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Current Chinese Science


ISSN (Print): 2210-2981
ISSN (Online): 2210-2914

Research Article Section: Nanotechnology

Covariance Matching Based Adaptive Attitude Estimation of a Nano- Satellite Using SVD-Aided EKF

Author(s): Chingiz Hajiyev* and Demet Cilden-Guler

Volume 3, Issue 2, 2023

Published on: 17 October, 2022

Page: [154 - 163] Pages: 10

DOI: 10.2174/2210298102666220914094544


Background: The covariance matching procedure of the measurement noise covariance, namely the R matrix, was processed in singular value decomposition (SVD), which is one of the single-frame methods.

Aims: Tuning the system noise covariance Q matrix for the single-frame method aided Kalman filtering algorithm.

Objective: Develop the R and Q double covariance matching rule for the single-frame method aided Kalman filtering algorithm.

Methods: The matching procedure of the measurement noise covariance, namely the R matrix, is processed in singular value decomposition (SVD), which is one of the single-frame methods. The second matching rule is defined in the second stage of the proposed EKF design.

Results: The matching rules are run simultaneously, which makes the filter capable of being robust against initialization errors, system noise uncertainties, and measurement malfunctions at the same time without an external filter design necessity.

Conclusion: A single-frame method aided Kalman filtering algorithm based double covariance matching rule is presented in this paper. First, the measurement noise covariance matching is introduced using the SVD method that processes the R-adaptation inherently for the filtering stage. Second, the system noise covariance matching is described so as to have double covariance matching at the same time during the estimation procedure. The SVD-Aided AEKF becomes R- and Qadaptive simultaneously by applying the Q-adaptation rule to the intrinsically R-adaptive SVDaided EKF.

Keywords: Covariance matching, SVD-aided EKF, attitude estimation, nanosatellite, adaptive filter, Single frame methods.

Graphical Abstract
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