Improved IMU/GNSS EKF Fusion Using XGBoost Machine Learning Algorithm
Bibliographic record
Abstract
For years, Inertial Measurement Unit (IMU) and Global Positioning System (GPS) have been playing a crucial role in navigation systems. The emergence of inexpensive IMU sensors has offered a lightweight alternative, yet they suffer from larger errors that build up gradually, leading to drift errors in navigation. Traditionally, IMUs are combined with GPS to ensure stable and accurate navigation. However, this integration fails during challenging GPS environments. This work introduces an inventive approach to alleviate navigation drifts by eradicating IMU errors through the utilization of the Extreme Gradient Boosting (XGBoost) algorithm. Unlike traditional approaches relying on high-end IMUs, this paper utilizes Inverse Kinematics to obtain pseudo-clean IMU training data from PVA values estimated by the EKF when GPS signals are available. The proposed approach is evaluated using both simulated and real-world datasets across various GPS outage durations, demonstrating significant enhancements in PVA estimation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".