A Fast Sparse Azimuth Super-Resolution Imaging Method of Real Aperture Radar Based on Iterative Reweighted Least Squares With Linear Sketching
Xingyu Tuo, Yin Zhang, Yulin Huang, Jianyu Yang
Abstract
It is greatly significant to achieve radar forward-looking region imaging. Due to the limitation of phase ambiguity and small Doppler gradient in forward-looking region, synthetic aperture radar and Doppler beam sharpening cannot work for forward-looking imaging, while real aperture radar (RAR) has arbitrary imaging geometry. Nevertheless, restricted by the antenna aperture, azimuth resolution of RAR is coarse, super-resolution technology is required to improve its azimuth resolution. Exploiting the sparse prior information of the target, the super-resolution problem can be transformed into an <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> norm minimization problem mathematically. Iterative reweighted algorithm can effectively solve the <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> norm minimization problem by replacing <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> norm with reweighted <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula> norm and computing the weight in each iteration. However, it suffers from a large computational load due to the repeated multiplications and inversions of large matrices. In this article, a fast azimuth super-resolution imaging method of RAR based on iterative reweighted least squares (IRLS) with linear sketching (LS) was proposed to achieve fast super-resolution imaging of RAR. The LS theory is employed to compress echo matrix and antenna measurement matrix into much smaller matrices via multiplying them by an embedded matrix. Then, the IRLS solver was utilized to address the reconstructed objective function. Much of the expensive computation can then be performed on the smaller matrices, thereby accelerating the algorithm. Simulations and experimental data prove that the proposed algorithm can offer a time complexity reduction without loss of imaging performance.