In recent years, iterative adaptive approach (IAA) has been proposed for super-resolution imaging in scanning radar, providing improved azimuth resolution. Traditional IAA involves computing the correlation matrix R for target scattering in each range cell, leading to iterative row-by-row solving and matrix inversion operations, causing high computational complexity. To this end, this paper proposes a Fast Batch-Based Iterative Adaptive Approach (FBB-IAA) that enables parallel and synchronized super-resolution processing of each range cell in the echo matrix. Additionally, it utilizes the two-dimensional conjugate gradient (2D-CG) method to avoid matrix inversion operation, significantly reducing the computational complexity compared to traditional IAA. Simulation results validate the superiority of the proposed method.

High-squint super-resolution imaging for airborne scanning radar is crucial in remote sensing and earth information observation. Various methods have been proposed to enhance the azimuth resolution of imaging. However traditional methods are often limited by the requirement for manual adjustment of hyperparameters. In this paper, we propose a hyperparameter-free high-squint super-resolution method for airborne scanning radar based on likelihood based estimation of sparse parameters (LIKES). Compared to traditional sparse imaging methods, our presented approach ensures super-resolution while addressing the issue of manual hyperparameter adjustment. Simulation results demonstrate the effectiveness of the proposed method.

Super-resolution techniques based on the convolution model of target scattering coefficient and antenna pattem have been widely used in scanning radar forward-looking imaging for past few years. In previous work, the sparse regularization model is proved to be effective in solving the recovery problem of sparse scenes. However, traditional regularization methods usually only add constraints to the target and the regularization parameters are difficult to choose, thus easily causing noise amplification and image recovery distortion. In this paper, a parameter-free estimation method based on low-rank and sparse hybrid constraints is proposed. Firstly, based on the traditional regularization model, sparse and low-rank constraints are added to the target and background respectively to effectively suppress the noise amplification; then, a parameter-free estimation solver is proposed to solve the problem that the regularization parameter is much more difficult to choose. In addition, the superior performance of the proposed method is verified by simulations.

In recent years, the Sparse Iterative Covariance Estimation (SPICE) algorithm has been applied to Direction of Arrival (DOA) estimation in MIMO radar, significantly enhancing radar resolution and quality. However, the high computational complexity of this algorithm poses challenges for real-time processing performance. In this paper, we introduce an online implementation framework for MIMO radar DOA estimation based on the SPICE algorithm. By incorporating sliding window processing, the complexity of the parameter space in each iteration is reduced with minimal resolution loss. Compared to existing SPICE algorithms, the proposed online sliding window qSPICE method achieves substantial computational savings without sacrificing performance. Simulation results demonstrate the superior performance of the proposed method.

Sparse super-resolution algorithm has been used in scanning radar imaging to improve its azimuth resolution. For sparse targets, traditional super-resolution methods usually introduce L1norm to improve azimuth resolution. However, the results obtained based on the L1norm are usually biased estimates, which leads to the limited effect of improving the azimuth resolution. In this paper, a sparse target reconstruction method based on non-convex penalty term is proposed. On the one hand, in order to reduce the bias effect, the L1norm in the cost function is replaced with the SCAD (smoothly clipped absolute deviation) penalty term that is closer to the L0norm. On the other hand, ADMM method is used to solve multi-constraint problems, and we use iterative shrinkage threshold method to solve non-convex optimization subproblem. Compared with the traditional sparse super-resolution method, the proposed method has better performance. The superior performance of the proposed method is verified by simulation and measured data processing.

The imaging process of synthetic aperture radar (SAR) inherently introduces distortions such as blurring, noise, and various disturbances, leading to a notable degradation in image quality. Especially in challenging environmental conditions, SAR images often suffer from reduced resolution and limited detailed information. This letter proposes an innovative super-resolution reconstruction approach utilizing a self-normalized enhanced generative adversarial network (SNEGAN) to address these challenges. The utilization of the scaled exponential linear unit as the generator’s activation function enhances the self-normalization capability of the generative adversarial network, enabling improved adaptation to SAR image scenes. Furthermore, the exclusion of the batch normalization layer is introduced to alleviate computational demands and mitigate model oscillations. Experimental evaluations conducted on RSDD-SAR datasets validate the method’s superior performance in terms of both resolution enhancement and denoising.

Forward-looking radar (FLR) has been widely discussed because of its super-resolution capability. However, for the high-speed radar platform, the super-resolution performance of FLR degrades significantly due to the limited signal model accuracy. In this article, to observe the azimuth–elevation information of multiple targets based on a high-speed radar platform, a sparse Doppler phase convolution (SDPC) model is proposed by randomly and sparsely scanning the radar beam to reduce the coherent processing interval (CPI) and limit the signal model errors. On the one hand, the Doppler phase is introduced to characterize the vector superposition relations of the echo in each azimuth–elevation direction, thus limiting the error of the conventional convolution model (CM). On the other hand, an azimuth–elevation sparse scanning scheme is proposed to reduce the CPI, allowing for accurate second-order approximation of the range history and further limiting the reconstructed errors for high-speed radar platforms. In addition, the velocity application boundary and the sparsity boundary of the SDPC model are quantitatively analyzed. Simulations compare and validate the performance of the proposed SDPC model with the conventional CM using three classical super-resolution algorithms. Based on the proposed model, azimuth–elevation information of multiple targets can be accurately reconstructed on high-speed radar platforms.

