A Fusion Method of Multivariate Measurement Data Based on Principal Component Estimation

For the acquired multivariate measurement data, a reasonable multivariate measurement data fusion algorithm needs to be designed to improve the outer ballistic measurement accuracy. Based on the theory of the classic EMBET method, this paper proposes a method of multivariate measurement data fusion based on principal component estimation. By optimizing the characteristic root screening method, the ill-conditioned phenomenon of the Jacobian matrix of the classic EMBET method is weakened, and the accuracy of measurement is effectively improved. Simulation experiments and measured data have confirmed the effectiveness of this method.