The Stochastic Stability Analysis for Outlier Robustness of Kalman-type Filtering Framework Based on Correntropy-Induced Cost

This note introduces the Modified Extended Kalman Filter (MEKF), reformulating the EKF update step within a nonlinear regression framework. We propose a novel outlier-robust scheme, MCIC-MEKF, utilizing the minimum correntropy-induced cost criterion. We provide a theoretical analysis of its outlier...

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Bibliographic Details
Published inIEEE transactions on automatic control pp. 1 - 8
Main Authors Tao, Yangtianze, Kang, Jiayi, Yau, Stephen Shing-Toung
Format Journal Article
LanguageEnglish
Published IEEE 22.10.2024
Subjects
Adaptation models
Costs
Estimation error
Extended Kalman Filter
Kalman filters
Maximum Correntropy Criterion
Noise
Outliers
Robustness
Stability criteria
Stochastic processes
Stochastic Stability
Uncertainty
Vectors
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Summary:This note introduces the Modified Extended Kalman Filter (MEKF), reformulating the EKF update step within a nonlinear regression framework. We propose a novel outlier-robust scheme, MCIC-MEKF, utilizing the minimum correntropy-induced cost criterion. We provide a theoretical analysis of its outlier robustness through stochastic stability, proving exponentially bounded mean square posterior estimation error under natural conditions. Additionally, we present a technical approximation for the adaptive Kalman gain, enhancing efficiency without compromising performance. Simulation results confirm MCIC-MEKF's robustness against various non-Gaussian noises with large outliers, outperforming several filtering benchmarks.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2024.3485469