Transversely Stable Extended Kalman Filters for Systems on Manifolds in Euclidean Spaces

In this article, we devise a variant of the extended Kalman filter that can be generally applied to systems on manifolds with simplicity and low computational cost. We extend a given system on a manifold to an ambient open set in Euclidean space and modify the system such that the extended system is transversely stable on the manifold. Then, we apply the standard extended Kalman filter derived in Euclidean space to the modified dynamics. This method is efficient in terms of computation and accurate in comparison with the standard extended Kalman filter. It has the merit that we can apply various Kalman filters derived in Euclidean space including extended Kalman filters for state estimation for systems defined on manifolds. The proposed method is successfully applied to the rigid body attitude dynamics whose configuration space is the special orthogonal group in three dimensions.

[1]  George K. I. Mann,et al.  Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry , 2019 .

[2]  Axel Barrau,et al.  Invariant Kalman Filtering , 2018, Annu. Rev. Control. Robotics Auton. Syst..

[3]  Taeyoung Lee,et al.  Stable manifolds of saddle equilibria for pendulum dynamics on S2 and SO(3) , 2011, IEEE Conference on Decision and Control and European Control Conference.

[4]  Fernando Jiménez,et al.  Feedback Integrators , 2016, J. Nonlinear Sci..

[5]  Soo Jeon,et al.  Kinematic Kalman Filter (KKF) for Robot End-Effector Sensing , 2009 .

[6]  F. Lewis,et al.  Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition , 2007 .

[7]  Costanzo Manes,et al.  Comparative Study of Unscented Kalman Filter and Extended Kalman Filter for Position/Attitude Estimation in Unmanned Aerial Vehicles , 2008 .

[8]  D. Gingras,et al.  Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[9]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[10]  Dong Eui Chang,et al.  On controller design for systems on manifolds in Euclidean space , 2018, International Journal of Robust and Nonlinear Control.

[11]  D. Bernstein Matrix Mathematics: Theory, Facts, and Formulas , 2009 .

[12]  Carlos H. Muravchik,et al.  Posterior Cramer-Rao bounds for discrete-time nonlinear filtering , 1998, IEEE Trans. Signal Process..

[13]  B.J. Sipos Application of the manifold-constrained unscented Kalman filter , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[14]  Dong Eui Chang,et al.  Global Chartwise Feedback Linearization of the Quadcopter With a Thrust Positivity Preserving Dynamic Extension , 2017, IEEE Transactions on Automatic Control.

[15]  Fredrik Gustafsson,et al.  Some Relations Between Extended and Unscented Kalman Filters , 2012, IEEE Transactions on Signal Processing.

[16]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[17]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[18]  Gene H. Golub,et al.  Matrix computations , 1983 .