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Adaptive recognition and correction of baseline shifts from collocated GPS and accelerometer using two phases Kalman filter

  • The real-time recognition and precise correction of baseline shifts in strong-motion records is a critical issue for GPS and accelerometer combined processing. This paper proposes a method to adaptively recognize and correct baseline shifts in strong-motion records by utilizing GPS measurements using two phases Kalman filter. By defining four kinds of learning statistics and criteria, the time series of estimated baseline shifts can be divided into four time intervals: initialization, static, transient and permanent. During the time interval in which the transient baseline shift is recognized, the dynamic noise of the Kalman filter system and the length of the baseline shifts estimation window are adaptively adjusted to yield a robust integration solution. The validations from an experimental and real datasets show that acceleration baseline shifts can be precisely recognized and corrected, thus, the combined system adaptively adjusted the estimation strategy to get a more robust solution. (C) 2014 COSPAR. Published by Elsevier Ltd.The real-time recognition and precise correction of baseline shifts in strong-motion records is a critical issue for GPS and accelerometer combined processing. This paper proposes a method to adaptively recognize and correct baseline shifts in strong-motion records by utilizing GPS measurements using two phases Kalman filter. By defining four kinds of learning statistics and criteria, the time series of estimated baseline shifts can be divided into four time intervals: initialization, static, transient and permanent. During the time interval in which the transient baseline shift is recognized, the dynamic noise of the Kalman filter system and the length of the baseline shifts estimation window are adaptively adjusted to yield a robust integration solution. The validations from an experimental and real datasets show that acceleration baseline shifts can be precisely recognized and corrected, thus, the combined system adaptively adjusted the estimation strategy to get a more robust solution. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.show moreshow less

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Metadaten
Author:Rui Tu, Rongjiang Wang, Thomas R. WalterORCiDGND, FaQi Diao
DOI:https://doi.org/10.1016/j.asr.2014.07.008
ISSN:0273-1177 (print)
ISSN:1879-1948 (online)
Parent Title (English):Advances in space research
Publisher:Elsevier
Place of publication:Oxford
Document Type:Article
Language:English
Year of first Publication:2014
Year of Completion:2014
Release Date:2017/03/27
Tag:Baseline shift; GPS; Integration; Kalman filter; Strong-motion
Volume:54
Issue:9
Pagenumber:9
First Page:1924
Last Page:1932
Funder:China Scholarship Council
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften
Peer Review:Referiert