Underwater Localization with Time-Synchronization and Propagation Speed Uncertainties
Bibliographic record
Abstract
Underwater acoustic localization (UWAL) is a key element in most underwater communication applications. The absence of GPS as well as the signal propagation environment makes UWAL similar to indoor localization. However, UWAL poses additional challenges. The propagation speed varies with depth, temperature, and salinity, anchor and unlocalized (UL) nodes cannot be assumed time-synchronized, and nodes are constantly moving due to ocean currents or self-motion. Taking these specific features of UWAL into account, in this paper, we describe a new sequential algorithm for joint time-synchronization and localization for underwater networks. The algorithm is based on packet exchanges between anchor and UL nodes, makes use of directional navigation systems employed in nodes to obtain accurate short-term motion estimates, and exploits the permanent motion of nodes. Our solution also allows self-evaluation of the localization accuracy. Using simulations, we compare our algorithm to two benchmark localization methods as well as to the Cramér-Rao bound (CBR). The results demonstrate that our algorithm achieves accurate localization using only two anchor nodes and outperforms the benchmark schemes when node synchronization and knowledge of propagation speed are not available. Moreover, we report results of a sea trial where we validated our algorithm in open sea.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".