High-Resolution Millimeter-Wave Ground-Based SAR Imaging via Compressed Sensing

Abstract

Compressed sensing (CS) is a technique which reconstructs an approximated full image using reduced samples. With the CS technique, the measurement time is reduced and high-resolution reconstructed data are obtained. On the other hand, the computational time is increased because the CS algorithm requires slightly more time than conventional reconstruction algorithms. Therefore, reducing the computational time is a critical issue. In this paper, an improved CS algorithm is proposed. The proposed algorithm is based on a greedy algorithm which solves CS problems using an iteration method. By reducing the number of iterations, the computational time of the proposed algorithm is reduced. The proposed CS algorithm is applied to a simple discrete Fourier transform problem and to millimeter-wave ground-based synthetic aperture radar imaging for verification.