A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance
The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to impro...
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MDPI AG
2018-03-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/3/808 |
| _version_ | 1818007206569705472 |
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| author | Binqi Zheng Pengcheng Fu Baoqing Li Xiaobing Yuan |
| author_facet | Binqi Zheng Pengcheng Fu Baoqing Li Xiaobing Yuan |
| author_sort | Binqi Zheng |
| collection | DOAJ |
| description | The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results. |
| first_indexed | 2024-04-14T05:12:36Z |
| format | Article |
| id | doaj.art-7d5c508b113c4887b99019272738de60 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T05:12:36Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-7d5c508b113c4887b99019272738de602022-12-22T02:10:30ZengMDPI AGSensors1424-82202018-03-0118380810.3390/s18030808s18030808A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise CovarianceBinqi Zheng0Pengcheng Fu1Baoqing Li2Xiaobing Yuan3Science and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, ChinaScience and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, ChinaScience and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, ChinaScience and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, ChinaThe Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.http://www.mdpi.com/1424-8220/18/3/808Adaptive filterdata fusionrobust state estimationnonlinear systemuncertain noise covariance |
| spellingShingle | Binqi Zheng Pengcheng Fu Baoqing Li Xiaobing Yuan A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance Sensors Adaptive filter data fusion robust state estimation nonlinear system uncertain noise covariance |
| title | A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance |
| title_full | A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance |
| title_fullStr | A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance |
| title_full_unstemmed | A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance |
| title_short | A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance |
| title_sort | robust adaptive unscented kalman filter for nonlinear estimation with uncertain noise covariance |
| topic | Adaptive filter data fusion robust state estimation nonlinear system uncertain noise covariance |
| url | http://www.mdpi.com/1424-8220/18/3/808 |
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