Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight Environment
This study introduces a pose estimation algorithm integrating an Inertial Navigation System (INS) with an Alternating Current (AC) magnetic field-based navigation system, referred to as the Magnetic Positioning System (MPS), evaluated using a 6 Degrees of Freedom (DoF) drone. The study addresses sig...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/12/3/228 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849392770894004224 |
|---|---|
| author | Juyoung Seo Dongha Kwon Byungjin Lee Sangkyung Sung |
| author_facet | Juyoung Seo Dongha Kwon Byungjin Lee Sangkyung Sung |
| author_sort | Juyoung Seo |
| collection | DOAJ |
| description | This study introduces a pose estimation algorithm integrating an Inertial Navigation System (INS) with an Alternating Current (AC) magnetic field-based navigation system, referred to as the Magnetic Positioning System (MPS), evaluated using a 6 Degrees of Freedom (DoF) drone. The study addresses significant challenges such as the magnetic vector distortions and model uncertainties caused by motor noise, which degrade attitude estimation and limit the effectiveness of traditional Extended Kalman Filter (EKF)-based fusion methods. To mitigate these issues, a Tightly Coupled Unscented Kalman Filter (TC UKF) was developed to enhance robustness and navigation accuracy in dynamic environments. The proposed Unscented Kalman Filter (UKF) demonstrated a superior attitude estimation performance within a 6 m coil spacing area, outperforming both the MPS 3D LS (Least Squares) and EKF-based approaches. Furthermore, hyperparameters such as alpha, beta, and kappa were optimized using the Sequential Importance Resampling (SIR) process of the Particle Filter. This adaptive hyperparameter adjustment achieved improved navigation results compared to the default UKF settings, particularly in environments with high model uncertainty. |
| format | Article |
| id | doaj-art-667eafd8beb44066976d69e0864b92eb |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-667eafd8beb44066976d69e0864b92eb2025-08-20T03:40:42ZengMDPI AGAerospace2226-43102025-03-0112322810.3390/aerospace12030228Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight EnvironmentJuyoung Seo0Dongha Kwon1Byungjin Lee2Sangkyung Sung3Department of Aerospace Information Engineering, Konkuk University, Seoul 05029, Republic of KoreaDepartment of Aerospace Information Engineering, Konkuk University, Seoul 05029, Republic of KoreaDepartment of Mechanical and Aerospace Engineering, Konkuk University, Seoul 05029, Republic of KoreaDepartment of Mechanical and Aerospace Engineering, Konkuk University, Seoul 05029, Republic of KoreaThis study introduces a pose estimation algorithm integrating an Inertial Navigation System (INS) with an Alternating Current (AC) magnetic field-based navigation system, referred to as the Magnetic Positioning System (MPS), evaluated using a 6 Degrees of Freedom (DoF) drone. The study addresses significant challenges such as the magnetic vector distortions and model uncertainties caused by motor noise, which degrade attitude estimation and limit the effectiveness of traditional Extended Kalman Filter (EKF)-based fusion methods. To mitigate these issues, a Tightly Coupled Unscented Kalman Filter (TC UKF) was developed to enhance robustness and navigation accuracy in dynamic environments. The proposed Unscented Kalman Filter (UKF) demonstrated a superior attitude estimation performance within a 6 m coil spacing area, outperforming both the MPS 3D LS (Least Squares) and EKF-based approaches. Furthermore, hyperparameters such as alpha, beta, and kappa were optimized using the Sequential Importance Resampling (SIR) process of the Particle Filter. This adaptive hyperparameter adjustment achieved improved navigation results compared to the default UKF settings, particularly in environments with high model uncertainty.https://www.mdpi.com/2226-4310/12/3/228unscented Kalman filtertightly coupleddronemagnetic positioning systemextended Kalman filternavigation |
| spellingShingle | Juyoung Seo Dongha Kwon Byungjin Lee Sangkyung Sung Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight Environment Aerospace unscented Kalman filter tightly coupled drone magnetic positioning system extended Kalman filter navigation |
| title | Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight Environment |
| title_full | Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight Environment |
| title_fullStr | Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight Environment |
| title_full_unstemmed | Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight Environment |
| title_short | Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight Environment |
| title_sort | robust mps ins ukf integration and sir based hyperparameter estimation in a 3d flight environment |
| topic | unscented Kalman filter tightly coupled drone magnetic positioning system extended Kalman filter navigation |
| url | https://www.mdpi.com/2226-4310/12/3/228 |
| work_keys_str_mv | AT juyoungseo robustmpsinsukfintegrationandsirbasedhyperparameterestimationina3dflightenvironment AT donghakwon robustmpsinsukfintegrationandsirbasedhyperparameterestimationina3dflightenvironment AT byungjinlee robustmpsinsukfintegrationandsirbasedhyperparameterestimationina3dflightenvironment AT sangkyungsung robustmpsinsukfintegrationandsirbasedhyperparameterestimationina3dflightenvironment |