Parameter Estimation of Aircraft Dynamics via Unscented Smoother with Expectation-Maximization Algorithm

Abstract

This paper proposes a new method for parameter estimation of aircraft dynamics modeled in state space. The developed method employs the smoother, which estimates the state and unknown parameters, combined with expectation-maximization algorithm to estimate unknown statistics in the problem, i.e., the mean and covariance of an initial state, and the noise covariances. To approximate the expectation values in the expectation-maximization with a reasonable computational cost, an unscented transform based on the estimates obtained by the unscented Rauch–Tung–Striebel smoother is employed. Moreover, nonconvex numerical optimization algorithms are not necessaryinthemaximizationstepoftheexpectation-maximization,becausetheoptimumsoftheunknownstatistics are given in analytical forms. Thus, the developed method achieves low computational cost and high robustness. Its effectiveness is demonstrated through two problems of estimating aircraft aerodynamic parameters.