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.

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.

Recently, the iterative adaptive approach (IAA) has been widely applied to enhance the azimuth resolution of real beam mapping (RBM) imagery. However, the IAA suffers from extremely high computational complexity in practice. This paper proposes a Split IAA for sparse scene to reduce the complexity. First, the IAA cost function is decomposed. Then the echo data is split into blocks, and the iterative model is redefined according to the target block and its corresponding cost function. Consequently, the high-dimensional data inversion problem is decomposed into multiple low-dimensional sub-problems to achieve fast super-resolution imaging. The measured results show that the proposed Split IAA significantly reduces the computational complexity without affecting super-resolution performance.

Recently, a two-dimensional (2-D) scanning radar super-resolution model has been proposed to simultaneously achieve azimuth-pitch super-resolution imaging. However, due to the addition of the pitch dimension, the complexity of the state-of-art methods becomes extremely high. In this paper, based on the sparse learning via iterative minimization (SLIM), we propose a low-complexity 2-D sparse scanning radar super-resolution method. First, the signal model of 2-D scanning radar is established. Then, base on the traditional SLIM method, the 2-D scattering estimation of the target can be iteratively solved by exploiting the conjugate gradient (CG) algorithm and the Kronecker product property. Compared with the existing methods, the proposed method has lower computational complexity and stronger adaptive ability without losing resolution performance. The simulation verifies the effectiveness of the proposed method.

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.

Recently, deep learning has gained increasing popularity in array signal processing. In this paper, we estimate the direction of arrival (DOA) for the multiple-input and multiple-output (MIMO) radar system based on deep learning. First, we convert DOA estimation into a linear inverse problem with spatial sparsity, and construct a neural network based on the iterative shrinkage thresholding algorithm (ISTA) to improve the interpretability of the network. Then, a stacked denoising autoencoder (DAE) is employed to achieve data-driven denoising, which improves the anti-jamming ability of DOA estimation. Finally, a new deep unfolded network named denoising learned ISTA (Denoising-LISTA) is proposed for DOA estimation. Simulation results illustrate that the proposed method improves the robustness of DOA estimation with single snapshot sampling and keeps significant predominance in beam sharpening and sidelobe suppression.

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.

Ground-to-air real aperture radar scans the airspace to acquire range-azimuth-pitch 3D echoes for imaging. In real situations, the echo data may be corrupted by interference, making it difficult to reconstruct the original scene using echoes with missing data. To overcome this problem, a fast superresolution imaging method under the non-uniform sampling model is proposed in this paper. Firstly, a non-uniform sampling model is proposed to well model the echoes with missing data, which makes it possible to reconstruct the real target distribution from the echoes with missing data. Secondly, we use the sparse regularization (SR) super-resolution imaging method to reconstruct the real target distribution. Since the high dimension of the dictionary matrix leads to the expensive computational cost of the SR method, we propose a fast superresolution imaging algorithm based on low-rank approximation to reconstruct the targets quickly. Simulation results show that the original scene can be effectively reconstructed from the echoes with missing data based on our proposed model, and the proposed algorithm greatly improves the computational efficiency compared with the traditional methods while not leading to a loss of imaging performance.

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.

Recently, a sparse super-resolution method relying on L1iterative reweighted norm (IRN) has been proposed to improve the imaging resolution of scanning radar. However, the method has poor adaptability due to the noise-sensitive user-parameter. To this end, an adaptive L1iterative reweighted sparse super-resolution method with no user-parameter is derived. Firstly, the scanning radar super-resolution model is established. Secondly, the user-parameter selection in the L1-IRN method is analyzed. Finally, the adaptive iteration weights are derived by transforming the sparse estimation problem into a maximum posterior (MAP) estimation problem. Compared with the existing L1-IRN method, the proposed method does not have any user-parameter, so it has adaptability to different signal-to-noise ratios (SNR) and is more robust. Simulation verifies the superiority of the proposed method.

Synthetic aperture radar (SAR) imaging for moving target can obtain complete situational awareness information of the detection area, and can realize the monitoring and control for moving target in the region of interest, which has important military and civilian dual-use value. However, due to the complex motion of target, the processing results of the existing SAR imaging methods severly defocused. In this paper, a high resolution SAR imaging method for moving target is proposed. First, we eliminate the coupling induced by linear range cell migration (RCM) by keystone transform. Then, the particle swarm optimization algorithm (PSO) is utilized to estimate the Doppler frequency rate, which can solve the problem of Doppler frequency rate mismatching when azimuth compression. Simulation results verifies the effectiveness of the proposed method.

Spatial resolution of synthetic aperture radar (SAR) is a vital index to evaluate the performance of its observed image. However, high spatial resolution of SAR is achieved at the cost of system resources. Therefore, super-resolution methods can be applied in SAR systems to improve their spatial resolution without system resource increases. In this paper, we propose a new residual network-based structure for super-resolution of SAR images. The proposed method adopts the structure of global residuals and adds several convolutional layers before and after the residual module to take into account the depth and width of the network. The simulation results show that the proposed method is effective as the visual effect and data evaluation.

