Resolving Velocity Ambiguity in Multifrequency, Pulse-to-Pulse Coherent Doppler Sonar
Bibliographic record
Abstract
Coherent Doppler sonar allows noninvasive velocity measurements and is suitable for both laboratory and field applications. The approach is particularly attractive in those environments where optical techniques are not suitable either because of power requirements or more critically, water turbidity. Notably, the technique has been employed successfully in oceanic and river boundary layer studies. However, the occurrence of range and velocity ambiguities limit the more general application of the technique. This paper introduces a method to overcome speed ambiguities by acquiring acoustic backscatter at two (or more) frequencies simultaneously with a broadband transmit pulse. The different frequencies have distinct velocity ambiguities allowing disambiguation of the velocity measurements. The approach is conceptually similar to the use of multiple transmit pulse rates but has the advantage that the data can be acquired simultaneously and so there is no loss in data rate. In addition, system geometry often restricts the allowed pulse repetition rate so that disambiguation using frequency is more flexible and more generally applicable. Theoretically, the effective ambiguity velocity of a dual-frequency system can be extended arbitrarily but phase noise in a practical system restricts the method to about a fivefold increase in ambiguity velocity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".