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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; in revised form: 14 June 2012 / Accepted: 18 June 2012 / Published: 27 June 2012
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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.

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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.

AMA 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(7):8877-8894.

Chicago/Turabian Style

Feng, Guohu; Wu, Wenqi; Wang, Jinling. 2012. "Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors." Sensors 12, no. 7: 8877-8894.


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