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.

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.

The sparse iterative covariance estimation (SPICE) algorithm is promising for hyperparameter-free sparse source location for time-division-multiplexing multiple-input multiple-output (TDM-MIMO) radar systems, with well-documented merits in resolution enhancement and sidelobe suppression. Regrettably, the method typically requires a large number of iterations to converge, each requiring high-dimensional matrix operations, rendering the existing batch SPICE method impractical and expensive to implement in hardware when dealing with massive TDM-MIMO observations. In order to enable real-time processing, this paper presents a sub-aperture-recursive (SAR) SPICE method, allowing for recursively refining the location parameters for each received (RX) block observation that becomes available sequentially in time. The proposed method not only offers the same benefits as the batch SPICE method, but also allows for a computationally efficient online processing, without the need for high-dimensional matrix operations, notably reducing the required hardware resources as well as processing time. We further present a high-throughput architecture for the resulting method on a XCZU15EG-FFVB1156 field-programmable gate array (FPGA). In combination with simulation results, we demonstrate the effectiveness through experimental data measured by a cascaded MIMO radar system with 12 transmit (Tx) and 16 Rx antennas, demonstrating that the computational time of resolving closely spaced sources on 256 predefined grid points can be processed in merely 12 ms.

Recently, the iterative adaptive approach (IAA) method has been adopted to allow for high-resolution direction of arrival (DOA) estimation of MIMO radar. In this paper, the computational complexity caused by traditional rough convergence criterion is reduced by following a selective-coordinate iterative strategy. First, we analyze the iterative termination criterion of the current IAA. Then, considering a novel criterion that defined by the absolute value of the adjacent iterative points of each coordinate, those coordinates that are not converged can be selectively iterated. In this way, unnecessary iterative calculations can be greatly reduced. Finally, the complexity of IAA and the proposed method are compared and analyzed in detail. Simulation and measured data illustrate that the proposed method offers a computational complexity reduction without loss of performance.

Receiving signals sequentially through multiple channels in azimuth, radar has the potential of forward looking high-resolution imaging. However, due to the limitation of platform size, its azimuth resolution is poor. In this paper, by considering the effect of platform motion and geometric distortion, a modified iterative adaptive algorithm(IAA) method is proposed to realize multichannel radar forward-looking superresolution imaging. Simulation results are illustrated to verify the effectiveness of the method.

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.

The generalized sparse iterative covariance-based estimation ($q$-SPICE) 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 letter, we strive to alleviate this problem, deriving a beam-updating$q$-SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online$q$-SPICE implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current online$q$-SPICE. Our experimental assessment, conducted using both simulated and real data, demonstrates the advantage of the beam-updating$q$-SPICE method in the task of sparse reconstruction for scanning radar.

Superresolution methods can be applied to real aperture radar (RAR) to improve its angular resolution by solving an inverse problem. However, traditional superresolution methods are achieved after batch data collection, which requires extensive operational complexity and storage space. To solve this problem for RAR, an online detect-before-reconstruct (DBR) framework is proposed in this article based on the sparse property of targets. First, along the range direction, each sample of the echo data is detected to reduce the computational complexity by reducing the dimension of the effective data. Second, along the azimuth direction, a data-adaptive online processing structure is proposed to reduce the storage requirement for the angular superresolution problem. Finally, within the online processing structure, a target data-adaptive updating strategy is proposed to reduce the number of iterations for each target grid. The online DBR-based framework can effectively reduce the operational complexity caused by the noise values of the echo data. Based on the proposed online processing structure, the storage requirement and the operational complexity of the angular superresolution for an RAR system can be greatly reduced without significant reconstruction performance loss. The results of simulations and experimental data verify the proposed framework.

Angular resolution of real aperture radar (RAR) can be improved using deconvolution methods to achieve enhanced target information based on the convolution relationship between target scatterings and an antenna pattern. However, depending on the wide scanning scope and dense sampling angular interval, the computational complexity of the deconvolution methods will drastically increase as the dimension of azimuthal data increases. In this paper, to efficiently improve the angular resolution of RAR, a generalized adaptive asymptotic minimum variance (GAAMV) estimator that relies on a normalized projection array (NPA) model is proposed. On the one hand, the traditional convolution model of RAR is transformed into an NPA model to compress the data dimension. The proposed NPA model can normalize the signal model to make it independent of the sampling parameters. On the other hand, based on the NPA model, a GAAMV estimator is proposed to efficiently reconstruct the targets by adaptively updating each grid. Moreover, the penalty parameter is extended as a generalized case to improve its adaptability to different scenes. Based on the proposed model and method, the computational complexity can be decreased, especially for high-dimensional azimuthal data. Simulations and experimental data verify the proposed model and method.

