SINR improvement in airborne/spaceborne STAP radars using a priori platform knowledge

Abstract

It is shown that partial a priori information about the airborne/spacebased clutter covariance matrix (CCM) can be used effectively to enhance significantly the convergence performance of a block-processed space/time adaptive processor (STAP) in a clutter and jamming environment. The partial knowledge of the CCM is based upon the simplified general clutter model (GCM) which has been developed by the airborne radar community. A priori knowledge of parameters which should be readily measurable (but not necessarily accurate) by the radar platform associated with this model is assumed. The GCM generates an assumed CCM. The assumed CCM along with exact knowledge of the thermal noise covariance matrix is used to form a maximum likelihood estimate (MLE) of the unknown interference covariance matrix which is used by the STAP. The new algorithm that employs the a priori clutter and thermal noise covariance information is evaluated using two clutter models: (1) a mismatched GCM and (2) the high-fidelity Research Laboratory Space Time Adaptive Processing (RLSTAP) clutter model. For both clutter models, the new algorithm performed significantly better than the sample matrix inversion (SMI) and fast maximum likelihood (FML) STAP algorithms, the latter of which uses only information about the thermal noise covariance matrix.