Accuracy of TWR-Based Ranging and Localization in Mobile Acoustic Underwater Networks
Bibliographic record
Abstract
Underwater robots require location information for autonomous navigation and even remote control. [3–1]Acoustic communication is the natural choice to cater for distance information to anchors with known position in an underwater environment. Additionally, it does not require the use of extra hardware, making it useful in cost-sensitive applications. Unfortunately, the acoustic channel is slow, adding considerable, but typically ignored, errors to distance measurements and, as a consequence, location estimates. Quantification of errors in realworld scenarios and field tests is difficult, if not impossible, unless expensive, special equipment is available. Therefore, we derive a detailed, yet comprehensible, mathematical model to obtain distance of a moving robot to one or many anchors and its real position. We identify the influencing factors and study the error of both distance measurements and self-localization. Our results indicate that compensation of robot movement is required for accurate self-localization.
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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 | 0.000 |
| 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 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".