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A New Adaptive Extended Kalman Filter for Cooperative Localisation

Lookup NU author(s): Professor Jonathon Chambers



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2018.

For re-use rights please refer to the publisher's terms and conditions.


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.

Publication metadata

Author(s): Huang Y, Zhang Y, Xu B, Wu Z, Chambers JA

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Aerospace and Electronic Systems

Year: 2018

Volume: 54

Issue: 1

Pages: 353-368

Print publication date: 01/02/2018

Online publication date: 26/09/2017

Acceptance date: 29/07/2017

Date deposited: 30/11/2017

ISSN (print): 0018-9251

ISSN (electronic): 1557-9603

Publisher: IEEE


DOI: 10.1109/TAES.2017.2756763


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