Marine radar plays a significant role in ship navigation. However, when contending with interference among cosailing navigation radars, the echo data may be unintentionally corrupted, and it becomes challenging to obtain high-quality imagery using current radar imaging methods. To overcome this problem, an efficient anti-interference imaging framework is presented in this article based on the theory of nonuniform sampling. First, a beam-recursive anti-interference method based on the signal-to-interference-plus-noise ratio (SINR) estimation is proposed to compensate for the shortcoming of the traditional interference rejection method. Second, a nonuniform sampling model is established to well model the echo data with missing samples, which facilitates reconstructing the marine radar imagery from the missing echo data. Finally, a fast super-resolution method based on the dimension-reduction iterative adaptive approach (DRIAA) is proposed to reconstruct the distribution of sea-surface targets at a much lower computational complexity. Simulated and experimental results demonstrate that our anti-interference imaging framework can provide radar imagery with higher quality and lower computational complexity than the existing radar imaging methods in the presence of unintentional interference.

Cross-resolution enhancement for airborne high-squint radar (AHSR) imagery is mathematically equivalent to the ill-conditioned problem of inverse-scattering reconstruction. Although a variety of inversion methods with regularization can be introduced to advance the field of AHSR imagery, they turn out to be computationally intensive when extended to 2-D (range and cross-range dimension) image formulation due to the range-by-range calculation for the space-variant inversion operators over the full range swath. To tackle the problem of efficiency, this article presents a low-complexity inverse-scattering strategy. Our underlying idea is to equalize the space-variant Doppler centroid embedded in an inversion operator for a reference range cell using Doppler centroid compensation. With the proposed strategy, the necessary computational complexity required for 2-D AHSR inverse-scattering reconstruction can be significantly reduced by requiring only the calculation of the inversion operator, independently of the number of range cells. Our experimental assessment, conducted using both the simulation and real data, demonstrates that our proposed inverse-scattering strategy offers preferable computational reduction in the task of inverse-scattering reconstruction for 2-D AHSR imagery without resolution loss.

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.

Airborne radar high-squint looking imaging is an important research for remote sensing. The traditional Doppler beam sharpening based on fast Fourier transform (FFT) has good real-time performance but low cross-range resolution. Many super-resolution methods have been proposed to enhance the cross-range resolution for airborne radar. However, these methods generally adopt the batch processing mode with high computational complexity and high memory usage, which lead to poor real-time performance. This paper proposes an online super-resolution imaging approach for airborne scanning radar based on sliding window recursive least square (SWRLS) algorithm. The current scattering estimation can be derived recursively through downdating and updating. The proposed method effectively improves the cross-range resolution as well as the real-time performance and memory occupancy, which is beneficial to high-quint continuous realtime imaging for airborne radar. Simulation results are given to demonstrate the effectiveness of the proposed method.

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.

The echo received by ground-to-air radar is a range-azimuth-pitch three-dimensional data. After pulse compression, the data of each range unit can be regarded as an azimuth-pitch two-dimensional (2D) echo. The resolution of azimuth and pitch is limited to antenna aperture. In this paper, the well-known Wiener filtering, Richardson-Lucy (RL) and total variation (TV) methods are introduced to simultaneously improve the azimuth-pitch resolution of ground-to-air radar. We first analyze the received signal of ground-to-air-radar, and model the echo of each range unit as a 2D convolution of target reflectivity distribution and azimuth-pitch antenna pattern. Then we deduce the Wiener filter, RL and TV methods in detail, and theoretically realize the super-resolution imaging of the azimuth and pitch. Finally, the super-resolution performance of different methods is verified by simulation.

Iterative adaptive approach (IAA) is an effective super-resolution method to improve the resolution of airborne forward-looking radar imaging. Regretfully, the noise sensitivity caused by the non-full rank of matrix lead to the poor performance under low signal-to-noise ratio condition in the forward-looking imaging process. In response to this problem, a regularized IAA method (RIAA) based on singular value decomposition is proposed in this paper which utilizes singular value theory to decompose the autocorrelation matrix in the iteration which is applied to suppress the noise amplification and keep the main information of targets. Compared with conventional IAA method, the proposed method enjoys a preferable noise suppression performance without image quality degradation. Simulations are given to verify the performance gain.

Multistatic synthetic aperture radar (SAR) can adopt flexible topology structures to accomplish different missions. When we aim to coherently fuse multiple measurements of receivers, the topology structure of multi static SAR is the key to affect the imaging quality. In this paper, a topology design method based on wavenumber spectrum generation is proposed. The wavenumber spectrum distribution forms the dependency relationship between the imaging quality and topology structures. Based on the analysis of the kernel wavenumber spectrum distribution, the wavenumber spectrum generation is proposed to improve the spatial resolution. Using the generated wavenumber spectrum, the topology structure can be designed accurately. The proposed method effectively enhances the imaging resolution of multi static SAR at a low time cost. Simulation results verify the validity of the proposed method.

Radar antenna can work in scanning mode to obtain a wide region observation. However, for the targets located at the scene edge, the targets are only swept by less than half of the radar beam. Therefore, the scene edge targets are recovered distortedly using the conventional angular super-resolution methods. To keep the performance of recovered targets in the full scene, in this paper, a data extrapolation-based parallel iterative adaptive approach (PIAA) is proposed. First, we analyze the cause of scene edge target distortion. Then, the echo data is extrapolated by half of the radar beam to compensate the unobserved data. Last, a parallel iterative adaptive approach is proposed to recover the targets efficiently. Simulation data is applied to verify the proposed method.

