Projection approach for STAP

The sample support problem in space-time adaptive processing (STAP) applications arises from the requirement to adapt many spatial and temporal degrees-of-freedom (DOF) to a changing interference environment that includes clutter and jammers. Often, in heterogeneous overland strong clutter environments, the available wide sense stationary sample support is severely limited to preclude the direct implementation of the sample matrix inverse (SMI) approach. In this paper we outline an approach to address the sample support problem by utilizing projection methods - alternating projections or relaxed projection operators onto desired convex sets - to retain the a-priori known structure of the covariance matrix. Our initial analysis shows that by combining these approaches with eigenbased techniques, it is possible to reduce significantly the data samples required in non-stationary environment and consequently achieve superior target detection. In fact, multiplicative improvement in data reduction compared to direct eigen-based methods can be obtained at the expense of negligible loss in space-time aperture.