Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation
In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise c...
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| Format: | Article |
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MDPI AG
2023-12-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/12/1023 |
| _version_ | 1797382466575532032 |
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| author | Assefinew Wondosen Yisak Debele Seung-Ki Kim Ha-Young Shi Bedada Endale Beom-Soo Kang |
| author_facet | Assefinew Wondosen Yisak Debele Seung-Ki Kim Ha-Young Shi Bedada Endale Beom-Soo Kang |
| author_sort | Assefinew Wondosen |
| collection | DOAJ |
| description | In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise covariance matrix (Q) and measurement noise covariance matrix (R) must be chosen correctly. The use of EKF has been challenging due to the need for an easy mechanism to select Q and R values. As a result, this research focused on developing an algorithm that can be easily applied to determine Q and R, allowing us to harness the full potential of EKF. Accordingly, an EKF innovation consistency statistics-driven Bayesian optimization algorithm was employed to achieve this goal. Q and R values were tuned until the expected result met the performance requirement for minimum error through improved measurement innovation consistency. The comprehensive results demonstrate that when the optimum Q and R, as tuned by the suggested technique, were used, the performance of the EKF significantly improved. |
| first_indexed | 2024-03-08T21:05:48Z |
| format | Article |
| id | doaj.art-b5a8aaf19a064f38a0082e5ee13579a6 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-08T21:05:48Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-b5a8aaf19a064f38a0082e5ee13579a62023-12-22T13:45:15ZengMDPI AGAerospace2226-43102023-12-011012102310.3390/aerospace10121023Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System EstimationAssefinew Wondosen0Yisak Debele1Seung-Ki Kim2Ha-Young Shi3Bedada Endale4Beom-Soo Kang5Department of Aerospace Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, Republic of KoreaIn various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise covariance matrix (Q) and measurement noise covariance matrix (R) must be chosen correctly. The use of EKF has been challenging due to the need for an easy mechanism to select Q and R values. As a result, this research focused on developing an algorithm that can be easily applied to determine Q and R, allowing us to harness the full potential of EKF. Accordingly, an EKF innovation consistency statistics-driven Bayesian optimization algorithm was employed to achieve this goal. Q and R values were tuned until the expected result met the performance requirement for minimum error through improved measurement innovation consistency. The comprehensive results demonstrate that when the optimum Q and R, as tuned by the suggested technique, were used, the performance of the EKF significantly improved.https://www.mdpi.com/2226-4310/10/12/1023extended Kalman filtercovariance tuningBayesian optimizationattitude and heading reference systemUAV |
| spellingShingle | Assefinew Wondosen Yisak Debele Seung-Ki Kim Ha-Young Shi Bedada Endale Beom-Soo Kang Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation Aerospace extended Kalman filter covariance tuning Bayesian optimization attitude and heading reference system UAV |
| title | Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation |
| title_full | Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation |
| title_fullStr | Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation |
| title_full_unstemmed | Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation |
| title_short | Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation |
| title_sort | bayesian optimization for fine tuning ekf parameters in uav attitude and heading reference system estimation |
| topic | extended Kalman filter covariance tuning Bayesian optimization attitude and heading reference system UAV |
| url | https://www.mdpi.com/2226-4310/10/12/1023 |
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