STATISTICAL ANALYSIS OF NEAR FIELD-TO-FAR FIELD RCS TRANSFORMATION PERFORMANCE

摘要

In previous AMTA presentations, we developed and evaluated an image-based near field-to-far field transformation (IB NFFFT) algorithm for monostatic RCS measurements. We showed that the algorithm's far field RCS pattern prediction performance was quite good for a variety of frequencies, near field measurement distances, and target geometries. In this paper, we quantify the statistical RCS prediction performance of the IB NFFFT using simulated data from a generalized point scatterer model and method of moments (MoM) code, both of which allow modeling of targets with single and multiple interactions. It is shown that the predicted RCS statistics remain quite accurate under conditions where the predicted far field patterns have significantly degraded due to multiple interactions and other effects.