Next Article in Journal
Compact FTICR Mass Spectrometry for Real Time Monitoring of Volatile Organic Compounds
Next Article in Special Issue
Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing
Previous Article in Journal
Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
Previous Article in Special Issue
Robust Pedestrian Dead Reckoning Based on MEMS-IMU for Smartphones
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(5), 1414; https://doi.org/10.3390/s18051414

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.
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)
Full-Text   |   PDF [4200 KB, uploaded 3 May 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top