A Bayesian Super-Resolution Method for Forward-Looking Scanning Radar Imaging Based on Split Bregman

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

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

Sensors ◽

10.3390/s21030817 ◽

2021 ◽

Vol 21 (3) ◽

pp. 817

Author(s):

Weibo Huo ◽

Qiping Zhang ◽

Yin Zhang ◽

Yongchao Zhang ◽

Yulin Huang ◽

...

Keyword(s):

Computational Complexity ◽

Real Time ◽

Super Resolution ◽

Coefficient Matrix ◽

Resolution Method ◽

The Real ◽

L1 Regularization ◽

Regularization Problem ◽

Azimuth Resolution ◽

Forward Looking

The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an L1 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable L1 regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg-Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from O(N3) to O(N2). It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice.

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Majorize-Minimization Based Super-Resolution Method for Radar Forward-Looking Imaging

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324322 ◽

2020 ◽

Author(s):

Qiping Zhang ◽

Yin Zhang ◽

Yongchao Zhang ◽

Yulin Huang ◽

Wenchao Li ◽

...

Keyword(s):

Super Resolution ◽

Resolution Method ◽

Forward Looking

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Sparse super-resolution method based on truncated singular value decomposition strategy for radar forward-looking imaging

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.12.035021 ◽

2018 ◽

Vol 12 (03) ◽

pp. 1

Keyword(s):

Singular Value Decomposition ◽

Super Resolution ◽

Singular Value ◽

Truncated Singular Value Decomposition ◽

Resolution Method ◽

Value Decomposition ◽

Forward Looking

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A Radar Forward-Looking Super-Resolution Method Based On Singular Value Weighted Truncation

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8898704 ◽

2019 ◽

Author(s):

Xingyu Tuo ◽

Yin Zhang ◽

Deqing Mao ◽

Yao Kang ◽

Yulin Huang

Keyword(s):

Super Resolution ◽

Singular Value ◽

Resolution Method ◽

Forward Looking

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A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging

Remote Sensing ◽

10.3390/rs13142768 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2768

Author(s):

Qiping Zhang ◽

Yin Zhang ◽

Yongchao Zhang ◽

Yulin Huang ◽

Jianyu Yang

Keyword(s):

Super Resolution ◽

Radar Imaging ◽

Superior Performance ◽

L1 Norm ◽

Antenna Aperture ◽

Resolution Method ◽

Strong Point ◽

Wide Range ◽

Resolution Imaging ◽

Azimuth Resolution

Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution. However, a narrower beam means a larger antenna aperture. In practical applications, due to platform limitations, the antenna aperture is limited, resulting in a low azimuth resolution. The conventional sparse super-resolution method (SSM) has been proposed for improving the azimuth resolution of scanning radar imaging and achieving superior performance. This method uses the L1 norm to represent the sparse prior of the target and solves the L1 regularization problem to achieve super-resolution imaging under the regularization framework. The resolution of strong-point targets is improved efficiently. However, for some targets with typical shapes, the strong sparsity of the L1 norm treats them as strong-point targets, resulting in the loss of shape characteristics. Thus, we can only see the strong points in its processing results. However, in some applications that need to identify targets in detail, SSM can lead to false judgments. In this paper, a sparse denoising-based super-resolution method (SDBSM) is proposed to compensate for the deficiency of traditional SSM. The proposed SDBSM uses a sparse minimization scheme for denoising, which helps to reduce the influence of noise. Then, the super-resolution imaging is achieved by alternating iterative denoising and deconvolution. As the proposed SDBSM uses the L1 norm for denoising rather than deconvolution, the strong sparsity constraint of the L1 norm is reduced. Therefore, it can effectively preserve the shape of the target while improving the azimuth resolution. The performance of the proposed SDBSM was demonstrated via simulation and real data processing results.

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