Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy
This article introduces the Sliding Innovation Lattice Filter (SILF), a robust extension of the Lattice Kalman Filter (LKF) that leverages sliding mode theory. SILF incorporates a sliding boundary layer in the measurement update formulation, enabling the filter innovation to slide within predefined...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of Signal Processing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10192922/ |
| _version_ | 1797692164157734912 |
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| author | Abolfazl Rahimnejad Javad Enayati Luigi Vanfretti Stephen Andrew Gadsden Mohammad AlShabi |
| author_facet | Abolfazl Rahimnejad Javad Enayati Luigi Vanfretti Stephen Andrew Gadsden Mohammad AlShabi |
| author_sort | Abolfazl Rahimnejad |
| collection | DOAJ |
| description | This article introduces the Sliding Innovation Lattice Filter (SILF), a robust extension of the Lattice Kalman Filter (LKF) that leverages sliding mode theory. SILF incorporates a sliding boundary layer in the measurement update formulation, enabling the filter innovation to slide within predefined upper and lower bounds. This enhances the robustness of SILF, making it resilient to model uncertainties and noise. Additionally, a derivative-free formulation of SILF is developed using statistical linear regression, eliminating the need for Jacobian calculations. To further improve accuracy, robustness, and convergence behavior in the presence of abrupt changes in system model/parameters, SILF is reinforced with the Iterated Sigma Point Filtering and Strong Tracking Filtering strategies, resulting in the Reinforced Lattice Kalman Filter (RLKF). The experimental findings for the estimation of distorted power waveforms illustrate the superior performance of SILF and RLKF over competing methods, especially when operating in scenarios characterized by model uncertainties and noisy environments. |
| first_indexed | 2024-03-12T02:24:37Z |
| format | Article |
| id | doaj.art-fb00183fa5004f009df0c42a872f5cae |
| institution | Directory Open Access Journal |
| issn | 2644-1322 |
| language | English |
| last_indexed | 2024-03-12T02:24:37Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Signal Processing |
| spelling | doaj.art-fb00183fa5004f009df0c42a872f5cae2023-09-05T23:01:58ZengIEEEIEEE Open Journal of Signal Processing2644-13222023-01-01441042310.1109/OJSP.2023.329855510192922Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation StrategyAbolfazl Rahimnejad0https://orcid.org/0000-0002-2203-9075Javad Enayati1https://orcid.org/0000-0001-6191-3063Luigi Vanfretti2https://orcid.org/0000-0002-4125-1055Stephen Andrew Gadsden3https://orcid.org/0000-0003-3749-0878Mohammad AlShabi4https://orcid.org/0000-0002-9540-3675Faculty of Engineering, McMaster University, Hamilton, ON, CanadaR&D Department, Mazinoor Lighting Industry, Babol, IranComputer and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, USAFaculty of Engineering, McMaster University, Hamilton, ON, CanadaDepartment of Mechanical and Nuclear Engineering, University of Sharjah, Sharjah, UAEThis article introduces the Sliding Innovation Lattice Filter (SILF), a robust extension of the Lattice Kalman Filter (LKF) that leverages sliding mode theory. SILF incorporates a sliding boundary layer in the measurement update formulation, enabling the filter innovation to slide within predefined upper and lower bounds. This enhances the robustness of SILF, making it resilient to model uncertainties and noise. Additionally, a derivative-free formulation of SILF is developed using statistical linear regression, eliminating the need for Jacobian calculations. To further improve accuracy, robustness, and convergence behavior in the presence of abrupt changes in system model/parameters, SILF is reinforced with the Iterated Sigma Point Filtering and Strong Tracking Filtering strategies, resulting in the Reinforced Lattice Kalman Filter (RLKF). The experimental findings for the estimation of distorted power waveforms illustrate the superior performance of SILF and RLKF over competing methods, especially when operating in scenarios characterized by model uncertainties and noisy environments.https://ieeexplore.ieee.org/document/10192922/Lattice Kalman filtervariable structure filteradaptive fading factoriterated filtering methodrobust estimators |
| spellingShingle | Abolfazl Rahimnejad Javad Enayati Luigi Vanfretti Stephen Andrew Gadsden Mohammad AlShabi Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy IEEE Open Journal of Signal Processing Lattice Kalman filter variable structure filter adaptive fading factor iterated filtering method robust estimators |
| title | Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy |
| title_full | Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy |
| title_fullStr | Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy |
| title_full_unstemmed | Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy |
| title_short | Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy |
| title_sort | reinforced lattice kalman filters a robust nonlinear estimation strategy |
| topic | Lattice Kalman filter variable structure filter adaptive fading factor iterated filtering method robust estimators |
| url | https://ieeexplore.ieee.org/document/10192922/ |
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