Construction of training database based on high frequency RCS prediction methods for ATR

Abstract

Due to the difficulty of creating training databases using all real enemy targets, it is necessary to derive them using computer simulations. In this paper, we apply three high frequency radar cross section (RCS) methods to create a training database for automatic target recognition (ATR) using 1-D range profiles. These methods are: physical optics (PO), physical theory of diffraction (PTD) and shooting and bouncing ray (SBR). Experimental results derived from the performance of combinational feature space trajectory with a new distance metric (FSTND) classifier show that PO+PTD is the most efficient method for ATR because of the additional information by diffraction terms. SBR shows poor performance due to the cavity structure.