Initialization of the Kalman Filter without Assumptions on the Initial State
(2011) IEEE International Conference on Robotics and Automation, 2011 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:
https://lup.lub.lu.se/record/2158501
- author
- Linderoth, Magnus LU ; Soltesz, Kristian LU ; Robertsson, Anders LU and Johansson, Rolf LU
- organization
- publishing date
- 2011
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2011 IEEE International Conference on Robotics and Automation
- pages
- 4992 - 4997
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Conference on Robotics and Automation, 2011
- conference location
- Shanghai, China
- conference dates
- 2011-05-09 - 2011-05-13
- external identifiers
-
- scopus:84871680900
- ISBN
- 978-1-61284-380-3
- DOI
- 10.1109/ICRA.2011.5979684
- project
- RobotLab LTH
- ROSETTA
- language
- English
- LU publication?
- yes
- id
- 9a1691f6-4d24-47aa-b164-dea10ac6d8fe (old id 2158501)
- date added to LUP
- 2016-04-04 12:18:47
- date last changed
- 2024-01-13 04:27:51
@inproceedings{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}}, booktitle = {{2011 IEEE International Conference on Robotics and Automation}}, isbn = {{978-1-61284-380-3}}, language = {{eng}}, pages = {{4992--4997}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Initialization of the Kalman Filter without Assumptions on the Initial State}}, url = {{https://lup.lub.lu.se/search/files/5976356/3812652.pdf}}, doi = {{10.1109/ICRA.2011.5979684}}, year = {{2011}}, }