Low-Rank Approximation-Based Super-Resolution Imaging for Airborne Forward-Looking Radar

Iterative adaptive approach (IAA), based on the weighted least squares estimation (WLS) criterion, can effectively improve the azimuth resolution of airborne forward-looking radar imagery. Regretfully, the brute force IAA requires a large number of inversions of high-dimensional autocorrelation matrix, resulting in notably high computational complexity. In this paper, a low-rank iterative adaptive approach (LR-IAA) is proposed to solve this problem. Our underlying idea is to construct a low-rank Gaussian matrix to randomly sample the original echo model, and restore the original scene through spectral estimation method in a low-dimensional linear space. Compared with bruteforce implementation, the proposed LR-IAA enjoys a preferable computational efficiency without performance degradation. Simulations are given to verify the performance gain.