Talks and Poster Presentations (without Proceedings-Entry):
F. Meyer, O. Hlinka, F. Hlawatsch:
"Sigma Point Belief Propagation";
Talk: IEEE AIPWCS-2013,
Aalborg, Dänemark (invited);
The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief propagation (BP) message passing scheme.
SPBP achieves approximate marginalizations of posterior sistributions corresponding to (generally) loopy factor graphs. It is well suited for decentralized inference because of its low communication requirements. For a decentralized, dynamic sensor localization problem, we demonstrate that SPBP can outperform nonparametric (particle-based) BP while requiring significantly less computations and communications.
Sigma points, belief propagation, factor graph, unscented transformation, cooperative localization
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.