Stochastic Integration Filter with Improved State Estimate Mean-Square Error Computation

摘要

The paper deals with the Bayesian state estimation of nonlinear stochastic dynamic systems. The focus is aimed at the stochastic integration filter, which represents the Gaussian filters with the state and measurement prediction moments calculated by the stochastic integration rule. Besides the value of the integral, the rule also provides the covariance matrix of the integral value error. In the paper an improved mean-square error of the state estimate is proposed based on utilization of the integral error covariance matrix. The improved calculation is illustrated using two numerical examples for the stochastic integration filter of the third and fifth degrees.