Robust STAP in non-Gaussian clutter via infinity-norm snapshot-normalization

In this paper, a new method is proposed for the problem of space-time adaptive processing (STAP) in non-Gaussian clutter. The proposed method normalizes each space-time snapshot vector by its infinity-norm, so that the second-order statistics-based STAP algorithms can become applicable to a wide class of non-Gaussian heavy-tailed clutter environments. Unlike the well-known fractional lower-order moment (FLOM)-based methods, the proposed method does not require any priori knowledge of the non-Gaussian clutterpsilas statistics, and hence, it is ldquoblindrdquo. Numerical results show that the proposed method outperforms the customary second-order statistics-based STAP algorithms and the FLOM-based STAP algorithms in the presence of non-Gaussian clutter.