Journal of System Simulation
Abstract
Abstract: The system identification algorithm, based on the square root cubature Kalman filter (SRCKF) is proposed to address issues such as low accuracy, poor robustness, and weak generalization ability encountered by the extended Kalman filter (EKF) algorithm in parameters identification of ship maneuvering motion models. This algorithm, within the framework of CKF, replaces the original covariance matrix with its root mean square and utilizes triangular decomposition for prediction and update to enhance identification stability. The EKF is used as a comparison algorithm to identify the parameters of the second-order nonlinear response model of a ship with rudder angles that comply with changes in the rudder servo system using the numerical simulation data solved by the fourth-order Lunger Kuta method, and the obtained identification model is subjected to a verification test of the generalisation ability. The results indicate that the SRCKF algorithm has higher identification accuracy, stability, and generalization ability than the EKF algorithm.
Recommended Citation
Li, Qinghao; Ren, Junsheng; and Hua, Yan
(2024)
"Parametric Identification of Ship Maneuvering Motion Response Model Based on Square Root Cubature Kalman Filtering,"
Journal of System Simulation: Vol. 36:
Iss.
8, Article 5.
DOI: 10.16182/j.issn1004731x.joss.24-0195
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss8/5
First Page
1790
Last Page
1799
CLC
TP391.9
Recommended Citation
Li Qinghao, Ren Junsheng, Hua Yan. Parametric Identification of Ship Maneuvering Motion Response Model Based on Square Root Cubature Kalman Filtering[J]. Journal of System Simulation, 2024, 36(8): 1790-1799.
DOI
10.16182/j.issn1004731x.joss.24-0195
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