Design of parallel adaptive extended Kalman filter for online estimation of noise covariance
Aircraft Engineering and Aerospace Technology
ISSN: 0002-2667
Article publication date: 6 November 2018
Issue publication date: 30 January 2019
Abstract
Purpose
The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the measurement noise may deviate from its nominal value in practical environment, and the filtering performance may decline because of the statistical uncertainty. Although the adaptive EKF (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics.
Design/methodology/approach
Aiming at this problem, this paper develops a parallel adaptive EKF (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value.
Findings
The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty and ensure the estimation accuracy in the normal case. The simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF.
Practical implications
The presented algorithm is applicable for spacecraft relative attitude and position estimation.
Originality/value
The PAEKF is presented for a kind of nonlinear uncertain systems. Stability analysis is provided to show that the error of the estimator is bounded under certain assumptions.
Keywords
Citation
Xiong, K. and Liu, L. (2020), "Design of parallel adaptive extended Kalman filter for online estimation of noise covariance", Aircraft Engineering and Aerospace Technology, Vol. 91 No. 1, pp. 112-123. https://doi.org/10.1108/AEAT-01-2018-0066
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited