An improved square-root algorithm for RTS Kalman smoothing

This paper improves on a noted square-root RTS Kalman smoothing algorithm proposed by Park and Kailath for the application purpose. This improved square-root RTS algorithm is able to additionally accommodate arbitrary exogenous known input, as such case is quite common in the real-world applications...

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Bibliographic Details
Published in:	2015 IEEE International Conference on Information and Automation pp. 1032 - 1036
Main Authors:	Li-Hui Geng, Ninness, Brett, Ze-Yu Xia
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.08.2015
Subjects:	Accuracy Aerodynamics Covariance matrices Exogenous Input Heuristic algorithms Improved Squareroot Algorithm Kalman filters RTS Kalman Smoothing Signal processing algorithms Smoothing methods Unitary Transformations
Online Access:	Get full text
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Summary:	This paper improves on a noted square-root RTS Kalman smoothing algorithm proposed by Park and Kailath for the application purpose. This improved square-root RTS algorithm is able to additionally accommodate arbitrary exogenous known input, as such case is quite common in the real-world applications. In addition, hyperbolic Householder transformations are employed to avoid the computation of the difference of two positive semi-definite matrices. The Givens rotations based unitary transformations are further used to make the resulting algorithm have higher computational efficiency. The relevant implementation steps of this algorithm is also addressed. Finally, a numerical simulation is given to verify this improved algorithm.
DOI:	10.1109/ICInfA.2015.7279438