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

Zhang, Qiping; Zhang, Yin; Mao, Deqing; Huang, Yulin; Yang, Jianyu

doi:10.1109/igarss.2018.8518359

Cited by 7 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sparse regularization is an effective method for improving the azimuth resolution because the target of interest is usually sparse in radar forward-looking imaging. In the previous work, we conducted in-depth research on sparse regularization methods [ 18 , 19 ]. Typically, the sparse regularization method requires solving an

regularization problem [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…Split Bregman algorithm (SBA), as an efficient iterative algorithm, has been widely used to solve the challenging problem in many fields, such as imaging deblurring [ 22 ], radar super-resolution imaging [ 23 ], compressed sensing [ 24 ] and so on. In [ 18 ], SBA was utilized to improve the azimuth resolution of radar forward-looking imaging. The results show that SBA has better performance than traditional methods in resolution improvement and noise suppression.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Huo

Zhang

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

regularization problem [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huo

Zhang

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Split Bregman method (SBM) proposed in [28] is a universal convex optimization algorithm for both l 1 -norm and TV-norm regularization problems. By the idea of decomposing the original problem into several subproblems worked out by Bregman Iteration (BI) [29,30], SBM has been widely utilized in the complex domain through the complex-to-real converting technique [31,32], e.g., MRI imaging [33], SAR imaging [34], forward-looking scanning radar imaging [35], SAR image super-resolution [36], and massive MIMO channel estimation [37]. However, SBM still has great potential in terms of both reconstruction performance and time cost considering the following two points: The original BI defined in the real domain may not make good use of the phase information for complex variables, which degrades the recovery accuracy; secondly, the converting technique quadruples the elements of the sensing matrix A to 2 m × 2 n , which consumes more memory and time within the iteration process.…”

Section: Introductionmentioning

confidence: 99%

A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method

Xiong

Zhao

Shi

et al. 2019

Sensors

View full text Add to dashboard Cite

The Split Bregman method (SBM), a popular and universal CS reconstruction algorithm for inverse problems with both l1-norm and TV-norm regularization, has been extensively applied in complex domains through the complex-to-real transforming technique, e.g., MRI imaging and radar. However, SBM still has great potential in complex applications due to the following two points; Bregman Iteration (BI), employed in SBM, may not make good use of the phase information for complex variables. In addition, the converting technique may consume more time. To address that, this paper presents the complex-valued Split Bregman method (CV-SBM), which theoretically generalizes the original SBM into the complex domain. The complex-valued Bregman distance (CV-BD) is first defined by replacing the corresponding regularization in the inverse problem. Then, we propose the complex-valued Bregman Iteration (CV-BI) to solve this new problem. How well-defined and the convergence of CV-BI are analyzed in detail according to the complex-valued calculation rules and optimization theory. These properties prove that CV-BI is able to solve inverse problems if the regularization is convex. Nevertheless, CV-BI needs the help of other algorithms for various kinds of regularization. To avoid the dependence on extra algorithms and simplify the iteration process simultaneously, we adopt the variable separation technique and propose CV-SBM for resolving convex inverse problems. Simulation results on complex-valued l1-norm problems illustrate the effectiveness of the proposed CV-SBM. CV-SBM exhibits remarkable superiority compared with SBM in the complex-to-real transforming technique. Specifically, in the case of large signal scale n = 512, CV-SBM yields 18.2%, 17.6%, and 26.7% lower mean square error (MSE) as well as takes 28.8%, 25.6%, and 23.6% less time cost than the original SBM in 10 dB, 15 dB, and 20 dB SNR situations, respectively.

show abstract

Sparse With Fast MM Superresolution Algorithm for Radar Forward-Looking Imaging

Zhang

Huang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

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

Cited by 7 publications

References 5 publications

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

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

A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method

Sparse With Fast MM Superresolution Algorithm for Radar Forward-Looking Imaging

Contact Info

Product

Resources

About