Estimation of covariances for Kalman filter tuning using autocovariance method with Landweber iteration

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Autoren

  • M. Riva
  • Jesús Díaz Díaz
  • M. Dagen
  • T. Ortmaier

Organisationseinheiten

Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des Sammelwerks14th IASTED International Symposium on Intelligent Systems and Control (ISC 2013)
ErscheinungsortMarina del Ray, USA
PublikationsstatusVeröffentlicht - 2013

Abstract

Designing a Kalman filter requires knowledge about the stochastic part of the system. Thus, disturbances affecting states and measurements should be known. However, in practical application these disturbances are usually unknown. In this contribution a modification of the autocovariance least-square method is presented. This method converts the measurement and process noise covariance estimation problem into a least squares functional, which can be solved with a Landweber iteration to regularize the ill-posed problem. Then, a tuned Kalman filter gain can be calculated. A simulative evaluation is introduced to prove the method regarding robustness against modeling error and variance of the estimates.

Zitieren

Estimation of covariances for Kalman filter tuning using autocovariance method with Landweber iteration. / Riva, M.; Díaz, Jesús Díaz; Dagen, M. et al.
14th IASTED International Symposium on Intelligent Systems and Control (ISC 2013). Marina del Ray, USA, 2013.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Riva, M, Díaz, JD, Dagen, M & Ortmaier, T 2013, Estimation of covariances for Kalman filter tuning using autocovariance method with Landweber iteration. in 14th IASTED International Symposium on Intelligent Systems and Control (ISC 2013). Marina del Ray, USA.
Riva, M., Díaz, J. D., Dagen, M., & Ortmaier, T. (2013). Estimation of covariances for Kalman filter tuning using autocovariance method with Landweber iteration. In 14th IASTED International Symposium on Intelligent Systems and Control (ISC 2013)
Riva M, Díaz JD, Dagen M, Ortmaier T. Estimation of covariances for Kalman filter tuning using autocovariance method with Landweber iteration. in 14th IASTED International Symposium on Intelligent Systems and Control (ISC 2013). Marina del Ray, USA. 2013
Riva, M. ; Díaz, Jesús Díaz ; Dagen, M. et al. / Estimation of covariances for Kalman filter tuning using autocovariance method with Landweber iteration. 14th IASTED International Symposium on Intelligent Systems and Control (ISC 2013). Marina del Ray, USA, 2013.
Download
@inproceedings{3013f6384e2842358f282d76cf44a4dd,
title = "Estimation of covariances for Kalman filter tuning using autocovariance method with Landweber iteration",
abstract = "Designing a Kalman filter requires knowledge about the stochastic part of the system. Thus, disturbances affecting states and measurements should be known. However, in practical application these disturbances are usually unknown. In this contribution a modification of the autocovariance least-square method is presented. This method converts the measurement and process noise covariance estimation problem into a least squares functional, which can be solved with a Landweber iteration to regularize the ill-posed problem. Then, a tuned Kalman filter gain can be calculated. A simulative evaluation is introduced to prove the method regarding robustness against modeling error and variance of the estimates.",
author = "M. Riva and D{\'i}az, {Jes{\'u}s D{\'i}az} and M. Dagen and T. Ortmaier",
year = "2013",
language = "English",
booktitle = "14th IASTED International Symposium on Intelligent Systems and Control (ISC 2013)",

}

Download

TY - GEN

T1 - Estimation of covariances for Kalman filter tuning using autocovariance method with Landweber iteration

AU - Riva, M.

AU - Díaz, Jesús Díaz

AU - Dagen, M.

AU - Ortmaier, T.

PY - 2013

Y1 - 2013

N2 - Designing a Kalman filter requires knowledge about the stochastic part of the system. Thus, disturbances affecting states and measurements should be known. However, in practical application these disturbances are usually unknown. In this contribution a modification of the autocovariance least-square method is presented. This method converts the measurement and process noise covariance estimation problem into a least squares functional, which can be solved with a Landweber iteration to regularize the ill-posed problem. Then, a tuned Kalman filter gain can be calculated. A simulative evaluation is introduced to prove the method regarding robustness against modeling error and variance of the estimates.

AB - Designing a Kalman filter requires knowledge about the stochastic part of the system. Thus, disturbances affecting states and measurements should be known. However, in practical application these disturbances are usually unknown. In this contribution a modification of the autocovariance least-square method is presented. This method converts the measurement and process noise covariance estimation problem into a least squares functional, which can be solved with a Landweber iteration to regularize the ill-posed problem. Then, a tuned Kalman filter gain can be calculated. A simulative evaluation is introduced to prove the method regarding robustness against modeling error and variance of the estimates.

M3 - Conference contribution

BT - 14th IASTED International Symposium on Intelligent Systems and Control (ISC 2013)

CY - Marina del Ray, USA

ER -