Abstract
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 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 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable 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 to . 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.
Highlights
Radar forward-looking imaging plays an important role in precision guidance, autonomous driving, surface mapping and so on
Since the proposed superfast SBA (SFSBA) aims to improve the efficiency of the algorithm, in this subsection, we build a hardware platform based on a field programmable gate array (FPGA)
Some traditional super-resolution methods face the problem of limited resolution improvement, such as Tikhonov regularization (TREGU), truncate singular value decomposition (TSVD), RL and iterative adaptive approach (IAA)
Summary
Radar forward-looking imaging plays an important role in precision guidance, autonomous driving, surface mapping and so on. The array antenna can obtain a high-resolution image in the forward-looking region [5], but it is usually not applicable due to platform limitations. 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 [18], SBA was utilized to improve the azimuth resolution of radar forward-looking imaging. Aiming at the low azimuth resolution of radar forward-looking imaging and the high computational complexity of traditional SBA, a superfast SBA (SFSBA) is proposed in this paper. Different from traditional SBA, the proposed SFSBA firstly utilizes the Toeplitz structure of the coefficient matrix, along with the low displacement rank feature of the Toeplitz matrix and realizes fast inversion through the GS representation, reducing the computational complexity of each iteration to O( N 2 ).
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