Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance
The uncertain disturbance in the system signals can lead to biased state estimates and, in turn, can lead to deterioration in the performance of state estimation for a nonlinear dynamic system. In order to address these issues, this paper develops an adaptive fitting H-infinity filter (AFHF) based m...
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doaj-37363f2d5c7947538f8bd8a5e8df50002021-03-30T02:11:39ZengIEEEIEEE Access2169-35362020-01-018761437615710.1109/ACCESS.2020.29887939072090Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System DisturbanceJuan Xia0https://orcid.org/0000-0001-8391-5262Shesheng Gao1https://orcid.org/0000-0002-7980-9085Yongmin Zhong2https://orcid.org/0000-0002-0105-9296Xiaomin Qi3https://orcid.org/0000-0001-6343-6707Guo Li4https://orcid.org/0000-0002-0170-2341Yang Liu5https://orcid.org/0000-0002-4308-2195School of Automatics, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automatics, Northwestern Polytechnical University, Xi’an, ChinaSchool of Engineering, RMIT University, Bundoora, VIC, AustraliaDepartment of Electrical and Computer Engineering, COMSATS University Islamabad at Abbottabad Campus, Abbottabad, PakistanSchool of Automatics, Northwestern Polytechnical University, Xi’an, ChinaChina Jikan Research Ins titute of Engineering Investigations and Design Co., Ltd., Xi’an, ChinaThe uncertain disturbance in the system signals can lead to biased state estimates and, in turn, can lead to deterioration in the performance of state estimation for a nonlinear dynamic system. In order to address these issues, this paper develops an adaptive fitting H-infinity filter (AFHF) based moving-window by combining the novel noise estimator with fitting H-infinity filtering. Specifically speaking, the novel noise estimator is designed to estimate the process and measurement noise characteristics during a fixed window epoch on the basic of the moving-window technique. Subsequently, the noise characteristics at each window epoch is regarded as the input noise means and covariances of fitting H-infinity filtering at next epoch. Further, the attenuation level is adaptively calculated at each time step to change the structure of AFHF. The Monte-Carlo simulations and INS/GPS integrated navigation experiments are set up for the sake of verifying the superior performance of the proposed filtering with uncertain disturbances.https://ieeexplore.ieee.org/document/9072090/Robust estimationadaptive fitting H-infinity filteruncertain system disturbancethe noise estimator |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Juan Xia Shesheng Gao Yongmin Zhong Xiaomin Qi Guo Li Yang Liu |
spellingShingle |
Juan Xia Shesheng Gao Yongmin Zhong Xiaomin Qi Guo Li Yang Liu Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance IEEE Access Robust estimation adaptive fitting H-infinity filter uncertain system disturbance the noise estimator |
author_facet |
Juan Xia Shesheng Gao Yongmin Zhong Xiaomin Qi Guo Li Yang Liu |
author_sort |
Juan Xia |
title |
Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance |
title_short |
Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance |
title_full |
Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance |
title_fullStr |
Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance |
title_full_unstemmed |
Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance |
title_sort |
moving-window-based adaptive fitting h-infinity filter for the nonlinear system disturbance |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The uncertain disturbance in the system signals can lead to biased state estimates and, in turn, can lead to deterioration in the performance of state estimation for a nonlinear dynamic system. In order to address these issues, this paper develops an adaptive fitting H-infinity filter (AFHF) based moving-window by combining the novel noise estimator with fitting H-infinity filtering. Specifically speaking, the novel noise estimator is designed to estimate the process and measurement noise characteristics during a fixed window epoch on the basic of the moving-window technique. Subsequently, the noise characteristics at each window epoch is regarded as the input noise means and covariances of fitting H-infinity filtering at next epoch. Further, the attenuation level is adaptively calculated at each time step to change the structure of AFHF. The Monte-Carlo simulations and INS/GPS integrated navigation experiments are set up for the sake of verifying the superior performance of the proposed filtering with uncertain disturbances. |
topic |
Robust estimation adaptive fitting H-infinity filter uncertain system disturbance the noise estimator |
url |
https://ieeexplore.ieee.org/document/9072090/ |
work_keys_str_mv |
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