## The Unscented Kalman Filter for nonlinear estimation (2000)

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Citations: | 73 - 4 self |

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@INPROCEEDINGS{Wan00theunscented,

author = {Eric A. Wan and Rudolph Van Der Merwe},

title = {The Unscented Kalman Filter for nonlinear estimation},

booktitle = {},

year = {2000},

pages = {153--158}

}

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### Abstract

The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the Expectation Maximization (EM) algorithm) where both states and parameters are estimated simultaneously. This paper points out the flaws in using the EKF, and introduces an improvement, the Unscented Kalman Filter (UKF), proposed by Julier and Uhlman [5]. A central and vital operation performed in the Kalman Filter is the propagation of a Gaussian random variable (GRV) through the system dynamics. In the EKF, the state distribution is approximated

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Citation Context ...Equation 7). As expressed earlier, a number of algorithmic approaches exist for this problem. We present results for the Dual UKF and Joint UKF. Development of a Unscented Smoother for an EM approach =-=[2]-=- was presented in [13]. As in the prior state-estimation example, we utilize a noisy time-series application modeled with neural networks for illustration of the approaches. In the the dual extended K... |

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Citation Context ...h states and parameters are estimated simultaneously. This paper points out the flaws in using the EKF, and introduces an improvement, the Unscented Kalman Filter (UKF), proposed by Julier and Uhlman =-=[5]-=-. A central and vital operation performed in the Kalman Filter is the propagation of a Gaussian random variable (GRV) through the system dynamics. In the EKF, the state distribution is approximated by... |

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Citation Context ... i=0 W (m) i Y i (17) Py 2L X i=0 W (c) i fY i yg fY i yg T (18) Note that this method differs substantially from general "sampling " methods (e.g., Monte-Carlo methods such as particle f=-=ilters [1]-=-) which require orders of magnitude more sample points in an attempt to propagate an accurate (possibly nonGaussian) distribution of the state. The deceptively simple approach taken with the UT result... |

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