Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman Filters
High-precision navigation solutions are essential requirements for various industries, especially the autonomous robotics industry. Inertial navigation systems (INS) are the prime source of navigation information for these applications, while global navigation satellite system (GNSS) measurements ac...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10759648/ |
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| author | Maher Tarek Syed Tariq Shah Shady Zahran Eyad S. Oda Mostafa M. Ahmed Ahmad Almogren Mahmoud A. Shawky Sherif F. Nafea |
| author_facet | Maher Tarek Syed Tariq Shah Shady Zahran Eyad S. Oda Mostafa M. Ahmed Ahmad Almogren Mahmoud A. Shawky Sherif F. Nafea |
| author_sort | Maher Tarek |
| collection | DOAJ |
| description | High-precision navigation solutions are essential requirements for various industries, especially the autonomous robotics industry. Inertial navigation systems (INS) are the prime source of navigation information for these applications, while global navigation satellite system (GNSS) measurements act as an aided source to bound INS drift and provide global positioning. However, GNSS availability cannot always be guaranteed, leading to degradation in INS performance. Several filters have been used for INS/GNSS fusion, including the invariant extended Kalman filter (IEKF) and extended Kalman filter (EKF). The IEKF uses Lie group mathematics to preserve system symmetries and handles non-linearities effectively but suffers from rapid divergence during GNSS outages due to its reliance on bounding constraints. In contrast, the EKF is valued for its simplicity and efficiency, but it struggles with high non-linearities, causing the degradation of the accuracy over time, especially during GNSS outages. To overcome these issues, we propose a novel interaction multiple filtering (IMF) technique that integrates both filters’ state estimations based on the Markov chain instead of switching between their outputs. Experimental results demonstrate the effectiveness of the proposed approach, showing improved navigation accuracy during GNSS availability compared to EKF by 13.5% and 1.8% when compared to the IEKF. The improvement when comparing the proposed algorithm during challenging environments (GNSS unavailability) with EKF reached 93% and 96% compared to IEKF, proving its robustness in challenging environments. |
| format | Article |
| id | doaj-art-7bceb1f4f09b47228efbba2e044b62ce |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7bceb1f4f09b47228efbba2e044b62ce2025-08-20T02:06:50ZengIEEEIEEE Access2169-35362024-01-011217535717537410.1109/ACCESS.2024.350390110759648Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman FiltersMaher Tarek0https://orcid.org/0009-0009-8674-3836Syed Tariq Shah1https://orcid.org/0000-0003-4722-1786Shady Zahran2https://orcid.org/0000-0003-2415-3119Eyad S. Oda3https://orcid.org/0000-0003-3024-5108Mostafa M. Ahmed4Ahmad Almogren5https://orcid.org/0000-0002-8253-9709Mahmoud A. Shawky6https://orcid.org/0000-0003-3393-8460Sherif F. Nafea7https://orcid.org/0000-0002-5344-2860Control and Navigation Department, Technical Research and Development Center, Cairo, EgyptSchool of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.College of Computing and IT, Arab Academy for Science, Technology & Maritime Transport, Cairo, EgyptElectrical Engineering Department, Suez Canal University, Ismailia, EgyptDepartment of Geomatics, University of Calgary, Calgary, AB, CanadaDepartment of Computer Science, College of Computer Information Sciences, King Saud University, Riyadh, Saudi ArabiaJames Watt School of Engineering, University of Glasgow, Glasgow, U.K.Electrical Engineering Department, Suez Canal University, Ismailia, EgyptHigh-precision navigation solutions are essential requirements for various industries, especially the autonomous robotics industry. Inertial navigation systems (INS) are the prime source of navigation information for these applications, while global navigation satellite system (GNSS) measurements act as an aided source to bound INS drift and provide global positioning. However, GNSS availability cannot always be guaranteed, leading to degradation in INS performance. Several filters have been used for INS/GNSS fusion, including the invariant extended Kalman filter (IEKF) and extended Kalman filter (EKF). The IEKF uses Lie group mathematics to preserve system symmetries and handles non-linearities effectively but suffers from rapid divergence during GNSS outages due to its reliance on bounding constraints. In contrast, the EKF is valued for its simplicity and efficiency, but it struggles with high non-linearities, causing the degradation of the accuracy over time, especially during GNSS outages. To overcome these issues, we propose a novel interaction multiple filtering (IMF) technique that integrates both filters’ state estimations based on the Markov chain instead of switching between their outputs. Experimental results demonstrate the effectiveness of the proposed approach, showing improved navigation accuracy during GNSS availability compared to EKF by 13.5% and 1.8% when compared to the IEKF. The improvement when comparing the proposed algorithm during challenging environments (GNSS unavailability) with EKF reached 93% and 96% compared to IEKF, proving its robustness in challenging environments.https://ieeexplore.ieee.org/document/10759648/Arial vehiclesKalman filtersGNSS outagesinertial navigation systemsinteraction multiple filteringMarkov chain |
| spellingShingle | Maher Tarek Syed Tariq Shah Shady Zahran Eyad S. Oda Mostafa M. Ahmed Ahmad Almogren Mahmoud A. Shawky Sherif F. Nafea Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman Filters IEEE Access Arial vehicles Kalman filters GNSS outages inertial navigation systems interaction multiple filtering Markov chain |
| title | Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman Filters |
| title_full | Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman Filters |
| title_fullStr | Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman Filters |
| title_full_unstemmed | Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman Filters |
| title_short | Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman Filters |
| title_sort | enhanced navigation precision through interaction multiple filtering integrating invariant and extended kalman filters |
| topic | Arial vehicles Kalman filters GNSS outages inertial navigation systems interaction multiple filtering Markov chain |
| url | https://ieeexplore.ieee.org/document/10759648/ |
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