Abstract
A new algorithm based on expectation maximization (EM) is presented for identifying the parameters and noise covariance matrices in an aircraft dynamic system. The proposed algorithm contains two steps. The first step is to estimate the state of the system using the Kalman filtering (KF) and the current estimator of these unknows. In the second step, the parameters as well as the noise covariance matrices are recursively updated by using the online EM algorithm and the multidimensional stochastic approximation strategy. In order to make a comprehensive comparison of the proposed algorithm and the traditional algorithm, the proposed algorithm is tested by using simulation data and shows desirable estimation accuracy.
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Zou, H., Zhang, W., Zuo, J., Chen, X., Cao, Y. (2019). EM-Based Online Identification Algorithm for Linear Aerodynamic Model Parameters. In: Zhang, X. (eds) The Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018). APISAT 2018. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-13-3305-7_182
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DOI: https://doi.org/10.1007/978-981-13-3305-7_182
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