A Machine Learning Approach for Dead-Reckoning Navigation at Sea Using a Single Accelerometer
Bibliographic record
Abstract
Dead-reckoning (DR) navigation is used when Global Positioning System (GPS) reception is not available or its accuracy is not sufficient. At sea, DR requires the use of inertial sensors, usually a gyrocompass and an accelerometer, to estimate the orientation and distance traveled by the tracked object with respect to a reference coordinate system. In this paper, we consider the problem of DR navigation for vessels located close to or on the sea surface, where motion is caused by ocean waves. In such cases, the vessel pitch angle is fast time varying and its estimation by direct measurements of orientation is prone to drifts and noises of the gyroscope. Regarding this problem, we propose a method to compensate for the vessel pitch angle using a single acceleration sensor. Using a constraint expectation–maximization (EM) algorithm, our method classifies acceleration measurements into states of similar pitch angles. Subsequently, for each class, we project acceleration measurements into the reference coordinate system along the vessel heading direction, and obtain distance estimations by integrating the projected measurements. Results in both simulated and actual sea environments demonstrate that, by using only acceleration measurements, our method achieves accurate results.
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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 | 0.001 |
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".