Robust STAP using reiterative censoring

Effective methods are developed for selecting sample snapshots for the training data used to compute the adaptive weights for an adaptive match filter; specifically a space/time adaptive processing (STAP) airborne radar configuration is considered. Several new robust adaptive algorithms are presented and evaluated against interference scenarios consisting of jamming, nonhomogeneous airborne clutter (generated by the RLSTAP model), internal system noise, and outliers (which could take the form of targets themselves). These algorithms use either the generalized inner product (GIP) or the adaptive power residue (APR) metrics in reiterative fashion for culling the training data.