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.
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
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
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