A structure-based subspace fitting approach for interference rejection in moving uniform linear array

Abstract

Space-time adaptive processing (STAP) is a well-suited methodology to eliminate strong interference in moving-platform sensor arrays. A fundamental issue in STAP is to determine the optimal weight vector. This can be obtained either by sample matrix inverse (SMI) or by subspace projection. We perform a structure-based two-stage signal subspace reconstruction to obtain an accurate estimate of the interference subspace with small training-data support. The performance of the proposed approach is evaluated based on numerically simulation.