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Recursive weighted least squares estimation algorithm based on minimum model error principle
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DOI：10.1016/j.dt.2020.02.003.png)
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摘要
En 中文
Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system, such as projectile's trajectory estimation and control. While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined, which may result in filtering divergence. As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model, we improve the weighted least squares method (WLSM) with minimum model error principle. Invariant embedding method is adopted to solve the cost function including the model error. With the knowledge of measurement data and measurement error covariance matrix, we use gradient descent algorithm to determine the weighting matrix of model error. The uncertainty and linearization error of model are recursively estimated by the proposed method, thus achieving an online filtering estimation of the observations. Simulation results indicate that the proposed recursive esti-mation algorithm is insensitive to initial conditions and of good robustness. (c) 2020 China Ordnance Society. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Minimum model error
Weighted least squares method
State estimation
Invariant embedding method
Nonlinear recursive estimate
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1.9K
被引数:
6.4K
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