Synthetic Aperture Radar (SAR) is widely used in environmental monitoring and disaster early warning due to its high resolution imaging performance. A distributed radar system can be established by mounting radars on multiple unmanned aerial vehicle (UAV) platforms. Distributed radar utilizes multiple transmitters distributed in different spatial positions, flying along a certain planned path and enable multiple transmitters to obtain as large an aperture as possible in a certain time. In this paper, an intelligent optimal path planning method for distributed radar short-time aperture synthesis is proposed, which can deal with terrain obstacles and line-of-sight occlusion in UAV flight path and achieve the goal of maximum aperture accumulation in a specific time. Simulation results verified the effectiveness of the UAV intelligent optimal path planning method.

Sparse regularization method has been widely used to realize super-resolution imaging in radar forward-looking imaging. However, most of existed methods directly minimize a nondifferentiable L1 regularization problem. In this paper, a Majorize-Minimization (MM) based super-resolution method is proposed to realize super-resolution for radar forward-looking imaging. According to MM principle, the proposed method converts the non-differentiable L1 regularization problem into a differentiable L2 regularization problem, and the real target distribution is obtained by solving the L2 regularization problem. Due to the introduction of the sparse prior, the proposed method can better improve the azimuth resolution of radar forward-looking imaging. In addition, the application of MM principle makes the non-differentiable L1 regularization easier to be solved. Finally, the superior performance of the proposed method is verified by simulation.

Total variation (TV) method has been utilized to realize super-resolution and preserve contour information of target in radar forward-looking imaging. However, its real-time ability is restricted to matrix inversion. In this paper, a fast TV (FTV) superresolution method is proposed to improve the real-time superresolution ability of traditional TV method. The proposed FTV method utilizes the low displacement rank features of Toplitz matrix and realizes fast matrix inversion by Gohberg-Semencul (GS) representation. It not only effectively improves the azimuth resolution and preserve the contour information of target, but also reduced the computational complexity of traditional TV method to improve its real-time superresolution ability. The superior performance of the proposed FTV method is verified by simulation and measured data processing.

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.

In this paper, an accelerated maximum a posteriori (AMAP) algorithm is proposed to realize fast and effective super resolution imaging of real beam scanning radar. The main idea of this algorithm is to construct a prediction vector based on the first and the second order of difference information before iteration. By using Taylor expansion series and second-order vector extrapolation technique, it aims to enhance the convergence speed of maximum a posteriori algorithm. Finally, the proposed algorithm is verified by simulations.

This paper proposes a parking space information monitoring system by millimeter wave synthetic aperture radar (SAR) based on unmanned aerial vehicle (UAV). Parking space information that people are concerned about includes vacant parking place, parking place occupied by obstacles and place parked by vehicles. Specially, the free parking space detection is an important module for the parking guidance system (PGS) that can help drivers to find parking space efficiently. In this system, we obtain high resolution SAR images of parking lots at first. Then, in order to define the free parking space, Maximally Stable Extremal Region (MSER) method is exploited to leach the candidate regions occupied by vehicles from millimeter wave SAR images. Next, the system utilize visual saliency detection method to extract obstacles from the non-parked parking space acquired by pre-detection. Ultimately, the three types of information have been determined, including vacant parking space, parking space occupied by obstacles and the parked place. Experimental results prove that the integrated scheme performs well in parking information determination.

Scene surveillance radar, which generates radar stochastic radiation field with time and space to obtain more observation information, plays a significant role in disaster monitoring and environmental security. To explore its three-dimensional (3-D) imaging capabilities, in this paper, we propose an echo rearrangement super-resolution imaging method to achieve 3D high resolution imaging for SRR. Because the echo of SRR is uncorrelated along sampling time, we adjust the conventional intrapulse frequency hopping to interpulse frequency hopping. In this way, the proposed method can improve the imaging resolution by echo rearrangement utilizing the noncorrelation with time of stochastic radiation field. The 3-D image provides the scene reflectivity estimation along polar coordinate system including pitch, azimuth and space distance. Simulation results are given to illustrate the performance of the proposed method.

Airborne bistatic (and multistatic) radar, which utilizes the spatial diversity of radar platforms to achieve high-resolution imaging, plays a significant role for the next generation radar. Based on the distribution structure of radar platforms, we can deduce the spatial spectrum to reconstruct the targets. However, the processing efficiency of spatial spectrum is different because the echo data can be projected into different shapes in spatial spectrum region. In this paper, an efficient imaging method based on spatial spectrum projection algorithm (SS-PA) for bistatic radar is proposed. First, the spatial spectrum distribution is illustrated based on the system structure. Then, the minimum external rectangular grids are depicted when the spatial spectrum is projected into different directions. Finally, the most efficient imaging view is obtained according to the ratio of spatial spectrum distribution to processing area. The proposed method can provide the most efficient imaging view for bistatic radar, which reduces computational complexity for the system implementation. Simulation result verifies the proposed method.

