Sparse aperture fill for SAR using super-SVA

Synthetic aperture radar (SAR) is a coherent imaging process that requires an uninterrupted collection of Nyquist-sampled signal data. Corrupted or missing data in the collected aperture produces artifacts and reduces the achievable resolution. For the case of corrupted data due to an improperly functioning system component or an intentional interference signal, simply nulling the corrupted part of the aperture will often improve the utility of the resulting image. However, significant artifacts will likely remain as a result of the gap introduced when the bad data is removed. The authors presented a technique based on the super-spatially variant apodization (super SVA) algorithm [Stankwitz and Kosek 1995] that can interpolate the collected data to fill the gap formed by the missing data. Super-SVB was originally designed to extrapolate SAR signal data; however, it can also be used to interpolate across gaps in the data. This technique can be extended to effectively build a large aperture from a number of closely spaced, but non-abutting, e.g. sparse, apertures. This ability to fill sparse apertures introduces a new potential price/performance tradeoff for the system designer with specific resolution requirements