Maximum Correntropy Generalized Conversion Based Nonlinear Filtering

Nonlinear filtering methods have gained prominence in various applications, and one of the notable methods is the generalized conversion filter (GCF) based on deterministic sampling. The GCF offers an innovative method for converting measurements, exhibiting superior estimation performance when comp...

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Bibliographic Details
Published inIEEE sensors journal p. 1
Main Authors Dang, Lujuan, Jin, Shibo, Ma, Wentao, Chen, Badong
Format Journal Article
LanguageEnglish
Published IEEE 21.09.2024
Subjects
Covariance matrices
Deterministic Sampling (DS)
Generalized Conversion Filter (GCF)
Kalman filters
Maximum Correntropy Criterion (MCC)
Noise
Noise measurement
Nonlinear systems
State estimation
Time measurement
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Summary:Nonlinear filtering methods have gained prominence in various applications, and one of the notable methods is the generalized conversion filter (GCF) based on deterministic sampling. The GCF offers an innovative method for converting measurements, exhibiting superior estimation performance when compared to several popular existing nonlinear estimators. However, a notable limitation of existing GCF is their reliance on the minimum mean square error criterion. While GCF excels in environments with Gaussian noise, their performance can significantly deteriorate in the presence of non-Gaussian noise, particularly when subjected to heavy-tailed impulse noise interference. To address this challenge and enhance the robustness of GCF against impulse noise, this paper proposes a novel nonlinear filter known as the maximum correntropy generalized conversion filter (MCGCF). Similar to GCF, the proposed filter also employs a general measurement conversion, wherein deterministic sampling is utilized to optimize the first and second moments of multidimensional transformations. To obtain a robust posterior estimate of the state and covariance matrices, the MCGCF employs a nonlinear regression method to derive state update based on the maximum correntropy criterion (MCC). To validate the efficacy of the proposed MCGCF, two experiments are presented. These experiments illustrate the filter's ability to deliver robust and accurate estimates, even in challenging scenarios with nonlinear systems and non-Gaussian noises.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3461835