Missile trajectory learning and estimation based on variational inference of Gaussian factor structure
Aiming at the problem that the state of ballistic missiles with nonlinear batch state is difficult to predict,the Gaussian variational inference estimation method has the disadvantages of large iteration error and long estimation time.Based on this,a Gaussian variational inference method with factor structure covariance is proposed.By decomposing the covariance,the covariance relationship between all parameters is transformed into the connection between some main parameters in the matrix.At the same time,the stochastic gradient ascent method,reparameterization technique and adaptive learning rate method are used to iteratively optimize the variational lower bound and variational parameters to obtain the optimal parameters.Finally,the algorithm is used to analyze the example of missile state estimation,and compared with the state estimation algorithm based on Gaussian variational inference,its simulation calculation time is reduced by 23.6％on average.Accordingly,the proposed Gaussian variational inference method with factor covariance structure can effectively improve the computational efficiency of missile state estimation and reduce computational errors.
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维普
