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A novel square root cubature kalman filter using model-enhanced gaussian process state-space models.

Author(s)
Zhikun He Xi’an Research Institute of High-Tech, Xi’an, Shaanxi 710025, China
Guangbin Liu Xi’an Research Institute of High-Tech, Xi’an, Shaanxi 710025, China
Xijing Zhao Xi’an Research Institute of High-Tech, Xi’an, Shaanxi 710025, China
Zhicheng Yao Xi’an Research Institute of High-Tech, Xi’an, Shaanxi 710025, China
Bo Zhang Xi’an Research Institute of High-Tech, Xi’an, Shaanxi 710025, China
Abstract
A novel nonlinear filter called model-enhanced Gaussian process square root cubature Kalman filter (MEGP-SRCKF) is proposed to estimate the state of nonlinear dynamic systems where their state-space models are unknown or insufficiently accurate. The algorithm integrates Gaussian process regression (GPR) into square root cubature Kalman filter (SRCKF). Given the training data, GPR model is used to learn and represent the residual of system after factoring the contributions of the parametric model. The combination of GPR and parametric models enhances the performance of either model alone. It improves the accuracy of the transition and measurement models. The resulting MEGP-SRCKF algorithm has several advantages over standard extended Kalman filter (EKF), non-augmented unscented Kalman filter (UKF), augmented UKF, and SRCKF. Two cases are used to test these filters and the superiority of the proposed filter is demonstrated, where the MEGP-SRCKF can obtain the better results whether an accurate parametric state-space model is obtained.
Publication Details
Page(s) 535-541
DOI DOI not available
Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 45, Issue: 2, Year: 2012

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