Individually optimal solutions to a remote state estimation problem with communication costs

Shinkyu Park; Nuno C. Martins

doi:10.1109/CDC.2014.7040013

Individually optimal solutions to a remote state estimation problem with communication costs

Shinkyu Park, Nuno C. Martins

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

In this paper, we discuss a remote state estimation problem that consists of a linear plant driven by process noise, a sensing unit that assesses the state of the plant, and a remotely located estimator. At each time step, the sensing unit decides whether to transmit the state of the plant to the estimator. The purpose of this work is to design a sensing unit and an estimator that minimize a cost which combines the expectation of the squared estimation error and a communication cost. We establish the existence of a jointly optimal solution to the problem, and we propose an iterative method, which generates a sequence that approaches the set of individually optimal solutions1.

Original language	English (US)
Title of host publication	Proceedings of the IEEE Conference on Decision and Control
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4014-4019
Number of pages	6
ISBN (Print)	9781479977468
DOIs	https://doi.org/10.1109/CDC.2014.7040013
State	Published - Jan 1 2014
Externally published	Yes

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10.1109/CDC.2014.7040013

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title = "Individually optimal solutions to a remote state estimation problem with communication costs",

abstract = "In this paper, we discuss a remote state estimation problem that consists of a linear plant driven by process noise, a sensing unit that assesses the state of the plant, and a remotely located estimator. At each time step, the sensing unit decides whether to transmit the state of the plant to the estimator. The purpose of this work is to design a sensing unit and an estimator that minimize a cost which combines the expectation of the squared estimation error and a communication cost. We establish the existence of a jointly optimal solution to the problem, and we propose an iterative method, which generates a sequence that approaches the set of individually optimal solutions1.",

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PY - 2014/1/1

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N2 - In this paper, we discuss a remote state estimation problem that consists of a linear plant driven by process noise, a sensing unit that assesses the state of the plant, and a remotely located estimator. At each time step, the sensing unit decides whether to transmit the state of the plant to the estimator. The purpose of this work is to design a sensing unit and an estimator that minimize a cost which combines the expectation of the squared estimation error and a communication cost. We establish the existence of a jointly optimal solution to the problem, and we propose an iterative method, which generates a sequence that approaches the set of individually optimal solutions1.

AB - In this paper, we discuss a remote state estimation problem that consists of a linear plant driven by process noise, a sensing unit that assesses the state of the plant, and a remotely located estimator. At each time step, the sensing unit decides whether to transmit the state of the plant to the estimator. The purpose of this work is to design a sensing unit and an estimator that minimize a cost which combines the expectation of the squared estimation error and a communication cost. We establish the existence of a jointly optimal solution to the problem, and we propose an iterative method, which generates a sequence that approaches the set of individually optimal solutions1.

UR - http://ieeexplore.ieee.org/document/7040013/

UR - http://www.scopus.com/inward/record.url?scp=84988293938&partnerID=8YFLogxK

U2 - 10.1109/CDC.2014.7040013

DO - 10.1109/CDC.2014.7040013

M3 - Conference contribution

SN - 9781479977468

SP - 4014

EP - 4019

BT - Proceedings of the IEEE Conference on Decision and Control

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Individually optimal solutions to a remote state estimation problem with communication costs

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