Robust optimal filters of linear stochastic systems
| Project/Area Number |
22540158
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | Osaka Institute of Technology |
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Principal Investigator |
TANIKAWA Akio 大阪工業大学, 情報科学部, 教授 (00163618)
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| Project Period (FY) |
2010 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2010: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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| Keywords | 確率論 / 応用数学 |
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| Research Abstract |
Although the Kalman filter has been the most popular and widely used optimal filter for stochastic systems in practice, it is not a robust filter and so we cannot have good results often in simulations for the systems with mismatches between the actual systems and their mathematical models (i.e., the systems with modeling errors). Chen and Patton succeeded in proposing a simple filtering algorithm ODDO which is robust and optimal and can be applicable even to the systems with modeling errors. However, we later indicated that the ODDO was derived from their incorrect basic formula. In this project, we have succeeded in establishing a correct theory of robust optimal filters for stochastic systems with unknown disturbances and developing widely applicable algorithms (iterative methods) for practical systems.
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Report
(4 results)
Research Products
(18 results)
