Aircraft System Identification via Cubature Kalman Smoother with Expectation Maximization Algorithm

摘要

This paper developed a novel system identification algorithm to estimate parameter of aircraft dynamics modeled in state space. The developed method utilizes the cubature Kalman smoother to estimate the state and un?known parameters, combined with expectation?maximization algorithm, which estimates the statistics?unknown pa?rameters, i.e., the mean and covariance of an initial state, and the covariance of both process noise and measure?ment noise. To reduce the computational cost with considerable accuracy decline, the cubature Kalman smoother is employed to approximate the expectation values in the expectation maximization. Further, the analytical forms of un?known statistics parameters are given in the maximization step, which makes the nonconvex numerical optimization unnecessary. Its effectiveness is demonstrated through one problem of estimating aircraft aerodynamic parameters. The result shows that the proposed algorithm is of high accuracy as well as converge faster compared with other algo?rithms.

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