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.

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm.

Forward-looking radar plays an important role in many military and civilian fields. However, the problem of low azimuth resolution has restricted its applications seriously. Although many methods have been used to achieve azimuth superresolution, the traditional methods suffer from noise amplification or limited resolution under low signal-to-noise (SNR) condition. In this paper, we proposed a Bayesian deconvolution method which relies on linearized Bregman to achieve azimuth superresolution of forward-looking radar imaging. We first used the complex Gaussian distribution and Laplace distribution to describe the distribution of noise and targets, respectively, and transformed the superresolution problem into a convex optimization problem by maximum a posteriori estimation in the Bayesian framework. Second, linearized Bregman algorithm was used to solve the convex optimization problem. The proposed method introduces the prior information of noise and target, and overcomes the ill-posedness of deconvolution. As a result, the azimuth resolution is remarkably enhanced. Besides, the proposed method has high computational efficiency by linearizing objection function, so it can take both time cost and resolution improvement into consideration. Finally, the superior performance was verified by simulation and experimental data.

Forward-looking imaging for sea-surface target has raised many concerns in the fields of vessel monitoring and sea rescue in all-day and all-weather time. Constrained by the imaging principle, conventional high-resolution radar imaging techniques, such as synthetic aperture radar and Doppler beam sharpening, are incapable of forward-looking imaging. Currently, scanning radar is the main tool to obtain forward-looking image, but it suffers from low angular resolution. Recently, much attention has been paid to superresolution methods, which can provide improved angular resolution over that of real-beam scanning radar. However, these methods are mainly used for ground targets, which mainly consider the ground clutter. For sea surface imaging, the distribution of sea clutter is more complex than the ground clutter. Besides, the performance of sea surface imaging is limited by the moving speed of targets. If we directly use these existing methods to sea-surface target imaging, it will lead to amplification of clutter and even elimination of the real target. In this paper, we consider the sea clutter distribution and present a Rayleigh-based sparse maximum a posteriori (RSMAP) deconvolution algorithm to improve the angular resolution of the sea-surface low-speed and static targets. First, the Rayleigh distribution is considered as the likelihood function because it can describe the amplitude characteristic of the sea clutter. Second, since the target concerned is often sparse compared with the imaging region, the sparse constraint is considered as the prior information of the sea-surface targets in objective function. Finally, the targets distribution is obtained by MAP estimation under the Bayesian framework. The simulations and measured data processing results have shown that the proposed algorithm provides better angular resolution and target location accuracy than traditional methods.

Deconvolution methods can be used to improve the azimuth resolution in airborne radar imaging. Due to the sparsity of targets in airborne radar imaging, an $L_1$ regularization problem usually needs to be solved. Recently, the Split Bregman algorithm (SBA) has been widely used to solve $L_1$ regularization problems. However, due to the high computational complexity of matrix inversion, the efficiency of the traditional SBA is low, which seriously restricts its real-time performance in airborne radar imaging. To overcome this disadvantage, a fast split Bregman algorithm (FSBA) is proposed in this paper to achieve real-time imaging with an airborne radar. Firstly, under the regularization framework, the problem of azimuth resolution improvement can be converted into an $L_1$ regularization problem. Then, the $L_1$ regularization problem can be solved with the proposed FSBA. By utilizing the low displacement rank features of Toeplitz matrix, the proposed FSBA is able to realize fast matrix inversion by using a Gohberg–Semencul (GS) representation. Through simulated and real data processing experiments, we prove that the proposed FSBA significantly improves the resolution, compared with the Wiener filtering (WF), truncated singular value decomposition (TSVD), Tikhonov regularization (REGU), Richardson–Lucy (RL), iterative adaptive approach (IAA) algorithms. The computational advantage of FSBA increases with the increase of echo dimension. Its computational efficiency is 51 times and 77 times of the traditional SBA, respectively, for echoes with dimensions of $218\times 400$ and $400\times 400$ , optimizing both the image quality and computing time. In addition, for a specific hardware platform, the proposed FSBA can process echo of greater dimensions than traditional SBA. Furthermore, the proposed FSBA causes little performance degradation, when compared with the traditional SBA.

The problem of low azimuth resolution has restricted the applicability for radar forward-looking imaging in practice. In this paper, a sparse with fast majorization-minimization (SFMM) superresolution algorithm was proposed to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we analyzed the azimuth signal of the radar forward-looking area and modeled the azimuth signal as a convolution of antenna pattern and targets distribution. Second, the superresolution problem was converted into an L ₁ regularization issue by introducing the L ₁ norm to represent the distribution of the targets under the regularization framework. Third, according to the principle of majorization-minimization (MM) algorithm, a simple L ₂ regularization issue was obtained to replace the difficult L ₁ one, and the real target distribution was obtained by solving the L ₂ regularization problem (We named it sparse with MM (SMM) superresolution algorithm for convenience). Then, in order to improve the computational efficiency of the algorithm, we adopted the second-order vector extrapolation idea to accelerate the conventional MM algorithm and solve the L ₂ regularization problem. The simulation and real data verified that the proposed SFMM algorithm not only improves the azimuth resolution in radar forward-looking imaging but also increases convergence speed on the basis of SMM superresolution algorithm.