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 $L_1$ norm minimization problem mathematically. Iterative reweighted algorithm can effectively solve the $L_1$ norm minimization problem by replacing $L_1$ norm with reweighted $L_2$ 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.
Highlights
R EAL aperture radar (RAR) has the advantage of an arbitrary imaging geometry and has been of considerable interest in applications where traditional synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) are limited, such as airborne forward-looking ground mapping and aircraft forward-looking area navigation [1]–[4].In order to obtain a high resolution two-dimensional image, it is necessary to simultaneously improve range resolution and azimuth resolution
Least-squares solutions tend to be quite sensitive to data with large errors, iterative reweighted least squares (IRLS) method cannot be directly used to L1 norm issue because of ill-posed antenna measurement matrix in RAR super-resolution imaging
Simulation and experiment result are compared with conventional super-resolution imaging methods, including truncated singular value decomposition (TSVD) method, iterative adaptive approach (IAA) and l1 sparse regularization method solved by iterative reweighted norm, which is called l1-IRN method for convenience
Summary
R EAL aperture radar (RAR) has the advantage of an arbitrary imaging geometry and has been of considerable interest in applications where traditional synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) are limited, such as airborne forward-looking ground mapping and aircraft forward-looking area navigation [1]–[4]. The iterative adaptive approach (IAA), which was presented in the application of passive array processing [13]–[15], was applied to azimuth super-resolution [16] Since it requires the computation of the covariance matrix R, R−1 and weighted least squares (WLS) estimate for each sampled grid, the improved performance of IAA comes at the cost of notably high computational complexity. Least-squares solutions tend to be quite sensitive to data with large errors, IRLS method cannot be directly used to L1 norm issue because of ill-posed antenna measurement matrix in RAR super-resolution imaging.
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