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Distributed Gauss-Newton Method for State Estimation Using Belief Propagation by Cosovic, Mirsad; Vukobratovic, Dejan is a scholarly article available to read on EtoBox.

What is Distributed Gauss-Newton Method for State Estimation Using Belief Propagation about?

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.

Author

Cosovic, Mirsad; Vukobratovic, Dejan

Published

2017

Language

EN