非高斯加性噪声干扰下雷达高度表参数估计OA

To address the issues of degraded ranging accuracy and reduced robustness of radar altimeters under non-Gaussian additive noise interference,a height measurement method based on Bayesian parameter estimation is proposed.This method employs the generalized Gaussian distribution to model the characteristics of non-Gaussian noise,and combines Bayesian learning to achieve precise estimation of elevation parameters for airborne radar altimeters.Validation results from both simulation and measured data demonstrate that,compared with traditional least squares methods and existing Bayesian approaches,the proposed method improves the ranging accuracy and robustness of radar altimeter parameter estimation under non-Gaussian additive noise interference,providing reliable technical support for high-precision parameter estimation under complex noise conditions.