A New Continuous Discrete Unscented Kalman Filter

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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.

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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
DOI
Publication statusE-pub ahead of print - 2019
Publication categoryResearch
Peer-reviewedYes

    Research areas

  • 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
ID: 285386203