A Higher-Order Extended Cubature Kalman Filter Method Using the Statistical Characteristics of the Rounding Error of the System Model

Haiyang Zhang and Chenglin Wen ()
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Haiyang Zhang: School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
Chenglin Wen: School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China

Mathematics, 2024, vol. 12, issue 8, 1-16

Abstract: The cubature Kalman filter (CKF) cannot accurately estimate the nonlinear model, and these errors will have an impact on the accuracy. In order to improve the filtering performance of the CKF, this paper proposes a new CKF method to improve the estimation accuracy by using the statistical characteristics of rounding error, establishes a higher-order extended cubature Kalman filter (RHCKF) for joint estimation of sigma sampling points and random variables of rounding error, and gives a solution method considering the rounding error of multi-level approximation of the original function in the undermeasured dimension. Finally, numerical simulations show that the RHCKF has a better estimation effect than the CKF, and that the filtering accuracy is improved by using the information of the higher-order rounding error, which also proves the effectiveness of the method.

Keywords: cubature Kalman filtering; rounding error; estimation accuracy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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