Non-Gaussian Filters for Nonlinear Continuous-Discrete Models
In this paper, we propose using an ensemble Kalman filter (EnKF) and particle filters (PFs) to obtain superior state estimation accuracy for nonlinear continuous-discrete models. We discretize the Ito-type stochastic differential system model by means of the usual procedure and suppress the approxim...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-03-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.9746/jcmsi.10.53 |
| _version_ | 1797661041935515648 |
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| author | Masaya Murata Kaoru Hiramatsu |
| author_facet | Masaya Murata Kaoru Hiramatsu |
| author_sort | Masaya Murata |
| collection | DOAJ |
| description | In this paper, we propose using an ensemble Kalman filter (EnKF) and particle filters (PFs) to obtain superior state estimation accuracy for nonlinear continuous-discrete models. We discretize the Ito-type stochastic differential system model by means of the usual procedure and suppress the approximation error by using small discrete times. We then simply apply the EnKF and PFs originally developed for nonlinear discrete-time models to the discretized system models, yielding the non-Gaussian filtering algorithms. Since the nonlinear problems generally make the states non-Gaussian as time proceeds, these non-Gaussian filters are promising for improving estimation accuracy. Their filtering performance is evaluated using two benchmark simulation models and compared with the performance of existing Gaussian filters, such as extended and unscented Kalman filters, and with that estimated by using the recursive Cramér-Rao lower bounds. |
| first_indexed | 2024-03-11T18:39:37Z |
| format | Article |
| id | doaj.art-2efacb23db9848a4804c48f975ea7567 |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:39:37Z |
| publishDate | 2017-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-2efacb23db9848a4804c48f975ea75672023-10-12T13:43:54ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702017-03-01102536110.9746/jcmsi.10.5312103110Non-Gaussian Filters for Nonlinear Continuous-Discrete ModelsMasaya Murata0Kaoru Hiramatsu1NTT Communication Science LaboratoriesNTT Communication Science LaboratoriesIn this paper, we propose using an ensemble Kalman filter (EnKF) and particle filters (PFs) to obtain superior state estimation accuracy for nonlinear continuous-discrete models. We discretize the Ito-type stochastic differential system model by means of the usual procedure and suppress the approximation error by using small discrete times. We then simply apply the EnKF and PFs originally developed for nonlinear discrete-time models to the discretized system models, yielding the non-Gaussian filtering algorithms. Since the nonlinear problems generally make the states non-Gaussian as time proceeds, these non-Gaussian filters are promising for improving estimation accuracy. Their filtering performance is evaluated using two benchmark simulation models and compared with the performance of existing Gaussian filters, such as extended and unscented Kalman filters, and with that estimated by using the recursive Cramér-Rao lower bounds.http://dx.doi.org/10.9746/jcmsi.10.53nonlinear continuous-discrete modelnon-gaussian filterimportance selectionsimulationsrecursive cramér-rao lower bounds |
| spellingShingle | Masaya Murata Kaoru Hiramatsu Non-Gaussian Filters for Nonlinear Continuous-Discrete Models SICE Journal of Control, Measurement, and System Integration nonlinear continuous-discrete model non-gaussian filter importance selection simulations recursive cramér-rao lower bounds |
| title | Non-Gaussian Filters for Nonlinear Continuous-Discrete Models |
| title_full | Non-Gaussian Filters for Nonlinear Continuous-Discrete Models |
| title_fullStr | Non-Gaussian Filters for Nonlinear Continuous-Discrete Models |
| title_full_unstemmed | Non-Gaussian Filters for Nonlinear Continuous-Discrete Models |
| title_short | Non-Gaussian Filters for Nonlinear Continuous-Discrete Models |
| title_sort | non gaussian filters for nonlinear continuous discrete models |
| topic | nonlinear continuous-discrete model non-gaussian filter importance selection simulations recursive cramér-rao lower bounds |
| url | http://dx.doi.org/10.9746/jcmsi.10.53 |
| work_keys_str_mv | AT masayamurata nongaussianfiltersfornonlinearcontinuousdiscretemodels AT kaoruhiramatsu nongaussianfiltersfornonlinearcontinuousdiscretemodels |