Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics
This article proposes an enhanced fusion technique to improve the accuracy of the state estimation of a navigational system. The smooth variable structure filter (SVSF) is examined to estimate the system’s state under model uncertainty. Its combination with the unscented Kalman filter (UK...
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2024-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
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| Online Access: | https://ieeexplore.ieee.org/document/10477539/ |
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| author | Mohammad Avzayesh Wasim Al-Masri Mamoun F. Abdel-Hafez Mohammad AlShabi |
| author_facet | Mohammad Avzayesh Wasim Al-Masri Mamoun F. Abdel-Hafez Mohammad AlShabi |
| author_sort | Mohammad Avzayesh |
| collection | DOAJ |
| description | This article proposes an enhanced fusion technique to improve the accuracy of the state estimation of a navigational system. The smooth variable structure filter (SVSF) is examined to estimate the system’s state under model uncertainty. Its combination with the unscented Kalman filter (UKF) to acquire better navigational accuracy while being robust to the system’s modeling uncertainty is investigated. The proposed hybrid method is compared with the extended Kalman filter (EKF), the UKF, and the SVSF. The proposed algorithms fuse an inertial measurement unit (IMU) with the Global Positioning Systems (GPS) measurements to obtain the vehicle’s state. Experimental results are compared to a commercial off-the-shelf (COTS) solution. It is shown that all filtering strategies have similar performance in the absence of large-magnitude noise and model uncertainties. When injecting modeling uncertainties, the performance of the UKF degrades, and that of the EKF goes out of bounds. On the other hand, increasing the covariances of the measurement and dynamics noise sequences causes the path of the SVSF to become nonsmooth and roughly oscillates around the true path. The proposed integrated UK-SVSF algorithm achieves the following objectives: first, using the Kaman-based filter enhances the optimality of the filter to GPS/IMU dynamics and measurements noise. Second, using the UKF reduces the estimation error by eliminating the first-order linearization step. Finally, using the SVSF enhances the estimate’s robustness to model uncertainty. Results reveal that, in the presence of both large-magnitude noise and model uncertainties, the UK-SVSF gives an enhanced estimation performance. |
| format | Article |
| id | doaj-art-8825c32b2de144e08e85e2bae6635dfd |
| institution | Kabale University |
| issn | 2768-7236 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Instrumentation and Measurement |
| spelling | doaj-art-8825c32b2de144e08e85e2bae6635dfd2025-01-15T00:04:25ZengIEEEIEEE Open Journal of Instrumentation and Measurement2768-72362024-01-01311210.1109/OJIM.2024.337938610477539Improved-Performance Vehicle’s State Estimator Under Uncertain Model DynamicsMohammad Avzayesh0https://orcid.org/0000-0003-2747-1219Wasim Al-Masri1https://orcid.org/0000-0003-1320-6035Mamoun F. Abdel-Hafez2https://orcid.org/0000-0002-9010-4094Mohammad AlShabi3https://orcid.org/0000-0002-9540-3675Department of Mechanical Engineering, American University of Sharjah, Sharjah, UAEDepartment of Mechanical Engineering, American University of Sharjah, Sharjah, UAEDepartment of Mechanical Engineering, American University of Sharjah, Sharjah, UAEDepartment of Mechanical and Nuclear Engineering, University of Sharjah, Sharjah, UAEThis article proposes an enhanced fusion technique to improve the accuracy of the state estimation of a navigational system. The smooth variable structure filter (SVSF) is examined to estimate the system’s state under model uncertainty. Its combination with the unscented Kalman filter (UKF) to acquire better navigational accuracy while being robust to the system’s modeling uncertainty is investigated. The proposed hybrid method is compared with the extended Kalman filter (EKF), the UKF, and the SVSF. The proposed algorithms fuse an inertial measurement unit (IMU) with the Global Positioning Systems (GPS) measurements to obtain the vehicle’s state. Experimental results are compared to a commercial off-the-shelf (COTS) solution. It is shown that all filtering strategies have similar performance in the absence of large-magnitude noise and model uncertainties. When injecting modeling uncertainties, the performance of the UKF degrades, and that of the EKF goes out of bounds. On the other hand, increasing the covariances of the measurement and dynamics noise sequences causes the path of the SVSF to become nonsmooth and roughly oscillates around the true path. The proposed integrated UK-SVSF algorithm achieves the following objectives: first, using the Kaman-based filter enhances the optimality of the filter to GPS/IMU dynamics and measurements noise. Second, using the UKF reduces the estimation error by eliminating the first-order linearization step. Finally, using the SVSF enhances the estimate’s robustness to model uncertainty. Results reveal that, in the presence of both large-magnitude noise and model uncertainties, the UK-SVSF gives an enhanced estimation performance.https://ieeexplore.ieee.org/document/10477539/Extended Kalman filter (EKF)global positioning system (GPS)inertial navigation system (INS)sensor fusionsmooth variable structure filter (SVSF)unscented Kalman filter (UKF) |
| spellingShingle | Mohammad Avzayesh Wasim Al-Masri Mamoun F. Abdel-Hafez Mohammad AlShabi Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics IEEE Open Journal of Instrumentation and Measurement Extended Kalman filter (EKF) global positioning system (GPS) inertial navigation system (INS) sensor fusion smooth variable structure filter (SVSF) unscented Kalman filter (UKF) |
| title | Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics |
| title_full | Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics |
| title_fullStr | Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics |
| title_full_unstemmed | Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics |
| title_short | Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics |
| title_sort | improved performance vehicle x2019 s state estimator under uncertain model dynamics |
| topic | Extended Kalman filter (EKF) global positioning system (GPS) inertial navigation system (INS) sensor fusion smooth variable structure filter (SVSF) unscented Kalman filter (UKF) |
| url | https://ieeexplore.ieee.org/document/10477539/ |
| work_keys_str_mv | AT mohammadavzayesh improvedperformancevehiclex2019sstateestimatorunderuncertainmodeldynamics AT wasimalmasri improvedperformancevehiclex2019sstateestimatorunderuncertainmodeldynamics AT mamounfabdelhafez improvedperformancevehiclex2019sstateestimatorunderuncertainmodeldynamics AT mohammadalshabi improvedperformancevehiclex2019sstateestimatorunderuncertainmodeldynamics |