Aiming at the problems of multi-sensor information fusion, a UAV position estimation based on non Gaussian strength and fused CKF is proposed. Firstly, to address the issue of poor estimation and compensation of system errors, a method of constructing pseudo measurement and equivalent measurement equations for radar system errors is proposed, and Kalman filtering is used for real-time estimation of radar system errors. Secondly, to address the issue of sample distribution morphology not being taken into account in skewness kurtosis testing, a method based on multimodal distribution and skewness coeffi- -cient constructors is proposed. By using multimodal distribution to identify multimodal distribution features and calculate sample skewness, non Gaussian features of data can be more comprehensively evaluated. Thirdly, to solve the problem of difficulty in constructing fusion coefficients due to non Gaussian noise in multi-sensor fusion, a method of constructing sensor fusion coefficients based on the strength of non Gaussian noise is proposed. Finally, using Cauchy kernel and Gaussian kernel CKF, the problem of reduced estimation accuracy caused by non Gaussian noise in dynamic state estimation is solved.