A Novel Outlier-Robust Kalman Filtering Framework Based on Statistical Similarity Measure

In this article, a statistical similarity measure is introduced 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 discu...

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Veröffentlicht in:IEEE transactions on automatic control Jg. 66; H. 6; S. 2677 - 2692
Hauptverfasser: Huang, Yulong, Zhang, Yonggang, Zhao, Yuxin, Shi, Peng, Chambers, Jonathon A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Covariance matrices
Estimation
Heavy-tailed noise
Iterative algorithms
Iterative methods
Kalman filter
Kalman filters
Noise measurement
outliers
Outliers (statistics)
Pollution measurement
Robustness
separate iterative algorithm
Similarity
Similarity measures
Stability analysis
statistical similarity measure
ISSN:0018-9286, 1558-2523
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Zusammenfassung:In this article, a statistical similarity measure is introduced 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2020.3011443