Finding a suitable parking position often leads to much traffic pressure and time consumption in a busy parking lot. An auxiliary parking method based on automotive millimeter wave SAR is proposed in this paper. Firstly, Maximally Stable Extremal Region (MSER) method is utilized to extract the candidate regions occupied by parked vehicles from the millimeter wave SAR images. Then, in order to eliminate the false alarm candidate regions, we employ the morphological filter and utilize the centroid position to further refine the candidate regions. Thirdly, the difference in width-to-height ratio of the candidate regions is exploited to distinguish the parking directions of the cars. After that, the available parking spaces are located according to the parking direction. Finally, further remove the spaces occupied by obstacles, and plan reasonable parking routes. Experimental results based on measured data show that the proposed method has outstanding detection and parking route planning performance in different scenes.

High resolution is always the most concerned issue of radar imaging. Traditional radar systems, which obtain echo data using single platform, can achieve limited imaging resolution in a specific view angle. Distributed radar system, which expands multi-platform in space to obtain high imaging resolution by forming a large aperture, is a novel and hot research point. Matched filter, such as inverse fast Fourier transform (IFFT), is a conventional method to deal with distributed radar imaging. However, the method relies strictly on geometric configuration. In this paper, an iterative adaptive approach (IAA) based method is proposed to solve the problem of configuration adaptability. It can maintain the performance of matrix during the iteration. Then, the distributed radar system can keep high resolution in different geometric configurations. Simulation results verified the excellent performance of the proposed IAA-based imaging method.

Distributed radar imaging utilizes expanded array elements in space to form a large aperture and obtain high imaging resolution. A great number of array elements are required in traditional distributed radar system which uses multiple platforms. The distribution of spatial spectrum is affected by the number and the signal form of array elements. In this research, to improve the utilization efficiency of platform resources, a resource allocation optimization method based on Unmanned Aerial Vehicle(UAV) is proposed. It chooses the optimized bandwidth and sampling frequency points of array elements by analyzing the relationship between spatial spectrum and imaging performance. This method can use a small number of UAVs to maintain high imaging resolution. Simulation results verified the effectiveness of the resource allocation optimization method for image quality improvement.

Conventional synthetic aperture radar (SAR) reconstructs the illuminated scene via fast Fourier transform (FFT), which results in high sidelobe level, and poor cross-range resolution due to the finite synthetic aperture length. In this paper, we formulate a sparse reconstruction method for SAR imaging based on the covariance fitting criterion, which assumes that only a few strong scatters exist in the whole scene. The method is able to fully control over the sparsity level and reconstruct the scenario in an adaptive manner. Experimental results with real SAR data show the better performance of our method compared with the conventional methods in terms of resolution improvement and sidelobe suppression.

The clear contour is required when realize azimuth superresolution of forward-looking radar imaging in many applications. Traditional deconvolution methods achieve the azimuth superresolution but are limited in contour recovery. Although the total variation (TV) method can be used to keep the contour information, it's sensitive to noise because of derivation. In this paper, we propose an improved total variation (ITV) method to realize azimuth superresolution of forward-looking radar imaging and recover the contour information. Firstly, the TV norm and L2norm are combined as the penalties under regularization framework. Then the regularization problem is solved by split Bregman algorithm. The proposed ITV method achieves higher azimuth resolution and better contour recovery performance than traditional methods, and the super performance is verified by simulations lastly.

Regularization technology can be utilized to improve the azimuth resolution for forward-looking scanning radar. In this paper, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging. This method can achieve azimuth superresolution and noise suppression. Simulations are given to verify the effectiveness of the method.

Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods.

Angular resolution of scanning radar is constrained by the size of antenna aperture. Such coarse resolution can not satisfy the applications of microwave remote sensing that require high resolution. Iterative adaptive approach (IAA) is a recently introduced method for scanning radar angular super-resolution, which could notably improve the angular resolution and suppress the noise amplification. In this paper, we further this development, by presenting a beam-recursive I-AA, allowing for adjusting the regularization parameter adaptively and dynamically for varying scenario. Such implementation could effectively eliminate the artifacts on background when applying the batch IAA to resolve closely spaced strong targets. Moreover, the technique offers a promising potential that deserves further attention on computationally efficient implementation and real-time imaging along antenna beam scanning. Simulations are provided to validate the effectiveness of the proposed approach.

Airborne radar forward-looking imaging is of great significance in many remote sensing applications. However, the existing synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) imaging techniques are incapable of forward-looking imaging. The real aperture radar (RAR) using a scanning antenna can provide forward-looking images, but suffers from coarse azimuth resolution. In this paper, we extend the iterative adaptive approach (IAA) to forward-looking super-resolution imaging. Different from the conventional forward-looking convolution model, both the Doppler phase and antenna convolution are considered in the new model, allowing for more accurate reconstruction of the forward-looking imaging scenario when applying the IAA. Simulation results demonstrate that the IAA-based super-resolution imaging can overcome the deficiencies of the SAR and DBS techniques in forward-looking imaging direction.

