Distributed Gauss-Newton Method for State Estimation Using Belief Propagation
Abstract
We present a novel distributed Gauss-Newton method for the non-linear state estimation (SE) model based on a probabilistic inference method called belief propagation (BP). The main novelty of our work comes from applying BP sequentially over a sequence of linear approximations of the SE model, akin to what is done by the Gauss-Newton method. The resulting iterative Gauss-Newton belief propagation (GN-BP) algorithm can be interpreted as a distributed Gauss-Newton method with the same accuracy as the centralized SE, however, introducing a number of advantages of the BP framework. The paper provides extensive numerical study of the GN-BP algorithm, provides details on its convergence behavior, and gives a number of useful insights for its implementation.
Keywords
Cite
@article{arxiv.1702.05781,
title = {Distributed Gauss-Newton Method for State Estimation Using Belief Propagation},
author = {Mirsad Cosovic and Dejan Vukobratovic},
journal= {arXiv preprint arXiv:1702.05781},
year = {2018}
}
Comments
Version of the journal paper accepted for publication. Demo source code available online at https://github.com/mcosovic