Cooperative localization using posterior linearization belief propagation

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
832-836
Series
IEEE Transactions on Vehicular Technology, Volume 67, issue 1
Abstract
This paper presents the posterior linearisation belief propagation (PLBP) algorithm for cooperative localisation in wireless sensor networks with nonlinear measurements. PLBP performs two steps iteratively: linearisation and belief propagation. At the linearisation step, the nonlinear functions are linearised using statistical linear regression with respect to the current beliefs. This SLR is performed in practice by using sigma-points drawn from the beliefs. In the second step, belief propagation is run on the linearised model. We show by numerical simulations how PLBP can outperform other algorithms in the literature.
Description
Keywords
Approximation algorithms, Bayes methods, Belief propagation, cooperative localisation, Covariance matrices, Gaussian message passing, Gaussian noise, Kalman filters, Message passing, Posterior linearisation, Sigma points
Other note
Citation
Garcia-Fernandez, A F, Svensson, L & Särkkä, S 2018, ' Cooperative localization using posterior linearization belief propagation ', IEEE Transactions on Vehicular Technology, vol. 67, no. 1, pp. 832-836 . https://doi.org/10.1109/TVT.2017.2734683