Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF
In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a...
| Format: | Article |
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| Language: | zho |
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EDP Sciences
2020-08-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p806/jnwpu2020384p806.html |
| _version_ | 1797421043114049536 |
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| collection | DOAJ |
| description | In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method. |
| first_indexed | 2024-03-09T07:11:14Z |
| format | Article |
| id | doaj.art-46a8ff2b73ad43d99756eb3cf435bb2d |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-09T07:11:14Z |
| publishDate | 2020-08-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-46a8ff2b73ad43d99756eb3cf435bb2d2023-12-03T09:04:07ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252020-08-0138480681310.1051/jnwpu/20203840806jnwpu2020384p806Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKFIn the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method.https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p806/jnwpu2020384p806.htmladaptive kalman filtertwo stage kalman filterunscented kalman filterinertial measurement unitfault diagnosisrandom walk modelsimulation experiment |
| spellingShingle | Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF Xibei Gongye Daxue Xuebao adaptive kalman filter two stage kalman filter unscented kalman filter inertial measurement unit fault diagnosis random walk model simulation experiment |
| title | Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF |
| title_full | Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF |
| title_fullStr | Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF |
| title_full_unstemmed | Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF |
| title_short | Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF |
| title_sort | aircraft inertial measurement unit fault diagnosis based on adaptive two stage ukf |
| topic | adaptive kalman filter two stage kalman filter unscented kalman filter inertial measurement unit fault diagnosis random walk model simulation experiment |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p806/jnwpu2020384p806.html |