SAR Imaging From Azimuth Missing Raw Data via Sparsity Adaptive StOMP

Abstract

Synthetic aperture radar (SAR) raw data missing occurs when the radar is interrupted for various reasons during the work. Different solutions have been proposed to this problem. In recent years, with the continuous deepening of the research on compressed sensing (CS), it has also been fully utilized in solving the problem of missing data. When using traditional greedy algorithms to recover missing data, we need to know the sparsity, but it is often unknowable in practice. This letter proposes to apply the sparsity adaptive segmented orthogonal matching pursuit (SAStOMP) algorithm to the recovery of SAR missing data. Simulation results show that the proposed method can recover missing SAR data under the condition of unknown sparsity and can adapt to a wider range of threshold parameters. It has good recovery performance for periodic and nonperiodic missing SAR raw data, thus improving SAR imaging results.