Deconvolution methods can be applied in airborne scanning radar to enhance its angular resolution for improving the collision avoidance ability in the forward-looking direction. However, as the movement speed of the airborne platform increases, the traditional convolution signal model cannot be applied because of the model errors in the amplitude profile and Doppler phase. In this article, an accurate profile-phase deconvolution method is proposed to achieve scanning radar forward-looking super-resolution imaging, particularly for high-speed platforms. On one hand, a profile-phase convolution (PPC) model is established by analyzing the influence of high-speed platform on echo amplitude profile and Doppler phase. The proposed model accurately captures the variation of beam dwell time caused by the coupling of platform motion and beam scanning, which directly affects the echo amplitude profile. On the other hand, relying on the proposed PPC model, an adaptive regularization (AR) deconvolution method is derived to avoid hyperparameter selection. Point-target and surface-target results demonstrate that the proposed PPC model and the AR deconvolution method are competent for super-resolution imaging on high-speed platforms.

Frequency modulation continuous wave (FMCW) radar has been paid much attention in forward-looking navigation applications because of its no-blind-range capability. However, after dechirp processing, pulse interference signals may appear in the range time domain, which seriously pollutes the whole radiation direction. In this article, a cross-domain low-rank and sparse (CD-LRS) optimization framework is proposed to enhance the angular resolution and suppress the pulse interference signals based on the scanning mode of its antenna. On the one hand, to cut off and recover the polluted signals, a low-rank spectra reconstruction approach is proposed by utilizing the low-rank characteristic of the Hankel matrix formed by the interference-rejected data in the range time domain. On the other hand, to suppress the residual interference signal and enhance the angular resolution simultaneously, an adaptive sparse reconstruction method is formed in the azimuthal time domain by adopting an alternating direction method of multipliers (ADMMs)-based solver. Compared with the traditional anti-interference methods, the proposed framework can enhance the angular resolution and suppress the interference signals based on the signal features in different domains. Simulations and experimental results are applied to verify the effectiveness of the proposed framework.

Currently, regularization methods are widely applied to radar forward-looking super-resolution imaging, but imaging performance is greatly affected by the regularization parameter. In order to address this issue, the regularization method with weak-depended on parameter for forward-looking super-resolution imaging is proposed in our work. First, the objective function is established under the premise of sparse target prior; then, iteratively reweighted solver is applied to resolve the objective function. The key idea is to join the regularization weighting factor in the process of solving sparse regularization problem, which reduces the sensitivity to the regularization parameter and avoids the imaging error caused by unreasonable parameter selection. Compared to traditional sparse regularization method, the proposed method is less dependent on the regularization parameter, and the imaging performance is superior under the same conditions. Simulation results verify the effectiveness of the proposed method.

Constrained by the coarse angular resolution, the real aperture radar (RAR) typically suffers from low location accuracy. Superresolution methods have been proposed to improve its location performance by an enhanced radar image. However, the limit of angular location has not been fully investigated. In this paper, an angular deterministic (conditional) Cramer-Rao bound (CRB) of the RAR is deduced to describe the angular location error bound. First, a virtual array projection model is analyzed in spatial frequency domain, which normalize the signal model of different antenna types. Then, a general form CRB based on complex signal is deduced. Last, the result for a single target is illustrated to verify the deduced CRB. The bound is significant to the design of radar system parameters and superresolution methods.

Most of the existing angular super-resolution techniques are based on the convolution model of the target scattering coefficient and the antenna pattern, and this convolution relationship provides the potential for improving the angular resolution. However, due to the non-ideal working environment in practical applications, the antenna pattern generates phase and amplitude errors, resulting in model mismatch. Model mismatch produces errors during super-resolution processing, degrading the final imaging quality. In order to address the issue, an angular super-resolution method of real aperture radar under model mismatch condition is presented in this paper. First, we introduce an error matrix on the original convolutional model to consider model mismatch errors. Secondly, the target sparse prior is exploited to construct the objective function under the model mismatch condition. Finally, the alternating direction method of multipliers (ADMM) solver is utilized to solve the objective function, obtaining the final imaging result. Simulations verify the effectiveness of the proposed method.

