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A Distributed Algorithm That Finds Almost Best Possible Estimate under Non-Vanishing and Time-Varying Measurement Noise

Lee, JG and Shim, H (2019) A Distributed Algorithm That Finds Almost Best Possible Estimate under Non-Vanishing and Time-Varying Measurement Noise. IEEE Control Systems Letters, 4. pp. 229-234.

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Abstract

In this letter, we review an existing distributed least-squares solver and share some new insights on it. Then, by the observation that an estimation of a constant vector under output noise can be translated into finding the least-squares solution, we present an algorithm for distributed estimation of the state of linear time-invariant systems under measurement noise. The proposed algorithm consists of a network of local observers, where each of them utilizes local measurements and information transmitted from the neighbors. It is proven that even under non-vanishing and time-varying measurement noise, we could obtain an almost best possible estimate with arbitrary precision. Some discussions regarding the plug-and-play operation are also given.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 15 Oct 2020 03:36
Last Modified: 01 Jul 2021 09:54
DOI: 10.1109/LCSYS.2019.2923475