A marginalised particle filter with variational inference for non‐linear state‐space models with Gaussian mixture noise

Autor: Cheng Cheng, Jean‐Yves Tourneret, Xiaodong Lu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: IET Radar, Sonar & Navigation, Vol 16, Iss 2, Pp 238-248 (2022)
Druh dokumentu: article
ISSN: 1751-8792
1751-8784
DOI: 10.1049/rsn2.12179
Popis: Abstract This work proposes a marginalised particle filter with variational inference for non‐linear state‐space models (SSMs) with Gaussian mixture noise. A latent variable indicating the component of the Gaussian mixture considered at each time instant is introduced to specify the measurement mode of the SSM. The resulting joint posterior distribution of the state vector, the mode variable and the parameters of the Gaussian mixture noise is marginalised with respect to the noise variables. The marginalised posterior distribution of the state and mode is then approximated by using an appropriate marginalised particle filter. The noise parameters conditionally on each particle system of the state and mode variable are finally updated by using variational Bayesian inference. A simulation study is conducted to compare the proposed method with state‐of‐the‐art approaches in the context of positioning in urban canyons using global navigation satellite systems.
Databáze: Directory of Open Access Journals
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