A New Adaptive Extended Kalman Filter for Cooperative Localisation

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  2. Professor Jonathon Chambers
Author(s)Huang Y, Zhang Y, Xu B, Wu Z, Chambers JA
Publication type Article
JournalIEEE Transactions on Aerospace and Electronic Systems
Year2017
Volume
Issue
Pagesepub ahead of print
ISSN (electronic)1557-9603
Full text is available for this publication:
To solve the problem of unknown noise covariance matrices inherent in the cooperative localization of autonomous underwater vehicles, a new adaptive extended Kalman filter is proposed. The predicted error covariance matrix and measurement noise covariance matrix are adaptively estimated based on an online expectation-maximization approach. Experimental results illustrate that, under the circumstances that are detailed in the paper, the proposed algorithm has better localization accuracy than existing state-of-the-art algorithms.
PublisherInstitute of Electrical and Electronics Engineers
URLhttps://doi.org/10.1109/TAES.2017.2756763
DOI10.1109/TAES.2017.2756763
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