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Initialization of the Kalman Filter without Assumptions on the Initial State

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Author

Summary, in English

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.

Publishing year

2011

Language

English

Pages

4992-4997

Publication/Series

2011 IEEE International Conference on Robotics and Automation

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Control Engineering

Conference name

IEEE International Conference on Robotics and Automation, 2011

Conference date

2011-05-09 - 2011-05-13

Conference place

Shanghai, China

Status

Published

Project

  • RobotLab LTH
  • ROSETTA

Research group

  • LCCC

ISBN/ISSN/Other

  • ISBN: 978-1-61284-380-3