• Chinese Journal of Ship Research
  • Vol. 18, Issue 4, 103 (2023)
Muyu HOU1, Shuhong GONG1,2, Donghai XIAO1, Yanchun ZUO1, and Yu LIU1
Author Affiliations
  • 1School of Physics, Xidian University, Xi'an 710071, China
  • 2Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an 710071, China
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    DOI: 10.19693/j.issn.1673-3185.03147 Cite this Article
    Muyu HOU, Shuhong GONG, Donghai XIAO, Yanchun ZUO, Yu LIU. Bagging-GPR method for ship RCS extrapolation in frequency domain[J]. Chinese Journal of Ship Research, 2023, 18(4): 103 Copy Citation Text show less

    Abstract

    Objective

    To solve the difficulty of obtaining a radar cross section (RCS) using traditional simulation and measurement methods under high frequency, this study proposes a hybrid method which combines bootstrap aggregation (Bagging) and spectral mixture covariance function-based Gaussian process regression (GPR) model to predict the RCS of ships in the high frequency band efficiently and accurately according to the data in the low frequency band.

    Methods

    First, according to the monostatic RCS data of ships in the low frequency band, the training subset is obtained by resampling. The spectral mixture covariance function-based GPR model is then used to extrapolate the RCS data of each subset in the frequency domain. Finally, the extrapolation results of each subset are mixed by the Bagging method to further improve the extrapolation accuracy and robustness of GPR. The proposed method is then tested on the simulation data and measured data respectively.

    Results

    The predicted value of the Bagging-GPR hybrid method is basically consistent with the simulated value and measured value, and the root mean square error is very small.

    Conclusions

    The Bagging-GPR hybrid method has high RCS extrapolation accuracy and good robustness in the frequency domain, providing a new technical means for quickly obtaining the high-frequency RCS characteristics of targets.