Distributed Weighted Least Squares Estimator Without Prior Distribution Knowledge

This paper concerns with a distributed state estimation problem, where all sensor nodes are required to achieve a consensus estimation. The weighted least squares (WLS) estimator is a promising way to tackle this problem since it does not need the prior distribution knowledge with respect to the est...

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Bibliographic Details
Published inInternational Wireless Communications and Mobile Computing Conference (Online) pp. 1673 - 1678
Main Authors Liu, Shun, Li, Zhifei, Zhang, Weifang, Liang, Yan
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.06.2021
Subjects
Alternating direction method of multipliers
Consensus estimation
Convergence
Distributed
Information filters
Mobile computing
Numerical simulation
Optimization
State estimation
Weighted least squares
Wireless communication
Online AccessGet full text
ISSN2376-6506
DOI10.1109/IWCMC51323.2021.9498793

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Summary:This paper concerns with a distributed state estimation problem, where all sensor nodes are required to achieve a consensus estimation. The weighted least squares (WLS) estimator is a promising way to tackle this problem since it does not need the prior distribution knowledge with respect to the estimated quantity and noise terms. To this end, the equivalent relation between the information filter and the WLS estimator is explored first. Following this, an optimization problem coupled with a consensus constraint is established. Finally, the consensus-based distributed WLS problem is handled by the alternating direction method of multiplier. The convergence and consensus estimations between nodes are tested by numerical simulations and theoretical analyses.
ISSN:2376-6506
DOI:10.1109/IWCMC51323.2021.9498793