A Novel Outlier-Robust Kalman Filtering Framework based on Statistical Similarity Measure.pdf (2.71 MB)
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A Novel Outlier-Robust Kalman Filtering Framework based on Statistical Similarity Measure

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journal contribution
posted on 25.01.2021, 10:09 by Yulong Huang, Yonggang Zhang, Yuxin Zhao, Peng Shi, Jonathon Chambers
In this paper, a statistical similarity measure is in-troduced to quantify the similarity between two random vectors. The measure is then employed to develop a novel outlier-robust Kalman filtering framework. The approximation errors and the stability of the proposed filter are analyzed and discussed. To implement the filter, a fixed-point iterative algorithm and a separate iterative algorithm are given, and their local convergent conditions are also provided, and their comparisons have been made. In addition, selection of the similarity function is considered, and four exemplary similarity functions are established, from which the relations between our new method and existing outlier-robust Kalman filters are revealed. Simulation examples are used to illustrate the effectiveness and potential of the new filtering scheme.

History

Author affiliation

School of Engineering

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Automatic Control

Pagination

1 - 1

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

0018-9286

eissn

2334-3303

Copyright date

2020

Available date

23/07/2020

Language

en