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

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In this article, we propose 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 measure the near-field backscattering of a target and reconstruct a corresponding radar image. Then, modified copies of this image with rotation, translation, and spatial filtering, are generated according to the predefined desired arrangement, and they are coherently summed to create a single synthesized image in which all scattering contributions contained in the modified images are virtually included. Finally, the synthesized image is utilized to predict the far-field RCS of the 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 the reduction of the production costs of them. Moreover, we can easily test several different experimental layouts of the multiple targets without repeating a real measurement. Numerical simulations and experiments are provided to demonstrate the validity of the proposed image-based RCS synthesis.