The accuracy of Doppler centroid estimation affects the target location and the scene mosaic in the Doppler beam sharpening imaging. Though it can be measured by different sensor instruments of servo, attitude, inertial, the inaccurate measurements decrease the imaging performance. In this paper, a Doppler centroid estimation method based on the morphological edge detection is proposed to obtain the Doppler centroid from the received data with loose-measured parameters. The Doppler frequency in the forward-looking region is symmetrical, but the target carries the highest Doppler frequency. The characteristic can be vividly reflected in range Doppler domain with morphological edge. The Doppler centroid can be estimated via the detection of the edge. The results of centroid estimation and Doppler beam sharpening imaging are given to verify the performance of the proposed method.

This paper proposes a space variant-based maximum a posteriori (MAP) angular super-resolution algorithm for highspeed moving real-beam scanning radar. Firstly, the aberration model of the antenna modulation function is established through analyzing the relationship between the scanning angle and the sight angle. Afterwards, an efficient piecewise constant model is formulated for the sake of reducing the computational complexity and restoring cost. Finally, the space variant-based MAP algorithm is derived based on the aberrant model. Simulation experiments demonstrate that the proposed method can improve the super-resolution performance of the high-speed platforms more efficiently than the traditional MAP method.

It is great significant to achieve clear outline reconstruction for radar forward-looking imaging. In this paper, we apply the total variation (TV) function as the regularization term operator to obtain the forward-looking imaging with clear outline. Firstly, we establish the deconvolution model, by which the forward-looking super-resolution imaging problem is converted into inverse problem. Then, taking the TV function as regularization constraint term, we construct the objective function to solve the inverse problem. Finally, we obtain the minimum of the objective function, by which we can achieve radar forward-looking super-resolution imaging with clear outline. Simulations verify effectiveness of the proposed method in reconstructing the outline of targets.

In this paper, the Cramér-Rao Bound (CRB) for scanning radar sensing is investigated, providing an algorithm-independent bound on the angular estimation error. Based on the deterministic signal model, we first derive a numerical CRB for unknown real signal parameters. Then, the approximate closed-form expression of CRBs are further provided for the single target case. Meanwhile, the potential estimation error of various classical super-resolution sensing methods are quantitatively investigated in this paper, and compared with the presented CRB.

In forward-looking scanning radar imaging, the azimuth resolution can be improved by adding the sparse constraint. However, the azimuth resolution is limited with noise influence by traditional sparse regularization methods. In this paper, we propose a Bayesian super-resolution method that solves the L1regularization problem using the split Bregman algorithm. This method decouples L1and L2norms for the independence of them to reduce the computational complexity. The simulations verify that the proposed algorithm provides a better resolution and de-noising ability compare with conventional methods.

Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals. The technique is formed by initially introducing a coarse grid dictionary constructed from integrating elements, spanning bands of the considered parameter space. After forming estimates of the initially activated bands, these are retained and refined, whereas nonactivated bands are discarded from the further optimization, resulting in a smaller and zoomed dictionary with a finer grid. Implementing this scheme allows for reliable sparse signal reconstruction, at a much lower computational cost as compared to directly forming a larger dictionary spanning the whole parameter space. Simulation and real data processing results demonstrate that the proposed wideband estimator offers significant computational savings, without noticeable loss of performance.

High-resolution scanning radar mapping of the surface is an effective tool for addressing concerns in local environmental and social investigation fields. Regrettably, the azimuth resolution of a scanning radar is constrained by the antenna beamwidth. Multiple super-resolution approaches have been applied to the scanning radar to enhance the azimuth resolution, but they suffer from limited resolution improvement. In this paper, a methodology to derive surface estimates from the scanning radar at an improved azimuth resolution is proposed. We first consider the truncated spectrum by discarding the unreliable frequencies to suppress the noise amplification. Then, based on the iterative adaptive approach (IAA), a novel inverse filtering method is formulated to obtain lower sidelobes and a higher resolution. Finally, by taking advantage of the Fourier property of the steering matrix and the Toeplitz structure of the covariance matrix, we exploit the Gohberg-Semencul representation and the data-dependent trigonometric polynomials to derive a fast IAA (FIAA)-based inverse filtering to mitigate the computational burden. Simulation results and real data processing demonstrate that the proposed FIAA-based inverse filtering outperforms the existing super-resolution approaches in resolution improvement and results in a higher computational efficiency.

The low angular resolution of scanning radar limits the application in the forward-looking imaging field. This paper proposes the mixed method of truncated singular value decomposition (TSVD) with regularization l1norm to achieve the angular super-resolution. First, the TSVD technique is applied to suppress the noise amplification and keep the main information of targets. Then the angular super-resolution is obtained by analyzing the main information in the form of regularization l1norm. The mixed method has the better performance, comparing with the TSVD method and regularization method. The performance has the lower sensitivity to the regularization parameter. Simulations and experimental results verify the efficacy of this method.

High cross-range resolution of forward-looking region is the key problem of radar imaging. This paper presents a multi-beam system to extend high resolution imaging domain based on the digital beamforming (DBF) technique. The Doppler bandwidth is increased in forward-looking direction while the symmetrical Doppler domain could be significant reduced. Then matched filter technique is employed to deal with the composed received echo. The feasibility and effectivity of this strategy were verified by simulation results.

