Robust STAP algorithms using prior knowledge for airborne radar applications

Space-time adaptive processing (STAP) schemes have shown promise for airbor ne radar applications. However, the majority of schemes develop an estimate of the covariance matrix for the test cell by averaging over surrounding r ange cells, called reference data. This method is only guaranteed to ensure good performance when a large set of homogeneous reference data is availab le with the same statistics as the test cell. In this paper, a new methodol ogy is proposed for obtaining a covariance matrix that uses a priori knowle dge. The approach is useful for detecting weak signals in cases with discre tes in some range cells which do not appear in other range cells. The focus is on the use of a simple model for ground clutter that incorporates our p rior knowledge on the structure of the ground clutter. The new methodology can be applied to most existing STAP schemes. This is illustrated by applyi ng the methodology to three specific existing schemes, The modified schemes are generally shown to outperform the existing schemes in non-stationary m easured data cases. (C) 1999 Elsevier Science B.V. All rights reserved.