Two-Step Dimension Reduction Strategy for Real-Aperture Radar Fast Super-Resolution Imaging

For real-aperture radar, its azimuth resolution is much coarser than the range resolution after pulse compression, super-resolution algorithms are desired to enhance its azimuth resolution. However, the super-resolution algorithms must require enough azimuth sampling to ensure their performance. When wide scanning scope or dense azimuth sampling, the amount of data will increase significantly, which brings a large computational burden to super-resolution processing. To cover this problem, we propose a two-step dimension reduction strategy. First, by using linear sketching technology, the high-dimensional matrices are projected to the low-dimensional space, thus accelerating the matrix-matrix multiplications in super-resolution algorithms. Second, exploiting Sherman-Morrison formula, we further realized the acceleration of the matrix inversion in super-resolution algorithms. The proposed two-step acceleration strategy in our work is applicable to the existing deconvolution super-resolution algorithms, including regularization methods and Bayesian methods. It can be verified by simulation and experimental data that the proposed accelerated algorithms have advantages in computing time without losing the quality of super-resolution imaging.