A Key Conditional Quotient Filter for Nonlinear, non-Gaussian and non-Markovian System
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912182061498368 |
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| author | Zhao, Yuelin Wu, Feng Zhu, Li |
| author_facet | Zhao, Yuelin Wu, Feng Zhu, Li |
| contents | This paper proposes a novel and efficient key conditional quotient filter (KCQF) for the estimation of state in the nonlinear system which can be either Gaussian or non-Gaussian, and either Markovian or non-Markovian. The core idea of the proposed KCQF is that only the key measurement conditions, rather than all measurement conditions, should be used to estimate the state. Based on key measurement conditions, the quotient-form analytical integral expressions for the conditional probability density function, mean, and variance of state are derived by using the principle of probability conservation, and are calculated by using the Monte Carlo method, which thereby constructs the KCQF. Two nonlinear numerical examples were given to demonstrate the superior estimation accuracy of KCQF, compared to seven existing filters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05162 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Key Conditional Quotient Filter for Nonlinear, non-Gaussian and non-Markovian System Zhao, Yuelin Wu, Feng Zhu, Li Computational Engineering, Finance, and Science This paper proposes a novel and efficient key conditional quotient filter (KCQF) for the estimation of state in the nonlinear system which can be either Gaussian or non-Gaussian, and either Markovian or non-Markovian. The core idea of the proposed KCQF is that only the key measurement conditions, rather than all measurement conditions, should be used to estimate the state. Based on key measurement conditions, the quotient-form analytical integral expressions for the conditional probability density function, mean, and variance of state are derived by using the principle of probability conservation, and are calculated by using the Monte Carlo method, which thereby constructs the KCQF. Two nonlinear numerical examples were given to demonstrate the superior estimation accuracy of KCQF, compared to seven existing filters. |
| title | A Key Conditional Quotient Filter for Nonlinear, non-Gaussian and non-Markovian System |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2501.05162 |