A Two-Stage Residual Extended Kalman Filter using Extreme Gradient Boosting and Kolmogorov–Arnold Networks for terrain-aided Unmanned Aerial Vehicle Localization in Global Navigation Satellite System-denied environments.
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| Title: | A Two-Stage Residual Extended Kalman Filter using Extreme Gradient Boosting and Kolmogorov–Arnold Networks for terrain-aided Unmanned Aerial Vehicle Localization in Global Navigation Satellite System-denied environments. |
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| Authors: | Abdelkader, Mohamed1 (AUTHOR), Jarraya, Imen1 (AUTHOR) imenjarraya85@gmail.com, Gabr, Khaled1 (AUTHOR), Al-Batati, Abdulrahman S.1 (AUTHOR), AlMusalami, Abdullah1 (AUTHOR), Alahmed, Fatimah1 (AUTHOR), Boulila, Wadii1 (AUTHOR) |
| Source: | Engineering Applications of Artificial Intelligence. Jun2026, Vol. 173, pN.PAG-N.PAG. 1p. |
| Subjects: | Kalman filtering, Machine learning, Drone aircraft, Boosting algorithms, Digital elevation models, Multisensor data fusion |
| Geographic Terms: | Saudi Arabia |
| Abstract: | Unmanned Aerial Vehicles (UAVs) localization in Global Navigation Satellite Systems-denied environments faces significant challenges due to sensor limitations, environmental factors, and the absence of absolute positioning references. Visual-based methods degrade under sparse textures and illumination changes, while traditional sensor fusion approaches suffer from cumulative drift and poor adaptability to dynamic conditions. We propose the Two-Stage Residual Extended Kalman Filter (TSR-EKF), a hybrid framework that incorporates Machine Learning (ML)-based residual corrections into a probabilistic state estimator. The core novelty lies in a clear two-stage residual learning strategy: the approach employs eXtreme Gradient Boosting (XGBoost) for coarse bias removal from terrain-induced and inertial errors, followed by Dense Kolmogorov–Arnold Networks (DenseKAN) for fine nonlinear residual refinement. These corrections are fused with Inertial Measurement Unit velocities and terrain-referenced measurements within an Extended Kalman Filter framework without altering its standard probabilistic structure. We conducted experimental validation using seven UAV flight trajectories over the Taif region, Saudi Arabia. These tests were performed in high-fidelity Gazebo simulations using actual terrain Digital Elevation Models (DEM). The results demonstrate clear and measurable performance gains. The TSR-EKF achieves root-mean-square error (RMSE) reductions of 57.4% to 98.9% compared to traditional EKF methods while maintaining sub-6m positioning accuracy and real-time computational feasibility with processing times under 2.1 ms per update. The framework exhibits exceptional robustness to sparse measurement conditions, making it suitable for mission-critical UAV operations in contested environments. Overall, these results highlight TSR-EKF as a practical and effective solution that combines Kalman filtering reliability with ML adaptability for robust autonomous navigation. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192692579 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Two-Stage Residual Extended Kalman Filter using Extreme Gradient Boosting and Kolmogorov–Arnold Networks for terrain-aided Unmanned Aerial Vehicle Localization in Global Navigation Satellite System-denied environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Abdelkader%2C+Mohamed%22">Abdelkader, Mohamed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jarraya%2C+Imen%22">Jarraya, Imen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> imenjarraya85@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Gabr%2C+Khaled%22">Gabr, Khaled</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Al-Batati%2C+Abdulrahman+S%2E%22">Al-Batati, Abdulrahman S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22AlMusalami%2C+Abdullah%22">AlMusalami, Abdullah</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alahmed%2C+Fatimah%22">Alahmed, Fatimah</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Boulila%2C+Wadii%22">Boulila, Wadii</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Jun2026, Vol. 173, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+elevation+models%22">Digital elevation models</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Saudi+Arabia%22">Saudi Arabia</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Unmanned Aerial Vehicles (UAVs) localization in Global Navigation Satellite Systems-denied environments faces significant challenges due to sensor limitations, environmental factors, and the absence of absolute positioning references. Visual-based methods degrade under sparse textures and illumination changes, while traditional sensor fusion approaches suffer from cumulative drift and poor adaptability to dynamic conditions. We propose the Two-Stage Residual Extended Kalman Filter (TSR-EKF), a hybrid framework that incorporates Machine Learning (ML)-based residual corrections into a probabilistic state estimator. The core novelty lies in a clear two-stage residual learning strategy: the approach employs eXtreme Gradient Boosting (XGBoost) for coarse bias removal from terrain-induced and inertial errors, followed by Dense Kolmogorov–Arnold Networks (DenseKAN) for fine nonlinear residual refinement. These corrections are fused with Inertial Measurement Unit velocities and terrain-referenced measurements within an Extended Kalman Filter framework without altering its standard probabilistic structure. We conducted experimental validation using seven UAV flight trajectories over the Taif region, Saudi Arabia. These tests were performed in high-fidelity Gazebo simulations using actual terrain Digital Elevation Models (DEM). The results demonstrate clear and measurable performance gains. The TSR-EKF achieves root-mean-square error (RMSE) reductions of 57.4% to 98.9% compared to traditional EKF methods while maintaining sub-6m positioning accuracy and real-time computational feasibility with processing times under 2.1 ms per update. The framework exhibits exceptional robustness to sparse measurement conditions, making it suitable for mission-critical UAV operations in contested environments. Overall, these results highlight TSR-EKF as a practical and effective solution that combines Kalman filtering reliability with ML adaptability for robust autonomous navigation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.engappai.2026.114430 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Kalman filtering Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Drone aircraft Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Digital elevation models Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Saudi Arabia Type: general Titles: – TitleFull: A Two-Stage Residual Extended Kalman Filter using Extreme Gradient Boosting and Kolmogorov–Arnold Networks for terrain-aided Unmanned Aerial Vehicle Localization in Global Navigation Satellite System-denied environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Abdelkader, Mohamed – PersonEntity: Name: NameFull: Jarraya, Imen – PersonEntity: Name: NameFull: Gabr, Khaled – PersonEntity: Name: NameFull: Al-Batati, Abdulrahman S. – PersonEntity: Name: NameFull: AlMusalami, Abdullah – PersonEntity: Name: NameFull: Alahmed, Fatimah – PersonEntity: Name: NameFull: Boulila, Wadii IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09521976 Numbering: – Type: volume Value: 173 Titles: – TitleFull: Engineering Applications of Artificial Intelligence Type: main |
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