Image-Based Radar Cross Section Synthesis for a Cluster of Multiple Static Targets

Abstract

This study proposes a novel image-based method for radar cross-section (RCS) prediction of a cluster of multiple static targets by synthesizing replicas of an original radar image. In this approach, we first measured the near-field backscattering of a target and reconstructed a corresponding radar image. Subsequently, modified copies of this image with rotation, translation, and spatial filtering were generated according to the predefined desired arrangement, and they were coherently summed to create a single synthesized image in which all scattering contributions contained in the modified images were virtually included. Finally, the synthesized image was utilized to predict the far-field RCS of multiple targets based on the theory of image-based near-field-to-far-field transformation (NFFFT). By employing the proposed algorithm, we can avoid building multiple test targets, resulting in reduced production costs. Moreover, we can easily test several different experimental layouts for multiple targets without repeating real measurements. Numerical simulations and experiments are conducted to demonstrate the validity of the proposed image-based RCS synthesis. The measurement results show that the proposed approach is valid when the multiple scattering between targets can be ignored, and the shadowing, which refers to the effect that a target is masked by another target, is insignificant.