Enhanced Navigation Precision Through Interaction Multiple Filtering: Integrating Invariant and Extended Kalman Filters
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".