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Radar Cross Section (RCS), a crucial physical quantity that characterizes the backscattering intensity of targets under radar illumination, is the primary metric for assessing stealth capabilities. With the progression of radar detection technologies, RCS reduction has become a forefront research topic in radar stealth, aiming to minimize target detectability. As radar networking technologies mature, Bistatic Radar RCS reduction is gaining increasing significance over Monostatic Radar RCS reduction in future electromagnetic stealth countermeasures. Artificial electromagnetic metasurface have introduced innovative technical pathways for Bistatic Radar RCS reduction. However, current metasurface designs still face challenges related to inefficiency and suboptimal performance, primarily due to the time-consuming nature of large-scale array optimization and the global extremum characteristics of Bistatic Radar RCS reduction. To overcome these limitations, this study proposes a Few-Shot Convolutional Neural Network (CNN)-based approach, which achieves uniform full-space radar echo scattering by directionally optimizing metasurface phase distributions, thereby enabling effective Bistatic Radar RCS reduction. The approach integrates convolutional feature extraction, residual enhancement, and fully connected optimization modules, alongside a customized loss function, to efficiently capture the complex multidimensional relationships between diffuse reflection phases and the full-space RCS extrema. Theoretical calculations, full-wave simulations, and experimental tests show that the metasurface designed with this approach achieve over 10 dB of Bistatic Radar RCS reduction within the 7.26 GHz to 10.74 GHz frequency range. The method also ensures uniform diffuse reflection across the full space for various incidence angles (30°、 45°、 60°). Compared to traditional optimization algorithms, this method enhances RCS reduction by 17.2% while significantly improving computational efficiency. This approach offers a promising new technical paradigm for achieving full-space electromagnetic stealth in advanced weapon systems.
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