Recursive weighted least squares estimation algorithm based on minimum model error principle

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 filt...

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Vydáno v:	Defence technology Ročník 17; číslo 2; s. 545 - 558
Hlavní autoři:	Lei, Xiaoyun, Zhang, Zhian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.04.2021 School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China KeAi Communications Co., Ltd
Témata:	Invariant embedding method Minimum model error Nonlinear recursive estimate State estimation Weighted least squares method Weighted least squares method Invariant embedding method State estimation Minimum model error Nonlinear recursive estimate
ISSN:	2214-9147, 2214-9147
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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 estimation algorithm is insensitive to initial conditions and of good robustness. •The WLSM is improved with MMEE for state estimation in nonlinear system in sequence.•It is less complex in time space and more suitable for online real-time data processing.•The results of optimal state estimation and the convergence aren’t affected by measurements with abnormal errors.
ISSN:	2214-9147 2214-9147
DOI:	10.1016/j.dt.2020.02.003