NDLI: Unscented statistical linearization and robustified Kalman filter for nonlinear systems with parameter uncertainties

Content Provider	IEEE Xplore Digital Library
Author	Murata, M. Nagano, H. Kashino, K.
Copyright Year	2014
Description	Author affiliation: NTT Commun. Sci. Labs., NTT Corp., Atsugi, Japan (Murata, M.; Nagano, H.; Kashino, K.)
Abstract	Kalman filter (KF) design is well established for perfectly known linear system and observation models. Real-world phenomena, however, have significant associated uncertainties, and the tuning of the KF is not so straightforward for tackling them. In this paper, we present a method of designing a robust filter for nonlinear systems with model parameter uncertainties. The uncertainties are imposed on the temporal changes in system parameters, which corresponds to the conditions that most real-world problems exhibit. Our proposed filter is based on a robustified KF, which assumes Gaussian distributed states and is designed to be robust to significant changes in the system parameters. The uncertain nonlinear systems are handled by using the linearized approximation models to guarantee the Gaussianity of states. This is achieved by using a statistical linearization in conjunction with unscented transformations and we thus call the linearization technique unscented statistical linearization (USL). The USL is employed for the prediction step of nonlinearly transformed state and the subsequent filtering is executed by using the robustified KF to make the filter robust to upcoming observations. We call our proposed filter for the uncertain nonlinear systems a robustified nonlinear KF (robustified NKF) and confirm the effectiveness by experiments using artificially generated data.
Starting Page	5079
Ending Page	5084
File Size	237029
Page Count	6
File Format	PDF
ISBN	9781479932726
ISSN	07431619
e-ISBN	9781479932740
DOI	10.1109/ACC.2014.6858583
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Robustness Mathematical model Nonlinear systems Equations Covariance matrices Vectors Estimation error Uncertain systems Kalman filtering
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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