Scanning radar (SR) super-resolution imaging has been widely reported recently. However, most of the existing methods are based on the amplitude convolution relationship between the reflectivity function and the antenna pattern, realizing super-resolution imaging through deconvolution. When the radar platform moves fast, Doppler phase and pattern distortion caused by the movement destroys the above convolution relation and decreases the super-resolution performance. In this paper, we proposed a pattern distorted complex convolution model to describe SR super-resolution imaging under high-speed platform. Simulations show that the proposed model is more suitable for SR super-resolution imaging under high speed platform.

Recently, super-resolution techniques have been widely used in real aperture radar super-resolution imaging. And the majorize-minimization(MM) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this paper, we strive to alleviate this problem, deriving an online MM algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The proposed method is a regularized extension of the current MM implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current MM algorithm. Our experimental assessment, conducted using simulated data, demonstrates the advantage of the online MM(OMM) algorithm in the task of sparse reconstruction for scanning radar.

Sparse superresolution algorithms have been applied in scanning radar imaging to improve its azimuth resolution. However, the inverse matrix in each iteration is usually diagonal loading by the updating result, which leads to huge computational complexity for two-dimensional echo data. In this letter, a batch-processing superresolution framework is proposed to process the echo data in parallel. On the one hand, the optimization problem for sparse target recovery is modified as matrix form, which presents batch-processing potential for two-dimensional echo data. On the other hand, the optimization problem is solved by the proposed alternating direction method of multipliers (ADMM)-based batch-processing framework, which can avoid high-dimensional matrix inversion along different range bins. Compared with traditional sparse superresolution methods, the proposed batch-processing framework is much suitable for two-dimensional echo data superresolution.

Scale information is a significant index for target measurement by real aperture radar (RAR). However, the measured target scale information by RAR is inaccurate because of the limited angular resolution. In this paper, to enhance the scale measurement ability of RAR, a generalized hybrid regularization (GHR) approach is proposed by combining the generalized sparse (GS) regularization norm and the generalized total variation (GTV) regularization norm. On the one hand, the GHR approach is proposed to simultaneously enhance the angular resolution and the scale information of targets by combing the generalized regularization norms. The GS regularization norm can improve the reconstructed angular resolution due to its sparsity over the L1 norm. The GTV regularization norm can preserve the steep target contour because of its edge enhancement ability over the total variation (TV) norm. On the other hand, based on the GHR optimization function, an adaptive iterative reweighted (AIR) solver is proposed to reduce the number of manually selected regularization parameters, allowing for accurate scale information reconstruction. Simulations and experiments verify the performance of the proposed method. Based on the proposed approach and solver, the target scale information can be accurately observed.

To address scanning radar forward-looking sea imaging, this paper proposed a super-resolution method based on Weibull distribution. The proposed method in our work introduced the generalized Gaussian distribution and Weibull distribution to represent the prior distribution of the target and the sea clutter respectively, which are more suitable for actual sea imaging. And the corresponding objective function was derived under the MAP framework. In order to overcome the objective function's nonlinearity, this paper adopt Newton-Raphson iterative method to resolve it. Finally, through simulations, which indicates that the proposed method has superior imaging performance compared with other traditional methods for sea imaging.

Exploiting the sparse property of the target of interest to achieve super-resolution imaging has been applied to real aperture radar (RAR) forward-looking imaging field. In this paper, we proposed a regularized FOCUSS method to realize RAR forward-looking super-resolution imaging. In addition, we discussed the influence of initialization on the imaging result, and selected the most suitable initialization for RAR forward-looking super-resolution imaging. Compared with the traditional sparse method based on Majorize-Minimization, our proposed algorithm has faster convergence speed under the same parameters condition.

