Precise RCS Extrapolation via Nearfield 3-D Imaging With Adaptive Parameter Optimization Bayesian Learning

Nearfield (NF) 3-D imaging provides an effective solution of objects’ radar cross section (RCS) within a compact range. This article proposes a precise RCS extrapolation via NF 3-D imaging with adaptive parameter optimization Bayesian learning (APOBL), i.e., first, in the process of NF 3-D imaging, objects’ scattering centers may vary with the observation angle, while it is hard for the existing Bayesian learning via presetting parameters to reach an optimal estimation. For this issue, we present a parameter self-adaption solution, improving precision, and stability. Second, we also apply a block-based optimization idea in Bayesian-learning-based 3-D imaging, ensuring NF 3-D imaging quality. Third, in the process of RCS extrapolation, we apply a weighted Green’s function operator into the 3-D imaging-based NF-to-far-field (NF–FF) compensation, further ensuring high precision. The simulation and experiment results verify that the proposed method has an advantage in precision over the existing 3-D imaging-based RCS extrapolation methods.