Continuous Action Learning Automata: A Strategy for Dynamic Optimization of Invariant Kalman Filter Covariances
Bibliographic record
Abstract
Accurate state estimation in autonomous vehicle navigation heavily relies on the precise tuning of Kalman filter covariance matrices. This paper introduces a novel application of Continuous Action Learning Automata (CALA) for the dynamic optimization of the measurement covariance matrix in a Left-Invariant Extended Kalman Filter (LIEKF). The proposed method leverages CALA’s reinforcement learning capabilities to fine-tune the filter parameters in response to environmental feedback adaptively. Integrating CALA with LIEKF, especially when augmented with Global Navigation Satellite System (GNSS) corrections, enhances the filter’s robustness and reliability in urban navigation tasks. Experimental results demonstrate that the CALA-enhanced LIEKF significantly outperforms traditional static methods, achieving lower mean absolute errors and improved accuracy during GNSS outages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".