An adaptive-covariance-rank algorithm for the unscented Kalman filter

Lauren E. Padilla, Clarence W. Rowley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The Unscented Kalman Filter (UKF) is a nonlinear estimator that is particularly well suited for complex nonlinear systems. In the UKF, the error covariance is estimated by propagating forward a set of "sigma points," which sample the state space at intelligently chosen locations. However, the number of sigma points required scales linearly with the dimension of the system, so for large-dimensional systems such as weather models, the approach becomes intractable. This paper presents an approximate version of the UKF, in which the error covariance is represented by a reduced-rank approximation, thereby substantially reducing the number of sigma points required. The method is demonstrated on a one-dimensional atmospheric model known as the Lorenz 96 model, and the performance is shown to be close to that of a full-order UKF.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
Pages1324-1329
Number of pages6
DOIs
StatePublished - Dec 1 2010
Event2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States
Duration: Dec 15 2010Dec 17 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2010 49th IEEE Conference on Decision and Control, CDC 2010
CountryUnited States
CityAtlanta, GA
Period12/15/1012/17/10

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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    Padilla, L. E., & Rowley, C. W. (2010). An adaptive-covariance-rank algorithm for the unscented Kalman filter. In 2010 49th IEEE Conference on Decision and Control, CDC 2010 (pp. 1324-1329). [5717549] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2010.5717549