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Design of parallel adaptive extended Kalman filter for online estimation of noise covariance

Kai Xiong (Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing, China)
Liangdong Liu (Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing, China)

Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Article publication date: 6 November 2018

Issue publication date: 30 January 2019

204

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

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