A marginalised particle filter with variational inference for non‐linear state‐space models with Gaussian mixture noise - Université Toulouse III - Paul Sabatier - Toulouse INP Accéder directement au contenu
Article Dans Une Revue IET Radar Sonar and Navigation Année : 2022

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

Résumé

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.
Fichier principal
Vignette du fichier
Cheng_28431.pdf (766.05 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04119084 , version 1 (06-06-2023)

Identifiants

Citer

Cheng Cheng, Jean-Yves Tourneret, Xiaodong Lu. A marginalised particle filter with variational inference for non‐linear state‐space models with Gaussian mixture noise. IET Radar Sonar and Navigation, 2022, 16 (2), pp.193-397. ⟨10.1049/rsn2.12179⟩. ⟨hal-04119084⟩
23 Consultations
27 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More