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A New Extension of the Kalman Filter to Nonlinear Systems (1997)

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by Simon J. Julier , Jeffrey K. Uhlmann
Citations:778 - 6 self
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BibTeX

@INPROCEEDINGS{Julier97anew,
    author = {Simon J. Julier and Jeffrey K. Uhlmann},
    title = {A New Extension of the Kalman Filter to Nonlinear Systems},
    booktitle = {},
    year = {1997},
    pages = {182--193}
}

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Abstract

The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearises all nonlinear models so that the traditional linear Kalman filter can be applied. Although the EKF (in its many forms) is a widely used filtering strategy, over thirty years of experience with it has led to a general consensus within the tracking and control community that it is difficult to implement, difficult to tune, and only reliable for systems which are almost linear on the time scale of the update intervals. In this paper a new linear estimator is developed and demonstrated. Using the principle that a set of discretely sampled points can be used to parameterise mean and covariance, the estimator yields performance equivalent to the KF for linear systems yet general...

Keyphrases

kalman filter    new extension    nonlinear system    general consensus    traditional linear kalman filter    nonlinear model    estimator yield performance equivalent    update interval    time scale    extended kalman filter    many form    filtering strategy    thirty year    linear system    common approach    new linear estimator   

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