Journal
AUTOMATICA
Volume 51, Issue -, Pages 27-39Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2014.10.077
Keywords
Distributed estimation; Distributed state estimation; Large scale optimization; Sensor network; Networked control
Funding
- State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT1414]
- Austrian Science Fund (FWF project) [M1230-N13]
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In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods. (C) 2014 The Authors. Published by Elsevier Ltd.
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