A New Continuous Discrete Unscented Kalman Filter

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The time and measurement update for the discrete time Kalman filter can be formulated in terms of conditional means and covariances. The unscented Kalman filter can be interpreted as calculating these conditional means and covariances by using the unscented transform. This approach can also be directly applied to nonlinear models as an alternative to the discrete time extended Kalman filter. In this paper, a novel method for computing the unscented Kalman filter for a nonlinear model with continuous time dynamics and discrete time measurements is presented. Compared to the existing approaches, this method is far simpler and less computationally demanding, and it performs at least as well.

Original languageEnglish
JournalI E E E Transactions on Automatic Control
ISSN0018-9286
DOIs
Publication statusE-pub ahead of print - 2019

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Kalman filters
Extended Kalman filters
Time measurement

Keywords

  • Computational modeling
  • Continuous discrete estimation
  • Differential equations
  • Estimation
  • Estimation and filtering
  • Kalman filters
  • Mathematical model
  • Stochastic differential equation
  • Time measurement
  • Transforms
  • Unscented Kalman filter
  • Unscented transform

Cite this

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title = "A New Continuous Discrete Unscented Kalman Filter",
abstract = "The time and measurement update for the discrete time Kalman filter can be formulated in terms of conditional means and covariances. The unscented Kalman filter can be interpreted as calculating these conditional means and covariances by using the unscented transform. This approach can also be directly applied to nonlinear models as an alternative to the discrete time extended Kalman filter. In this paper, a novel method for computing the unscented Kalman filter for a nonlinear model with continuous time dynamics and discrete time measurements is presented. Compared to the existing approaches, this method is far simpler and less computationally demanding, and it performs at least as well.",
keywords = "Computational modeling, Continuous discrete estimation, Differential equations, Estimation, Estimation and filtering, Kalman filters, Mathematical model, Stochastic differential equation, Time measurement, Transforms, Unscented Kalman filter, Unscented transform",
author = "Torben Knudsen and John-Josef Leth",
year = "2019",
doi = "10.1109/TAC.2018.2867325",
language = "English",
journal = "I E E E Transactions on Automatic Control",
issn = "0018-9286",
publisher = "IEEE",

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A New Continuous Discrete Unscented Kalman Filter. / Knudsen, Torben; Leth, John-Josef.

In: I E E E Transactions on Automatic Control, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - A New Continuous Discrete Unscented Kalman Filter

AU - Knudsen, Torben

AU - Leth, John-Josef

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AB - The time and measurement update for the discrete time Kalman filter can be formulated in terms of conditional means and covariances. The unscented Kalman filter can be interpreted as calculating these conditional means and covariances by using the unscented transform. This approach can also be directly applied to nonlinear models as an alternative to the discrete time extended Kalman filter. In this paper, a novel method for computing the unscented Kalman filter for a nonlinear model with continuous time dynamics and discrete time measurements is presented. Compared to the existing approaches, this method is far simpler and less computationally demanding, and it performs at least as well.

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KW - Continuous discrete estimation

KW - Differential equations

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KW - Mathematical model

KW - Stochastic differential equation

KW - Time measurement

KW - Transforms

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KW - Unscented transform

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DO - 10.1109/TAC.2018.2867325

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