An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers
Autor: | Yongfu Li, Hao Zhu, Guorui Zhang, Henry Leung |
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Rok vydání: | 2022 |
Předmět: | |
Zdroj: | IEEE Transactions on Automatic Control. 67:374-381 |
ISSN: | 2334-3303 0018-9286 |
Popis: | In this paper, a novel variational Bayesian (VB) adaptive Kalman filter with inaccurate nominal process and measurement noise covariances in the presence of outliers is proposed. The probability density functions of state transition and measurement likelihood are modeled as Gaussian-Gamma mixture distributions. The state, process and measurement noise covariances are jointly inferred by the VB technique. Computer simulations show that the proposed method has better filtering accuracy than existing state-of-the-art filters under outlier environments. |
Databáze: | OpenAIRE |
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