The real aperture radar (RAR) system can present full-view observation capability, but the coarse azimuth resolution restricts its application. Therefore, various super-resolution deconvolution methods are widely used in the real aperture super-resolution imaging field. But conventional deconvolution approaches only rely on amplitude information of antenna pattern profile, it will behave worse when forward-looking imaging with high speed or squint imaging. This paper analyzes the influence of phase and constructs a corresponding amplitude-phase model to resolve this problem. Finally, the effectiveness of the proposed amplitude-phase convolution model for forward-looking imaging with high speed or squint imaging is verified by simulations.

In radar forward-looking super-resolution imaging, improving the azimuth resolution while acquiring the contour information of the target has significant research value. In this letter, an approach based on the balanced Tikhonov and total variation (TV) deconvolution is proposed for radar forward-looking super-resolution imaging. We combine the Tikhonov regularization and TV regularization to construct the objective function and resolve the respective cost function using the alternating direction method of multipliers (ADMM). In each iteration, the gradient function of the target scattering coefficient is used as the adaptive weighted parameter to control automatically the weighting between the penalty terms from TV and the Tikhonov regularization. For the target with a sharper outline, the proportion of TV regularization penalty terms is increased; for the target with a smoother outline, the proportion of penalty term from the Tikhonov regularization is enhanced. The simulation and experimental results are considered to show the effectiveness of the proposed method. Compared with traditional super-resolution imaging methods, the proposed approach has superior outline retention capacity.

For real aperture radar, its azimuth resolution is much coarser than the range resolution after pulse compression, super-resolution algorithms are desired to enhance its azimuth resolution. However, the super-resolution algorithms must require enough azimuth sampling to ensure its performance. When wide scanning scope or dense azimuth sampling, the amount of data will increase significantly, which brings large computational burden to super-resolution processing. To cover this problem, we propose a two-step dimension reduction strategy. Firstly, by using linear sketching technology, the high-dimensional matrices are projected to the low-dimensional space, thus accelerating the matrix-matrix multiplications in super-resolution algorithms. Secondly, exploiting Sherman-Morrison formula, we further realized the acceleration of the matrix inversion in super-resolution algorithms. The proposed two-step acceleration strategy in our work is applicable to the existing deconvolution super-resolution algorithms, including regularization methods, Bayesian methods. It can be verified by simulation and experimental data that the proposed accelerated algorithms have advantages in computing time without losing the quality of super-resolution imaging.

Recently, a variety of super-resolution (SR) methods have been devoted to enhancing the angular resolution of real beam mapping (RBM) imagery in modern microwave remote sensing applications. When addressing large-scale datasets, however, they suffer from notably high computational complexity due to high-dimensional matrix inversion, multiplication, or singular value decomposition (SVD). To overcome this limitation, this article presents a low-complexity SR strategy based on adaptive low-rank approximation (LRA). Our underlying idea is first to construct a random matrix sketching to sample the raw echo measurements and restore the surface map of reflectivity in a low-dimensional linear space. The resulting low-complexity strategy enables substantial computational complexity reduction for a group of SR methods, at the cost of introducing a manually adjusted LRA parameter. Using the Fourier transform-based antenna analysis method, we further reveal that the LRA parameter that ensures support resolution improvement can be determined by a closed-form function of the aperture length, the wavelength, and the field of view, allowing for adaptively and efficiently selecting the optimal LRA parameter that well balances the tradeoff between LRA error and computational efficiency. We use both simulated and real datasets to demonstrate that the proposed LRA-based SR strategy can provide significant speedup without performance loss.

