Distributed weighted least-squares estimation with fast convergence for large-scale systems. (January 2015)

• Record Type: Journal Article
• Authors: Marelli, Damián Edgardo; Fu, Minyue
• Is Part Of: Automatica. Volume 51(2015)
• Page Start: 27
• Page End: 39
• Publication Date: 2015-01
• Subjects: Distributed estimation -- Distributed state estimation -- Large scale optimization -- Sensor network -- Networked control; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2014.10.077
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: 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.