Doppler tracking and compensation for underwater acoustic channels using shift-orthogonal pilot sequences
Bibliographic record
Abstract
A novel time-domain Doppler tracker and compensator, using shift-orthogonal OFDM pilot sequences, was developed, and assessed in simulations and early in-water trials. The objective is to address large Mach numbers like those experienced in communicating autonomous underwater vehicles (AUV). To start, the Doppler estimator extracts pilots in the received signal to produce Mach number estimates for every received signal sample. Given the large number of estimator samples, a Doppler tracker is proposed to reduce the number and simultaneously track Mach number variations. This novel tracker reduces the computational load on the compensator and makes it possible to implement on AUVs. Then, the Doppler compensator's first stage resamples the received signal using the Mach number estimates from the tracker's output. Since the estimates vary with time, the resampling performed is time-varying. Then, the final stage estimates the residual Doppler shift from the estimator and eliminates it with a phase rotation. The proposed tracker and compensator receiver outperform most existing compensators as measured through the mean-squared error, is more computationally efficient and addresses higher Mach numbers. Each element of the proposed receiver is tested in simulations and the results are shown to agree with theory. Comprehensive at-sea trials are next.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".