A new heavy-tailed robust Kalman filter with time-varying process bias

DOI10.1007/s00034-021-01866-8zbMath1509.93056OpenAlexW3207446489MaRDI QIDQ6046518

Cheng-hao Shan, Wei-Dong Zhou, Liang Hou, Guang-le Jia, Zi-hao Jiang

Publication date: 11 May 2023

Published in: Circuits, Systems, and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00034-021-01866-8

zbMATH Keywords

linear systemsKalman filtervariational Bayesianheavy-tailed process and heavy-tailed measurement noisestime-varying process bias

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Sensitivity (robustness) (93B35) Stochastic learning and adaptive control (93E35) Stochastic systems in control theory (general) (93E03)

Cites Work

• Unnamed Item
• Maximum correntropy Kalman filter
• A survey of design methods for failure detection in dynamic systems
• Bayesian state estimation on finite horizons: the case of linear state-space model
• A novel Student's \(t\)-based Kalman filter with colored measurement noise
• Huber-based adaptive unscented Kalman filter with non-Gaussian measurement noise
• A modified variational Bayesian noise adaptive Kalman filter
• Linear Optimal Unbiased Filter for Time-Variant Systems Without Apriori Information on Initial Conditions
• A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices
• Fast Kalman-Like Optimal Unbiased FIR Filtering With Applications
• Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
• A Novel Robust Gaussian–Student's t Mixture Distribution Based Kalman Filter

This page was built for publication: A new heavy-tailed robust Kalman filter with time-varying process bias