Distributed Receding Horizon Kalman Filter

Pepe Maestre Torreblanca; Pontus Giselsson; Anders Rantzer

doi:10.1109/CDC.2010.5717370

Distributed Receding Horizon Kalman Filter

Pepe Maestre Torreblanca, Pontus Giselsson, Anders Rantzer

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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Abstract

In this paper a distributed version of the Kalman filter is proposed. In particular, the estimation problem is reduced to the optimization of a cost function that depends on the system dynamics and the latest output measurements and state estimates which is distributed among the agents by means of dual decomposition. The techniques presented in the paper are applied to estimate the position of mobile agents.

Original language	English
Title of host publication	49th IEEE Conference on Decision and Control (CDC)
DOIs	https://doi.org/10.1109/CDC.2010.5717370
Publication status	Published - 2010
Event	49th IEEE Conference on Decision and Control - Atlanta, Georgia, United States Duration: 2010 Dec 15 → …

Conference

Conference	49th IEEE Conference on Decision and Control
Country/Territory	United States
City	Atlanta, Georgia
Period	2010/12/15 → …

Subject classification (UKÄ)

Control Engineering

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10.1109/CDC.2010.5717370

pepe_cdc10

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@inproceedings{68ead8f465ef46c3832e51f61ab7e2c9,

title = "Distributed Receding Horizon Kalman Filter",

abstract = "In this paper a distributed version of the Kalman filter is proposed. In particular, the estimation problem is reduced to the optimization of a cost function that depends on the system dynamics and the latest output measurements and state estimates which is distributed among the agents by means of dual decomposition. The techniques presented in the paper are applied to estimate the position of mobile agents.",

author = "Torreblanca, {Pepe Maestre} and Pontus Giselsson and Anders Rantzer",

year = "2010",

doi = "10.1109/CDC.2010.5717370",

language = "English",

isbn = "978-1-4244-7745-6 ",

booktitle = "49th IEEE Conference on Decision and Control (CDC)",

note = "49th IEEE Conference on Decision and Control ; Conference date: 15-12-2010",

}

TY - GEN

T1 - Distributed Receding Horizon Kalman Filter

AU - Torreblanca, Pepe Maestre

AU - Giselsson, Pontus

AU - Rantzer, Anders

PY - 2010

Y1 - 2010

N2 - In this paper a distributed version of the Kalman filter is proposed. In particular, the estimation problem is reduced to the optimization of a cost function that depends on the system dynamics and the latest output measurements and state estimates which is distributed among the agents by means of dual decomposition. The techniques presented in the paper are applied to estimate the position of mobile agents.

AB - In this paper a distributed version of the Kalman filter is proposed. In particular, the estimation problem is reduced to the optimization of a cost function that depends on the system dynamics and the latest output measurements and state estimates which is distributed among the agents by means of dual decomposition. The techniques presented in the paper are applied to estimate the position of mobile agents.

U2 - 10.1109/CDC.2010.5717370

DO - 10.1109/CDC.2010.5717370

M3 - Paper in conference proceeding

SN - 978-1-4244-7745-6

BT - 49th IEEE Conference on Decision and Control (CDC)

T2 - 49th IEEE Conference on Decision and Control

Y2 - 15 December 2010

ER -

Distributed Receding Horizon Kalman Filter

Abstract

Conference

Subject classification (UKÄ)

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Cite this