The square-root unscented Kalman filter for state and parameter-estimation (2001) [17 citations — 3 self]
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@INPROCEEDINGS{Merwe01thesquare-root,author = {Rudolph Van Der Merwe and Eric A. Wan},
title = {The square-root unscented Kalman filter for state and parameter-estimation},
booktitle = {in International Conference on Acoustics, Speech, and Signal Processing},
year = {2001},
pages = {3461--3464}
}
Years of Citing Articles
Abstract:
Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network). The EKF applies the standard linear Kalman filter methodology to a linearization of the true nonlinear system. This approach is sub-optimal, and can easily lead to divergence. Julier et al. [1] proposed the unscented Kalman filter (UKF) as a derivative-free alternative to the extended Kalman filter in the framework of state-estimation. This was extended to parameterestimation by Wan and van der Merwe [2, 3]. The UKF consistently outperforms the EKF in terms of prediction and estimation error, at an equal computational complexity of O(L 3
Citations
| 152 | A new extension of the Kalman filter to nonlinear systems – Julier, Uhlmann - 1997 |
| 46 | A state-space approach to adaptive RLS filtering,”IEEE Signal Processing Magazine – Sayed, Kailath - 1994 |
| 44 | Decoupled Extended Kalman Filter Training of Feedforward Layered Networks – Puskorius, Feldkamp - 1991 |
| 42 | der Merwe, “The unscented Kalman filter for nonlinear estimation – Wan, Van - 2000 |
| 13 | Dual estimation and the unscented transformation – Wan, Merwe, et al. - 2000 |
| 12 | Efficient Derivative-Free Kalman Filters for Online Learning – Merwe, Wan - 2001 |
| 6 | der Merwe, "The Unscented Kalman Filter for Nonlinear Estimation – Wan, van - 2000 |
| 4 | A State-Space Approach to Adaptive RLS – Sayed, Kailath - 1994 |
| 2 | Uhlmann, "A New Extension of the Kalman Filter to Nonlinear Systems – Julier, K - 1997 |
