Estimation and Registration on Graphs
Abstract
A statistical framework is introduced for a broad class of problems involving synchronization or registration of data across a sensor network in the presence of noise. This framework enables an estimation-theoretic approach to the design and characterization of synchronization algorithms. The Fisher information is expressed in terms of the distribution of the measurement noise and standard mathematical descriptors of the network's graph structure for several important cases. This leads to maximum likelihood and approximate maximum-likelihood registration algorithms and also to distributed iterative algorithms that, when they converge, attain statistically optimal solutions. The relationship between optimal estimation in this setting and Kirchhoff's laws is also elucidated.
Cite
@article{arxiv.1010.2983,
title = {Estimation and Registration on Graphs},
author = {Stephen D. Howard and Douglas Cochran and William Moran and Frederick R. Cohen},
journal= {arXiv preprint arXiv:1010.2983},
year = {2010}
}
Comments
15 pages