Distributed LMMSE Estimation for Large-Scale Systems Based on Local Information

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

10 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)8528-8536
Journal / PublicationIEEE Transactions on Cybernetics
Volume52
Issue number8
Online published24 Mar 2021
Publication statusPublished - Aug 2022

Abstract

This article studies the distributed linear minimum mean square error (LMMSE) estimation problem for large-scale systems with local information (LSLI). Large-scale systems are composed of numerous subsystems. Each subsystem only transmits information to its neighbors. Thus, only the local information is available to each subsystem. This implies that the information available to different subsystems is different. Using local information to design an LMMSE estimator, the gains of the estimator must satisfy the sparse structure constraint, which makes the estimator design challenging and complicates the boundedness analysis of the estimation error covariance (EEC). In this article, a framework of the distributed LMMSE estimation for LSLI is established. The gains of the LMMSE estimator are effectively constructed by solving linear matrix equations. A gradient descent algorithm is exploited to design the gains of the LMMSE estimator numerically. Sufficient conditions are derived to ensure the boundedness of the EEC. Also, a gradient-based search algorithm is developed to verify whether the sufficient conditions hold or not. Finally, an example is used to illustrate the effectiveness of the proposed results.

Research Area(s)

  • Distributed estimation, Estimation, Kalman filters, large-scale system, Large-scale systems, local information, Mathematical model, minimum mean square error, Robot sensing systems, Temperature measurement, Temperature sensors

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