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Open AccessLetter

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

School of Information and Communication Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Ali Khenchaf, Adam M. Kawalec and Waldemar SUSEK
Sensors 2021, 21(3), 817;
Received: 24 November 2020 / Revised: 21 January 2021 / Accepted: 21 January 2021 / Published: 26 January 2021
(This article belongs to the Special Issue Microwave Sensors and Radar Techniques)
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. View Full-Text
Keywords: super-resolution; radar imaging; Gohberg-Semencul representation; vector extrapolation super-resolution; radar imaging; Gohberg-Semencul representation; vector extrapolation
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MDPI and ACS Style

Huo, W.; Zhang, Q.; Zhang, Y.; Zhang, Y.; Huang, Y.; Yang, J. A Superfast Super-Resolution Method for Radar Forward-Looking Imaging. Sensors 2021, 21, 817.

AMA Style

Huo W, Zhang Q, Zhang Y, Zhang Y, Huang Y, Yang J. A Superfast Super-Resolution Method for Radar Forward-Looking Imaging. Sensors. 2021; 21(3):817.

Chicago/Turabian Style

Huo, Weibo; Zhang, Qiping; Zhang, Yin; Zhang, Yongchao; Huang, Yulin; Yang, Jianyu. 2021. "A Superfast Super-Resolution Method for Radar Forward-Looking Imaging" Sensors 21, no. 3: 817.

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