Distributed LMMSE Estimation for Large-Scale Systems Based on Local Information
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 8528-8536 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 52 |
Issue number | 8 |
Online published | 24 Mar 2021 |
Publication status | Published - Aug 2022 |
Link(s)
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
Citation Format(s)
Distributed LMMSE Estimation for Large-Scale Systems Based on Local Information. / Wang, Yan; Xiong, Junlin; Ho, Daniel W. C.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 8, 08.2022, p. 8528-8536.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 8, 08.2022, p. 8528-8536.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review