Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on “the Output Regulation and the Kalman Filter as the Signal Generator”
The authors recently introduced a Kalman filter-based approach for estimating noise-contaminated unmodeled signals. However, in that work, the dimension of the Kalman filter depends on the complexity of the signals being estimated, which are not known a priori. This typically requires a trial-and-er...
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858725/ |
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| author | Jesus Alberto Meda-Campana Juan Carlos Garcia-Hernandez Rodolfo Daniel Velazquez-Sanchez Luis Alberto Paramo-Carranza Tonatiuh Hernandez-Cortes Ricardo Tapia-Herrera |
| author_facet | Jesus Alberto Meda-Campana Juan Carlos Garcia-Hernandez Rodolfo Daniel Velazquez-Sanchez Luis Alberto Paramo-Carranza Tonatiuh Hernandez-Cortes Ricardo Tapia-Herrera |
| author_sort | Jesus Alberto Meda-Campana |
| collection | DOAJ |
| description | The authors recently introduced a Kalman filter-based approach for estimating noise-contaminated unmodeled signals. However, in that work, the dimension of the Kalman filter depends on the complexity of the signals being estimated, which are not known a priori. This typically requires a trial-and-error process, often resulting in an excessively large filter dimension that complicates the method’s practical application. To overcome this problem, this paper proposes a solution for estimating complex, unmodeled, and noise-contaminated signals while simultaneously obtaining a low-dimensional dynamical model whose output approximates the actual noise-free signal. This is achieved by incorporating a fuzzy error covariance matrix into the standard linear Kalman filter algorithm. The proposed modification prevents the Kalman filter estimate from drifting away from the true signal and provides an alternative to the traditional abrupt resetting of the error covariance matrix. Notably, this modification can be seen as a soft reset of certain initial conditions and, as demonstrated, does not compromise the Kalman filter’s stability. Furthermore, the result shows that incorporating simple fuzzy logic concepts may substantially enhance the performance of some established algorithms. |
| format | Article |
| id | doaj-art-f2062ac6482342c18b90934db7784516 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f2062ac6482342c18b90934db77845162025-02-11T00:01:05ZengIEEEIEEE Access2169-35362025-01-0113240412405210.1109/ACCESS.2025.353749710858725Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on “the Output Regulation and the Kalman Filter as the Signal Generator”Jesus Alberto Meda-Campana0https://orcid.org/0000-0001-6557-2745Juan Carlos Garcia-Hernandez1https://orcid.org/0009-0008-4795-0552Rodolfo Daniel Velazquez-Sanchez2Luis Alberto Paramo-Carranza3Tonatiuh Hernandez-Cortes4https://orcid.org/0000-0002-1966-2755Ricardo Tapia-Herrera5https://orcid.org/0000-0003-0674-9082SEPI-ESIME Zacatenco, Instituto Politécnico Nacional, Ciudad de México, MexicoSEPI-ESIME Zacatenco, Instituto Politécnico Nacional, Ciudad de México, MexicoSEPI-ESIME Zacatenco, Instituto Politécnico Nacional, Ciudad de México, MexicoESIME Zacatenco, Instituto Politécnico Nacional, Ciudad de México, MexicoDepartment of Mechatronics, Universidad Politécnica de Pachuca, Zempoala, MexicoCONAHCYT-SEPI-ESIME Zacatenco, Instituto Politécnico Nacional, Ciudad de México, MexicoThe authors recently introduced a Kalman filter-based approach for estimating noise-contaminated unmodeled signals. However, in that work, the dimension of the Kalman filter depends on the complexity of the signals being estimated, which are not known a priori. This typically requires a trial-and-error process, often resulting in an excessively large filter dimension that complicates the method’s practical application. To overcome this problem, this paper proposes a solution for estimating complex, unmodeled, and noise-contaminated signals while simultaneously obtaining a low-dimensional dynamical model whose output approximates the actual noise-free signal. This is achieved by incorporating a fuzzy error covariance matrix into the standard linear Kalman filter algorithm. The proposed modification prevents the Kalman filter estimate from drifting away from the true signal and provides an alternative to the traditional abrupt resetting of the error covariance matrix. Notably, this modification can be seen as a soft reset of certain initial conditions and, as demonstrated, does not compromise the Kalman filter’s stability. Furthermore, the result shows that incorporating simple fuzzy logic concepts may substantially enhance the performance of some established algorithms.https://ieeexplore.ieee.org/document/10858725/Kalman Filterfuzzy error covariance matrixunmodeled signals |
| spellingShingle | Jesus Alberto Meda-Campana Juan Carlos Garcia-Hernandez Rodolfo Daniel Velazquez-Sanchez Luis Alberto Paramo-Carranza Tonatiuh Hernandez-Cortes Ricardo Tapia-Herrera Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on “the Output Regulation and the Kalman Filter as the Signal Generator” IEEE Access Kalman Filter fuzzy error covariance matrix unmodeled signals |
| title | Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on “the Output Regulation and the Kalman Filter as the Signal Generator” |
| title_full | Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on “the Output Regulation and the Kalman Filter as the Signal Generator” |
| title_fullStr | Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on “the Output Regulation and the Kalman Filter as the Signal Generator” |
| title_full_unstemmed | Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on “the Output Regulation and the Kalman Filter as the Signal Generator” |
| title_short | Estimating Complex Signals With a Fuzzy-Enhanced Kalman Filter: A Note on “the Output Regulation and the Kalman Filter as the Signal Generator” |
| title_sort | estimating complex signals with a fuzzy enhanced kalman filter a note on x201c the output regulation and the kalman filter as the signal generator x201d |
| topic | Kalman Filter fuzzy error covariance matrix unmodeled signals |
| url | https://ieeexplore.ieee.org/document/10858725/ |
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