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- Sirichai Pornsarayouth
- Graduate School of Science and Engineering, Tokyo Institute of Technology
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- Yamakita Masaki
- Graduate School of Science and Engineering, Tokyo Institute of Technology
書誌事項
- タイトル別名
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- Using Ensemble Kalman Filter for Distributed Sensor Fusion
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説明
Sensor fusion is a very powerful tool in various applications. Contrary to conventional methods using a single sensor, sensor fusions with multiple nodes (multiple sensors) give a state estimate from the distinct perspective of the nodes. By letting each node exchange data with its neighbors, a group of nodes form a sensor network, sensor fusion on each node can achieve more accurate estimation.However, in large-scale applications, a centralized sensor fusion which requires data from all nodes at once is not practical. On the other hand, using distributed or decentralized sensor fusion with conventional consensus algorithms which do not consider cross-covariance terms among nodes is not considerably efficient. We propose Ensemble Kalman Filter (EnKF) storing an estimation as a group of distinct particles to determine correlation between estimations. A cross-covariance can be easily obtained by simply approximating a sample second central cross-moment between estimations. Such a property of EnKF enables us to directly update a current estimation with the estimation from nearby nodes, even when the measurement is delayed. The proposed algorithm is proven to be globally optimal and iteratively stable even if redundancy of the estimation between nodes occurs.
収録刊行物
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- システム制御情報学会論文誌
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システム制御情報学会論文誌 26 (12), 466-476, 2013
一般社団法人 システム制御情報学会
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詳細情報 詳細情報について
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- CRID
- 1390001205165983232
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- NII論文ID
- 130003396796
- 40019907213
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- NII書誌ID
- AN1013280X
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- ISSN
- 2185811X
- 13425668
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- NDL書誌ID
- 025080537
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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