A novel dynamic outlier-robust Kalman filter with Moving Horizon Estimation.
ISA Trans
; 151: 164-173, 2024 Aug.
Article
en En
| MEDLINE
| ID: mdl-38811310
ABSTRACT
The existence of dynamic outliers poses a significant challenge to the Kalman filter (KF). In addressing this challenge, this paper presents an innovative solution Firstly, by analyzing a period of measurement information to more accurately identify state and measurement dynamic outliers, the system's capacity to adapt to dynamic changes is significantly improved. Next, noise is modeled as a Gaussian-Student's t mixture distribution (GSTM), with mixed model parameters inferred using the variational Bayesian (VB) method based on measurement information, cleverly integrated into the Moving Horizon Estimation (MHE) framework, thus enhancing the flexibility and accuracy of the noise model. Lastly, the optimal window size was identified through simulation experiment analysis to further increase the estimation accuracy. Simulation results demonstrate that the proposed filter exhibits stronger robustness in resisting dynamic outliers compared to existing filters.
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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
ISA Trans
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos