Simultaneous Velocity Ambiguity Resolution and Noise Suppression for Multifrequency Coherent Doppler Sonar
Bibliographic record
Abstract
Abstract Coherent Doppler sonar is a useful tool for noninvasive measurement of ocean currents, sediment transport, and turbulence in coastal environments. Various methods have been proposed to separately address two of its inherent limitations: velocity ambiguity and measurement noise. However, in energetic turbulent flows, both factors may be present simultaneously. The presence of measurement noise complicates velocity ambiguity resolution, and conversely velocity ambiguity presents a challenge for existing noise suppression methods. A velocity estimator based on maximum a posteriori (MAP) estimation has been developed to resolve velocity ambiguity and suppress measurement noise simultaneously rather than separately. The estimator optimally combines measurements from multiple acoustic carrier frequencies and multiple transducers. Data fusion is achieved using a probabilistic approach, whereby measurements are combined numerically to derive a velocity likelihood function. The MAP velocity estimator is evaluated using a high-fidelity coherent Doppler sonar simulation of oscillating flow and data from a towing tank grid turbulence experiment where both velocity ambiguity and backscatter decorrelation were present. Time series and spectra from MAP velocity estimation are compared to those obtained with conventional Doppler signal processing. In addition to robustly resolving velocity ambiguity, the MAP velocity estimator is shown to reduce high-frequency noise in turbulence spectra.
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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".