Variational Bayesian adaptation of process noise covariance matrix in Kalman filtering

DOI10.1016/j.jfranklin.2021.02.037zbMath1464.93078OpenAlexW3135867532MaRDI QIDQ2027441

Shubi Zhang, Guobin Chang, Qiu-Zhao Zhang, Chao Chen

Publication date: 26 May 2021

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2021.02.037

zbMATH Keywords

covariance matrixvariational Bayesianprocess noiseadaptive Kalman filtering

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Stochastic learning and adaptive control (93E35)

Related Items (5)

Markov chain Monte Carlo based adaptive Rauch-Tung-Striebel smoother ⋮ Kalman filters based on multibody models: linking simulation and real world. A comprehensive review ⋮ Tuning-free filtering for stochastic systems with unmodeled measurement dynamics ⋮ Maximum correntropy criterion variational Bayesian adaptive Kalman filter based on strong tracking with unknown noise covariances ⋮ Switching Gaussian-heavy-tailed distribution based robust Gaussian approximate filter for INS/GNSS integration

Uses Software

• PRMLT

Cites Work

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