On Ensemble Kalman Filter, Particle Filter, and Gaussian Particle Filter
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- Murata Masaya
- NTT Communication Science Laboratories
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- Hiramatsu Kaoru
- NTT Communication Science Laboratories
Bibliographic Information
- Other Title
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- アンサンブルカルマンフィルタ,粒子フィルタ,ガウシアン粒子フィルタについて
- アンサンブルカルマンフィルタ,リュウシ フィルタ,ガウシアン リュウシ フィルタ ニ ツイテ
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Abstract
<p>In this paper, we clarify theoretical aspects of the representative non-Gaussian filters: the ensemble Kalman filter (EnKF) and the particle filter (PF). We first show that the EnKF is a realization algorithm of the linear optimal filter for nonlinear problems. We also show that under the Gaussian assumption for the predicted state, the EnKF provides a realization algorithm of the Gaussian filter. We next propose the multiple distribution estimation approach which is a novel framework for designing non-Gaussian filters and show that the PFs are special cases. We then propose a new PF algorithm to address the particle impoverishment problem inherent in the standard PF algorithms. We also show that by applying the proposed algorithm, we can improve the filtering accuracy of the Gaussian particle filter. We finally confirm the performance of each filter using two benchmark simulation models.</p>
Journal
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- Transactions of the Institute of Systems, Control and Information Engineers
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Transactions of the Institute of Systems, Control and Information Engineers 29 (10), 448-462, 2016
THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)
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Keywords
Details 詳細情報について
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- CRID
- 1390001205168891776
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- NII Article ID
- 130005292343
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- NII Book ID
- AN1013280X
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- ISSN
- 2185811X
- 13425668
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- NDL BIB ID
- 027649421
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed