Next Article in Journal
An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment
Next Article in Special Issue
AUV SLAM and Experiments Using a Mechanical Scanning Forward-Looking Sonar
Previous Article in Journal
Microfiber-Based Bragg Gratings for Sensing Applications: A Review
Previous Article in Special Issue
Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation
Sensors 2012, 12(7), 8877-8894; doi:10.3390/s120708877
Article

Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors

1,* , 1
 and
2
Received: 14 May 2012 / Revised: 14 June 2012 / Accepted: 18 June 2012 / Published: 27 June 2012
View Full-Text   |   Download PDF [298 KB, uploaded 21 June 2014]   |   Browse Figures

Abstract

A matrix Kalman filter (MKF) has been implemented for an integrated navigation system using visual/inertial/magnetic sensors. The MKF rearranges the original nonlinear process model in a pseudo-linear process model. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system is observable. It has been proved that such observability conditions are: (a) at least one degree of rotational freedom is excited, and (b) at least two linearly independent horizontal lines and one vertical line are observed. Experimental results have validated the correctness of these observability conditions.
Keywords: matrix Kalman filter; Lie derivatives; observability of nonlinear systems; navigation; vision; inertial measurement unit matrix Kalman filter; Lie derivatives; observability of nonlinear systems; navigation; vision; inertial measurement unit
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.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Feng, G.; Wu, W.; Wang, J. Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors. Sensors 2012, 12, 8877-8894.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

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