Knowledge-aided Heterogeneity-compensation Algorithm for STAP Applicable to Bistatic Configurations and Conformal Antenna Arrays

摘要

Space-time adaptive processing (STAP) is a well-suited technique to detect slow-moving targets in the presence of a strong interference background. We consider the application of STAP in a bistatic radar configuration when the radar returns are recorded by a conformal antenna array (CAA). The secondary data snapshots used to estimate the optimum weight vector are typically heterogeneous, i.e., not identically distributed with respect to range, thus preventing the STAP processor from achieving its optimum performance. We present a novel knowledge-aided (KA), registration-based pre-processor that mitigates the heterogeneity of the secondary data. When applied to simulated data for a bowl-shaped antenna, this pre-processor is shown to provide enhanced performance when used in conjunction either with the standard sample matrix inversion (SMI) algorithm or with the more computationally- and data-efficient joint domain localized (JDL) algorithm.