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Multiple Dimensional Correntropy Kalman Filter
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AI
DOI：10.1109/LSP.2025.3542697.png)
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摘要
En 中文
The letter addresses the robust state estimation problem of non-Gaussian systems disturbed by outliers. Unlike the existing correntropy-based state estimation framework, which uses a uniform weight for the evaluated error vectors and solely relies on inaccurate nominal covariance matrices for estimating the system state, this work proposes a novel maximum-correntropy Kalman filter. This new approach utilizes multiple dimensional correntropy to assess the similarity between vectors across different dimensions. Additionally, it adjusts the covariance matrices simultaneously by utilizing the adopted matrix similarity measure within the modified correntropy framework. Simulations on target tracking demonstrate that our proposed algorithm exhibits excellent estimation accuracy and robustness while possessing adaptive capability even under time-varying heavy-tailed noises.
Keyword:
Vectors
Noise
Covariance matrices
Signal processing algorithms
Kernel
Pollution measurement
Target tracking
Noise measurement
Kalman filters
Accuracy
Multiple dimension correntropy
non-Gaussian noise
robust Kalman filter
covariance estimation
