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Initialization of the Kalman Filter without Assumptions on the Initial State

Linderoth, Magnus LU ; Soltesz, Kristian LU ; Robertsson, Anders LU and Johansson, Rolf LU (2011) IEEE International Conference on Robotics and Automation, 2011 In 2011 IEEE International Conference on Robotics and Automation p.4992-4997
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.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2011 IEEE International Conference on Robotics and Automation
pages
6 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE International Conference on Robotics and Automation, 2011
external identifiers
  • Scopus:84871680900
ISBN
978-1-61284-380-3
project
ROSETTA
language
English
LU publication?
yes
id
9a1691f6-4d24-47aa-b164-dea10ac6d8fe (old id 2158501)
date added to LUP
2011-10-18 10:51:30
date last changed
2016-10-13 04:50:49
@misc{9a1691f6-4d24-47aa-b164-dea10ac6d8fe,
  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       = {Linderoth, Magnus and Soltesz, Kristian and Robertsson, Anders and Johansson, Rolf},
  isbn         = {978-1-61284-380-3},
  language     = {eng},
  pages        = {4992--4997},
  publisher    = {ARRAY(0x8d1cb58)},
  series       = {2011 IEEE International Conference on Robotics and Automation},
  title        = {Initialization of the Kalman Filter without Assumptions on the Initial State},
  year         = {2011},
}