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Open AccessArticle

A Sequential Multiplicative Extended Kalman Filter for Attitude Estimation Using Vector Observations

1
Department of Navigation Engineering, Naval University of Engineering, Wuhan 430000, China
2
Office of Research and Development, Naval University of Engineering, Wuhan 430000, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1414; https://doi.org/10.3390/s18051414
Received: 15 March 2018 / Revised: 10 April 2018 / Accepted: 25 April 2018 / Published: 3 May 2018
(This article belongs to the Collection Positioning and Navigation)
In this paper, a sequential multiplicative extended Kalman filter (SMEKF) is proposed for attitude estimation using vector observations. In the proposed SMEKF, each of the vector observations is processed sequentially to update the attitude, which can make the measurement model linearization more accurate for the next vector observation. This is the main difference to Murrell’s variation of the MEKF, which does not update the attitude estimate during the sequential procedure. Meanwhile, the covariance is updated after all the vector observations have been processed, which is used to account for the special characteristics of the reset operation necessary for the attitude update. This is the main difference to the traditional sequential EKF, which updates the state covariance at each step of the sequential procedure. The numerical simulation study demonstrates that the proposed SMEKF has more consistent and accurate performance in a wide range of initial estimate errors compared to the MEKF and its traditional sequential forms. View Full-Text
Keywords: attitude estimation; multiplicative extended Kalman filter; sequential estimation attitude estimation; multiplicative extended Kalman filter; sequential estimation
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MDPI and ACS Style

Qin, F.; Chang, L.; Jiang, S.; Zha, F. A Sequential Multiplicative Extended Kalman Filter for Attitude Estimation Using Vector Observations. Sensors 2018, 18, 1414.

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