Improved STAP performance using knowledge-aided secondary data selection

Abstract

Secondary data selection for estimation of the clutter covariance matrix, needed in space-time adaptive processing (STAP), is normally obtained from range rings nearby the cell under test. The assumption is that these range rings contain cells that are representative of the clutter statistics in the test cell. However, in a nonhomogeneous terrain environment, this may not be true. An innovative approach is presented, in the area of knowledge-aided STAP, which utilizes terrain data from the United States Geological Survey (USGS) to aid in the selection of secondary data cells. Results have been obtained and compared with the sliding (cell averaging symmetric) window method of secondary data selection. This comparison indicates that making use of the surveillance terrain knowledge improves STAP performance.