RCS DATA IMPROVEMENT USING TWO-DIMENSIONAL PARAMETRIC SIGNAL HISTORY EDITING (PSHE)

摘要

Target support and clutter contamination can be a limiting factor in radar cross section (RCS) measurements of signature-controlled targets. Conventional ISAR image editing methods can be used to remove contamination, but their performance degrades rapidly when the available resolution is insufficient to identify and separate the support returns from those of the target.rnERIM International, Inc. (EI) has developed and successfully demonstrated data post-processing techniques based on 1-D parametric spectral estimators for removing additive contamination from low resolution swept frequency measurements.rnTo further enhance performance and take advantage of the cross-range resolution afforded by target aspect information, EI has investigated the use of coherent 2-D spectral estimation techniques for improved identification and mitigation of measurement contamination in frequency and angle diverse data. In particular, parametric signal history editing (PSHE) algorithms based on 2-D TLS-Prony and 2-D MEMP have been developed and exercised on numerical simulations and measured data.rnThe paper demonstrates 2-D spectral estimation in representative measurement situations, identifies strengths and limitations, and quantifies mitigation algorithm performance. In addition, automated filtering of spectral representations using energy level ordering, Cramer Rao Bounds (CRBs), and spatial filtering are discussed.