A New Variational Bayesian-Based Kalman Filter with Unknown Time-Varying Measurement Loss Probability and Non-Stationary Heavy-Tailed Measurement Noise

A New Variational Bayesian-Based Kalman Filter with Unknown Time-Varying Measurement Loss Probability and Non-Stationary Heavy-Tailed Measurement Noise

In this paper, a new variational Bayesian-based Kalman filter (KF) is presented to solve the filtering problem for a linear system with unknown time-varying measurement loss probability (UTVMLP) and non-stationary heavy-tailed measurement noise (NSHTMN). Firstly, the NSHTMN was modelled as a Gaussia...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Chenghao Shan, Weidong Zhou, Yefeng Yang, Hanyu Shan
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: MDPI AG 2021-10-01
Sarja:Entropy
Aiheet:
variational Bayesian
Kalman filter
measurement loss probability
mixture distribution
non-stationary heavy-tailed measurement noise
Linkit:https://www.mdpi.com/1099-4300/23/10/1351

Internet

https://www.mdpi.com/1099-4300/23/10/1351

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