Sigma Point Belief Propagation
Abstract
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 lowcomplexity approximation of the belief propagation (BP) message passing scheme. SPBP achieves approximate marginalizations of posterior distributions 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 (particlebased) BP while requiring significantly less computations and communications.
 Publication:

IEEE Signal Processing Letters
 Pub Date:
 February 2014
 DOI:
 10.1109/LSP.2013.2290192
 arXiv:
 arXiv:1309.0363
 Bibcode:
 2014ISPL...21..145M
 Keywords:

 Computer Science  Artificial Intelligence;
 Computer Science  Distributed;
 Parallel;
 and Cluster Computing
 EPrint:
 5 pages, 1 figure