To realize high azimuth resolution for sea-surface targets, this paper proposes a superresolution imaging method that relies on the Weibull distribution. The proposed method introduces the generalized Gaussian distribution and Weibull distribution to represent the statistical distribution function of the target prior information and sea clutter, respectively. The corresponding objective function was derived under the maximum a posteriori (MAP) criterion. To address the nonlinearity of the objective function, this paper adopts the NewtonRaphson iterative method to resolve it. Simulations and experimental data assessment indicate that the proposed method has superior superresolution imaging performance compared with other traditional superresolution methods for sea-surface target imaging.

Real aperture super-resolution (RAS) technology is widely used in the field of radar forward-looking imaging. However, traditional RAS technology is based on the space-to-ground scanning mode. The essence of this technology is azimuth (angle) super-resolution, which is a one-dimensional super-resolution technology. In our work, we consider applying RAS technology to the space-to-space scanning. In this mode, we regard the echo of each range slice as the convolution of the target scattering coefficient distribution and the antenna pattern function. Its essence is azimuth and pitch super-resolution, which is a two-dimensional super-resolution technology. Finally, a reasonable objective function is constructed under the framework of regularization, and the ADMM solver is used to achieve two-dimensional super-resolution imaging. Simulations will prove the effectiveness of the proposed two-dimensional super-resolution algorithm.

Most existing super-resolution imaging methods fail to work in low signal-to-noise ratio (SNR) condition due to the ill-posed antenna measurement matrix, but the sparse-truncated singular value decomposition (TSVD) method can effectively suppress noise and improve azimuth resolution in low SNR condition. However, the current sparse-TSVD method encounters large computation cost, resulting in a slow algorithm speed. In this work, a fast sparse-TSVD super-resolution imaging method of real aperture radar is proposed. First, the proposed method is based on the results of TSVD, using the truncated unitary matrix and diagonal matrix to reconstruct the signal convolution model. The dimension of the reconstructed antenna measurement matrix reduces from$N \times N$to$k \times N$, and the dimension of the reconstructed echo matrix reduces from$N \times 1$to$k \times 1$, where$N$is azimuth sampling points and$k$is truncation parameter,$N \gg k$. Much of the expensive matrix– multiplication computation can then be performed on the smaller matrices, thereby accelerating the algorithm. Second, an objective function is established as the${l_{1}}$constraint based on the regularization strategy. Lastly, this article employs iterative reweighted least square (IRLS) method to solve the objective function, and the dimension of the reversed matrix is lessened from$N \times N$to$k \times k$, speeding up the algorithm further. The simulation and real data verify that the proposed algorithm not only improves the azimuth resolution in low SNR condition but also increases computational efficiency compared with the sparse-TSVD method.

Because of the poor performance of the conventional total variation (TV) super-resolution imaging method in low signal-to-noise ratio (SNR) condition, a TV super-resolution imaging method based on the truncated singular value decomposition (TSVD) strategy is proposed. First, based on the regularization theory, the TV function is selected as the constraint term to construct objective function. Second, to solve the problem of noise amplification faced by the conventional TV method, this article reconstructs the objective function based on the TSVD strategy, which improves the antinoise performance by discarding small singular values of antenna convolution matrix. Finally, due to the nondifferentiable property of reconstructed objective function, this article utilizes the iterative reweighted norm (IRN) method. Since the influence of the noise is weakened by the TSVD strategy, the proposed method can achieve super-resolution imaging and contour preservation in low SNR condition. The simulation and experimental results demonstrate the effectiveness of the proposed method.

The truncated singular value decomposition (TSVD) method has been applied to radar forward-looking imaging, however which suffers limited resolution. Especially under low signal to noise ratio (SNR) condition, there is a contradiction between keeping more singular values to improve resolution and suppressing noise amplification. In this paper, a method based on singular value weighted truncation is proposed to improve the resolution under low SNR condition. First, this paper analyses the essence of the conventional TSVD method. Then, the passage constructs a new singular value function to reserve more singular value on the original truncation parameter. Compared with the conventional TSVD method, the more singular values are retained which can improve the resolution under the premise of suppressing noise. Simulations demonstrate the effectiveness of the proposed method.