Detection performance for the GMF applied to STAP data

A major problem with most standard methods working on STAP data is their lack of robustness in the presence of heterogeneous clutter background. Indeed most of them rely on the assumption that the clutter remains homogeneous over quite a large range. We apply to the STAP data, a high-resolution method called the Global Matched Filter (GMF). Since it models and identifies both the interferences (clutter and jammer(s)) and the target(s) from the data by only using the snapshot of interest, it solves the above mentioned difficulty. We describe here how to apply the GMF to the STAP data and we compare its performance to the other STAP methods by establishing the target detection probability for a constant false alarm rate.