An efficient tuning framework for Kalman filter parameter optimization using design of experiments and genetic algorithms
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Bibliographic record
Abstract
<h3>Abstract</h3> The Extended Kalman Filter (EKF) is currently a dominant sensor fusion method for mobile devices, robotics, and autonomous vehicles. Its performance heavily depends on the selection of EKF parameters. Therefore, the optimal selection of parameters is a critical factor in EKF design and use. In this paper, a methodical and efficient method of EKF parameter tuning is presented. The tuning framework uses nominal parameters generated by Gauss Markov (GM) and Allan Variance (AV) methods that are tuned by Genetic Algorithms (GA) accelerated by Design of Experiments (DoE). This framework has been implemented in MATLAB and tested using simulations and real data under a tightly coupled EKF that fuses IMU and GNSS measurements of a self-driving car provided by the Blackberry QNX company. The results demonstrate that GA-tuned parameters increase accuracy substantially over nominally tuned parameters, and that the DoE technique consistently improves the convergence behavior of the GA.
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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 | 0.000 |
| 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 it