Cooperative localization using posterior linearization belief propagation
Research output: Contribution to journal › Article › Scientific › peer-review
- Chalmers University of Technology
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.
|Journal||IEEE Transactions on Vehicular Technology|
|Publication status||Published - 2018|
|MoE publication type||A1 Journal article-refereed|
- Approximation algorithms, Bayes methods, Belief propagation, cooperative localisation, Covariance matrices, Gaussian message passing, Gaussian noise, Kalman filters, Message passing, posterior linearisation, sigma points