Target recognition based on forward-looking imaging has many potential applications. However, the conventional algorithms fail to locate targets accurately due to the low resolution of forward-looking radar images. Meanwhile, the conventional algorithms always suffer from high computational complexity and cannot satisfy the requirement of real-time processing for high-speed platform. This paper proposes a novel target recognition method based on morphological and spatial features. The algorithm comprises of initial matching and dual verification algorithms based on image gray scale and a priori position information. It is demonstrated that the proposed algorithm can work well for the forward-looking radar images with coarse resolution and enjoy higher computational efficiency. Simulation and real data processing validates the superior performance of the proposed algorithm.

Recently, the iterative adaptive approach (IAA) was adopted to allow for the estimation of high-resolution scanning radar images. In this letter, we further develop this approach by introducing a range-recursive IAA (IAA-RR) formulation allowing for a computationally efficient updating of the resulting estimates along range. Besides exploiting the rich matrix structure to mitigate the computational complexity for each iteration, the correlation between adjacent range cells is exploited to accelerate the convergence of the IAA iterations. When an additional range measurement becomes available, further acceleration is available by exploiting the estimates already formed for the adjacent range cells. Compared with the existing fast IAA implementation, the proposed IAA-RR is shown to offer significant computational savings, without noticeable loss in performance. Numerical results illustrate the superior performance of the proposed IAA-RR algorithm.

In this letter, an improved regularized iterative adaptive approach (IAA) is proposed for scanning radar angular superresolution. Because the IAA requires matrix inversion, the increasing condition number of the covariance matrix leads to the ill-posed problem of the IAA. Based on this reality, the diagonal loading method is introduced to solve the ill-posed problem. Because the loading value controls the tradeoff between the azimuth resolution and noise amplification, we use the radiometer uncertainty principle to determine the optimum loading value. When compared with the existing angular superresolution approaches, the proposed regularized IAA is shown to provide significant resolution improvement. Numerical results illustrate the superior performance of the proposed regularized IAA.

The angular superresolution is of great significance for scanning radar in forward-looking imaging. There are many techniques documented in literature to enhance the angular resolution, of which deconvolution method and power spectral density(PSD)methods are favored and attain many interests. In this paper, we focus on analyzing the advantages and challenges of PSD methods in comparison with the deconvolution method. Firstly, three typical PSD estimation approaches are introduced, followed with the comparison with deconvo-lution method that summarizes the advantages and challenges of PSD methods in theory. Simulations are provided in terms of coherence and number of snapshots, which presents the performance of different PSD methods and Lucy-Richardson deconvolution method, better demonstrating the advantages and challenges of PSD methods.

A scheme of fast standard Capon beamformer (SCB) is proposed for scanning radar angular superresolution aiming at the computation burden caused by large swath mapping in azimuth. First, using the similar block tridiagonal property between the covariance matrix and Schur complement of its submatrix, the fast matrix inverse works in an improved divide and conquer (D&C) approach by recursively breaking down the problem of fast inverse into the same sub-problems. Secondly, based on the circulant structure of steering matrix, the SCB estimate of every assumed grids are rewritten by linear convolution. The resulting algorithm is shown to reduce the necessary computational load with one power without noticeable loss of performance.

Most of ever proposed scanning radar angular superresolution algorithms are iterative, but the optimum iterations are difficult to determine. Since the performance of them is related to the signal to-noise ratio (SNR), an accurate SNR estimation would be of great significance to provide reference for assistance of adaptive iteration termination condition analysis. In this paper, a data-aided (DA) SNR estimation approach is developed. This scheme first utilizes the known antenna pattern and identity matrix to construct the steering matrix of signals and noise. Then we introduce the iterative adaptive approach (IAA) to estimate SNR. Simulation validates that this scheme, termed as IAA-SNR can present an adaptive and effective SNR estimation for scanning radar.

This paper present a novel superresolution algorithm for real-beam scanning radar based on the iterative adaptive strategy. Firstly, we establish the objective function based on the minimum mean-square error (MMSE) criterion, then we build the iterative expression by update the covariance matrix. This algorithm has better superresolution performance than traditional deconvolution method. Simulation results verified the analysis before.

This paper present an efficient matrix inverse algorithm of the recent iterative adaptive approach (IAA) in the application of real beam superresolution (RBS). Based on the inherently band structure of the covariance matrix, the computational complexity of inverse can be reduced by avoiding the computation of zero elements. To achieve this, the divide and conquer (D&C) method will be introduced to fast inverse the covariance matrix. Numerical simulations illustrate the efficiency of the proposed algorithm.

The angular resolution is limited by the aperture size in real beam radar. To improve the angular resolution, some deconvolution algorithms have been proposed. However, it becomes challenging to estimate the amplitude and location parameters of illuminated targets as signal-to-noise ratio decreases. Through our research, we analyze the similarities of mathematic model and physical principle between array processing and real beam imaging, then in this paper we will show how the iterative adaptive approach (IAA), a spectral estimation method, can be applied to real beam radar for superresolution. The simulation results of real beam radar will be presented to demonstrate the performance of IAA.