Minimax Regret filter for uncertainty Single-Input Single-Output systems: simulation study
The Kalman filter, widely used since its introduction in 1960, assumes Gaussian random disturbances. However, this assumption can be inappropriate in non-Gaussian contexts, leading to suboptimal performance. Researchers have proposed robust filters like minimax filters to address this limitation, b...
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| Format: | Article |
| Language: | English |
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Universidad de Antioquia
2024-04-01
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| Series: | Revista Facultad de Ingeniería Universidad de Antioquia |
| Subjects: | |
| Online Access: | https://revistas.udea.edu.co/index.php/ingenieria/article/view/353535 |
| _version_ | 1827293220885757952 |
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| author | José Perea-Arango Piotr Graczyk Juan Pablo Fernández Gutiérrez |
| author_facet | José Perea-Arango Piotr Graczyk Juan Pablo Fernández Gutiérrez |
| author_sort | José Perea-Arango |
| collection | DOAJ |
| description |
The Kalman filter, widely used since its introduction in 1960, assumes Gaussian random disturbances. However, this assumption can be inappropriate in non-Gaussian contexts, leading to suboptimal performance. Researchers have proposed robust filters like minimax filters to address this limitation, but these filters can overly conservative estimates. This research introduces a novel approach that combines unknown-but-bounded dynamics for the state process and stochastic processes for the measurement equation along with a Minimax Regret framework to improve state estimation in one-dimensional linear dynamic models. We evaluate the proposed method through two simulation studies. The first study optimizes the hyperparameter value using Grid Search. In contrast, the second compares the performance of the proposed method with conventional methods, including the Kalman filter and a robust version of the RobKF filter implemented in R software, using a suitable performance metric such as mean squared error. The results demonstrate the superiority of the proposed algorithm.
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| first_indexed | 2024-04-24T13:21:36Z |
| format | Article |
| id | doaj.art-85b2418adbba4e1280c549b94971fb57 |
| institution | Directory Open Access Journal |
| issn | 0120-6230 2422-2844 |
| language | English |
| last_indexed | 2024-04-24T13:21:36Z |
| publishDate | 2024-04-01 |
| publisher | Universidad de Antioquia |
| record_format | Article |
| series | Revista Facultad de Ingeniería Universidad de Antioquia |
| spelling | doaj.art-85b2418adbba4e1280c549b94971fb572024-04-04T14:16:45ZengUniversidad de AntioquiaRevista Facultad de Ingeniería Universidad de Antioquia0120-62302422-28442024-04-0110.17533/udea.redin.20240412Minimax Regret filter for uncertainty Single-Input Single-Output systems: simulation studyJosé Perea-Arango0Piotr Graczyk1Juan Pablo Fernández Gutiérrez2Empresas Públicas de MedellínUniversité d'AngersUniversidad de Medellín The Kalman filter, widely used since its introduction in 1960, assumes Gaussian random disturbances. However, this assumption can be inappropriate in non-Gaussian contexts, leading to suboptimal performance. Researchers have proposed robust filters like minimax filters to address this limitation, but these filters can overly conservative estimates. This research introduces a novel approach that combines unknown-but-bounded dynamics for the state process and stochastic processes for the measurement equation along with a Minimax Regret framework to improve state estimation in one-dimensional linear dynamic models. We evaluate the proposed method through two simulation studies. The first study optimizes the hyperparameter value using Grid Search. In contrast, the second compares the performance of the proposed method with conventional methods, including the Kalman filter and a robust version of the RobKF filter implemented in R software, using a suitable performance metric such as mean squared error. The results demonstrate the superiority of the proposed algorithm. https://revistas.udea.edu.co/index.php/ingenieria/article/view/353535Minimax regret approachUnknown-but-boundedUnknown distribution errorGrid hyperparameter optimization |
| spellingShingle | José Perea-Arango Piotr Graczyk Juan Pablo Fernández Gutiérrez Minimax Regret filter for uncertainty Single-Input Single-Output systems: simulation study Revista Facultad de Ingeniería Universidad de Antioquia Minimax regret approach Unknown-but-bounded Unknown distribution error Grid hyperparameter optimization |
| title | Minimax Regret filter for uncertainty Single-Input Single-Output systems: simulation study |
| title_full | Minimax Regret filter for uncertainty Single-Input Single-Output systems: simulation study |
| title_fullStr | Minimax Regret filter for uncertainty Single-Input Single-Output systems: simulation study |
| title_full_unstemmed | Minimax Regret filter for uncertainty Single-Input Single-Output systems: simulation study |
| title_short | Minimax Regret filter for uncertainty Single-Input Single-Output systems: simulation study |
| title_sort | minimax regret filter for uncertainty single input single output systems simulation study |
| topic | Minimax regret approach Unknown-but-bounded Unknown distribution error Grid hyperparameter optimization |
| url | https://revistas.udea.edu.co/index.php/ingenieria/article/view/353535 |
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