An Efficient Tuning Framework for Kalman Filter Parameter Optimization using Design of Experiments and Genetic Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Extended Kalman Filter (EKF) is currently a dominant method of sensor fusion used for navigation of mobile devices, robotics and autonomous vehicles. One navigation system involves the EKF fusion of an Inertial Navigation System (INS) with a Global Navigation Satellite System (GNSS) to perform 3D pose estimation, which is essential to practical applications like autonomous vehicles and UAVs. This system involves using the INS to predict pose changes and uses GNSS measurements when available to correct for estimation drifts and sensor errors. The performance of the state estimation is heavily dependent on the accurate selection of EKF parameters, leading to the optimal selection of parameters being a critical factor in the design and use of the EKF. 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 a Design of Experiment (DoE) technique to efficiently optimize EKF parameters. This framework has been implemented in MATLAB and tested using simulations and real data. The results demonstrate that GA tuned parameters increases accuracy substantially, and that the DoE technique consistently improves the convergence behavior of the GA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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