A Kalman filter for the navigation of remotely operated vehicles
Why this work is in the frame
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Bibliographic record
Abstract
A Kalman based asynchronous data fusion algorithm for the navigation of a tethered remotely operated underwater vehicle is presented. Using a non-linear dynamic simulation of the tethered ROV system, the performance of the Kalman filter is measured for various motion sensor combinations. The sensor suite tested includes a Doppler velocity log, fiber-optic gyrocompass, depth sensor and an ultra-short baseline position system. Provided the gyrocompass functions properly, the study shows that an extended Kalman filter which uses a complete model of the ROV, including, drag, tether and thruster effects, does outperform a constant velocity model in instances of sensor drop out. The positioning error is reduced by 20% in these instances. It is found that the ultra-short baseline system is the driving factor in the smoothness of the results.
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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