Superresolution Inverse Synthetic Aperture Radar (ISAR) Imaging using Compressive Sampling

摘要

A method based on compressive sampling to achieve superresolution in ISAR imaging is presented. The super-resolution ISAR imaging algorithm is implemented by enforcing the sparsity constraints via random compressive sampling of the measured data. Sparsity constraint ratio (SCR) is used as a design parameter. Mutual coherence is used as a quantitative measure to determine the optimal SCR. ISAR data for full angular sector as well as different partial angular sectors are utilized in this study. Results show that significant resolution enhancement is achieved around optimal SCR of 0.2.