Sparse reconstruction techniques applied to ISAR images, based on compressed sensing

In this work we study the feasibility of sparse reconstruction techniques applied to compressed air-to-air ISAR images. The main purpose is to use compressed sensing algorithms to reduce the amount of information needed to manage the acquired hi-res images. In more details, we will apply these techniques to high-resolution Inverse SAR (ISAR) images obtained from a proprietary Selex-Galileo X-band radar. Performance results will be shown by comparing the reconstructed images with the original hi-res ones, demonstrating how the part of information relevant to target aircraft identification, is preserved despite the high compression ratio.