Sparse representation, and reconstruction in SAR

Recently, several novel approaches to radar signal processing have been introduced which allow the radar to perform signal detection and parameter estimation from much fewer measurements than that required by Nyquist sampling. These reduced-rate radars exploit the fact that the target scene is sparse in time, frequency or other domains. These concepts have also been applied to imaging systems such as synthetic aperture radar (SAR), inverse SAR (ISAR), and interferometric SAR (InSAR). The SAR imaging data are not naturally sparse in the range-time domain. However, they are often sparse in other domains, such as wavelets. This lecture introduces the audience to reduced-rate sampling methods with a focus on SAR, ISAR, InSAR, and InISAR systems. It will provide an overview of detailed signal processing theory to apply reduced-rate sampling to conventional radars and follow by its recent applications to imaging radars.