Initialization of the Kalman Filter without Assumptions on the Initial State

Magnus Linderoth; Kristian Soltesz; Anders Robertsson; Rolf Johansson

doi:10.1109/ICRA.2011.5979684

Initialization of the Kalman Filter without Assumptions on the Initial State

Magnus Linderoth, Kristian Soltesz, Anders Robertsson, Rolf Johansson

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

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Abstract

In absence of covariance data, Kalman filters are usually initialized by guessing the initial state. Making the variance of the initial state estimate large makes sure that the estimate converges quickly and that the influence of the initial guess soon will be negligible. If, however, only very few measurements are available during the estimation process and an estimate is wanted as soon as possible, this might not be enough. This paper presents a method to initialize the Kalman filter without any knowledge about the distribution of the initial state and without making any guesses.

Original language	English
Title of host publication	2011 IEEE International Conference on Robotics and Automation
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	4992-4997
ISBN (Print)	978-1-61284-380-3
DOIs	https://doi.org/10.1109/ICRA.2011.5979684
Publication status	Published - 2011
Event	IEEE International Conference on Robotics and Automation, 2011 - Shanghai, China Duration: 2011 May 9 → 2011 May 13

Conference

Conference	IEEE International Conference on Robotics and Automation, 2011
Country/Territory	China
City	Shanghai
Period	2011/05/09 → 2011/05/13

Subject classification (UKÄ)

Control Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICRA.2011.5979684

soltesz11cAccepted author manuscript, 134 KB

Cite this

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title = "Initialization of the Kalman Filter without Assumptions on the Initial State",

abstract = "In absence of covariance data, Kalman filters are usually initialized by guessing the initial state. Making the variance of the initial state estimate large makes sure that the estimate converges quickly and that the influence of the initial guess soon will be negligible. If, however, only very few measurements are available during the estimation process and an estimate is wanted as soon as possible, this might not be enough. This paper presents a method to initialize the Kalman filter without any knowledge about the distribution of the initial state and without making any guesses.",

author = "Magnus Linderoth and Kristian Soltesz and Anders Robertsson and Rolf Johansson",

year = "2011",

doi = "10.1109/ICRA.2011.5979684",

language = "English",

isbn = "978-1-61284-380-3",

pages = "4992--4997",

booktitle = "2011 IEEE International Conference on Robotics and Automation",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "IEEE International Conference on Robotics and Automation, 2011 ; Conference date: 09-05-2011 Through 13-05-2011",

}

Linderoth, M, Soltesz, K, Robertsson, A & Johansson, R 2011, Initialization of the Kalman Filter without Assumptions on the Initial State. in 2011 IEEE International Conference on Robotics and Automation. IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 4992-4997, IEEE International Conference on Robotics and Automation, 2011 , Shanghai, China, 2011/05/09. https://doi.org/10.1109/ICRA.2011.5979684

Initialization of the Kalman Filter without Assumptions on the Initial State. / Linderoth, Magnus; Soltesz, Kristian; Robertsson, Anders et al.
2011 IEEE International Conference on Robotics and Automation. IEEE - Institute of Electrical and Electronics Engineers Inc., 2011. p. 4992-4997.

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

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AU - Soltesz, Kristian

AU - Robertsson, Anders

AU - Johansson, Rolf

PY - 2011

Y1 - 2011

N2 - In absence of covariance data, Kalman filters are usually initialized by guessing the initial state. Making the variance of the initial state estimate large makes sure that the estimate converges quickly and that the influence of the initial guess soon will be negligible. If, however, only very few measurements are available during the estimation process and an estimate is wanted as soon as possible, this might not be enough. This paper presents a method to initialize the Kalman filter without any knowledge about the distribution of the initial state and without making any guesses.

AB - In absence of covariance data, Kalman filters are usually initialized by guessing the initial state. Making the variance of the initial state estimate large makes sure that the estimate converges quickly and that the influence of the initial guess soon will be negligible. If, however, only very few measurements are available during the estimation process and an estimate is wanted as soon as possible, this might not be enough. This paper presents a method to initialize the Kalman filter without any knowledge about the distribution of the initial state and without making any guesses.

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DO - 10.1109/ICRA.2011.5979684

M3 - Paper in conference proceeding

SN - 978-1-61284-380-3

SP - 4992

EP - 4997

BT - 2011 IEEE International Conference on Robotics and Automation

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Conference on Robotics and Automation, 2011

Y2 - 9 May 2011 through 13 May 2011

ER -

Initialization of the Kalman Filter without Assumptions on the Initial State

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Initialization of the Kalman Filter without Assumptions on the Initial State

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