Cooperative Localization Using Posterior Linearization Belief Propagation

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This paper presents the posterior linearization belief propagation (PLBP) algorithm for cooperative localization in wireless sensor networks with nonlinear measurements. PLBP performs two steps iteratively: linearization and belief propagation. At the linearization step, the nonlinear functions are linearized 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 linearized model. We show by numerical simulations how PLBP can outperform other algorithms in the literature.