Square root receding horizon information filters for nonlinear dynamic system models

Kim, D and Jeon, M 2013, 'Square root receding horizon information filters for nonlinear dynamic system models', IEEE Transactions on Automatic Control, vol. 58, no. 5, pp. 1284-1289.


Document type: Journal Article
Collection: Journal Articles

Title Square root receding horizon information filters for nonlinear dynamic system models
Author(s) Kim, D
Jeon, M
Year 2013
Journal name IEEE Transactions on Automatic Control
Volume number 58
Issue number 5
Start page 1284
End page 1289
Total pages 6
Publisher Institute of Electrical and Electronics Engineers
Abstract New nonlinear filtering algorithms are designed based on a receding horizon strategy, i.e., a finite impulse response (FIR) structure, and square root information filtering to achieve high accuracy and good performance in empirical error covariance tests. The new nonlinear receding horizon filters reduce approximation errors in nonlinear filtering by considering a set of recent observations with non-informative initial conditions. By applying information filtering, we are able to manage the non-informative initial conditions, and thus propose the square root version of the algorithm as a means of retaining the positive definiteness of the error covariance. Based on the proposed strategy, we then implement known nonlinear filtering frameworks. Simulation results confirm that the new nonlinear receding horizon filters outperform existing nonlinear filters in well-known nonlinear examples.
Subject Control Systems, Robotics and Automation
Keyword(s) Receding horizon estimation
Square root filtering
Unscented kalman filtering
DOI - identifier 10.1109/TAC.2012.2223352
Copyright notice © 2012 IEEE.
ISSN 0018-9286
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