation-sparse (TV-sparse) multiconstraint deconvolution is proposed to improve azimuth resolution of forward-looking imaging as well as preserve contour information of important targets. Since our interested targets usually appear to be sparse, the sparse constraint of the target is introduced first to achieve high resolution of forward-looking images, which may cause the loss of target contour information in the meantime. Second, total variation (TV) constraint is introduced based on the sparse constraint, converting traditional single-constraint super-resolution problem to a multiconstraint problem. We then use the split Bregman algorithm (SBA) to solve the multiconstraint problem, whose solution is the super-resolution image of radar forward-looking region. Compared with traditional super-resolution methods, the proposed method can improve the azimuth resolution of radar forward-looking imaging as well as better restore target contour information by adjusting respective weights of sparse constraint and TV constraint. Finally, the performance of the proposed method is validated with the simulation and measured data.