A novel Knowledge-Aided STAP using azimuth sub-look SAR image

This paper proposes a novel Knowledge-Aided Space-Time Adaptive Processing (KA-STAP) approach which uses azimuth sub-look Synthetic Aperture Radar (SAR) images to improve the interference covariance matrix estimation. The proposed approach extracts the azimuth scattering information of ground clutter scatters from the azimuth sub-look SAR images and improves the interference covariance matrix estimation by using the azimuth scattering characteristics of heterogeneous clutter scatters. Our proposed approach pays attention to the varied azimuth scattering of clutter scatters, which is very important in Scan Ground Moving Target Indication (Scan-GMTI) since scatters are illuminated from different azimuth look angles at different scanning angles. The performance of the proposed approach is demonstrated using simulated data.