Validity of the frozen-surface approximation for large time-bandwidth signals
Bibliographic record
Abstract
The physical modeling of underwater acoustic propagation, scattering, and reflection, and the signal processing associated with these processes, usually rely on the frozen-surface approximation. This longstanding approximation, which dates back to Carl Eckart’s seminal paper (The Scattering of Sound from the Sea Surface, 1953) assumes that the ocean surface can be modeled as being frozen in time throughout the entire pulse duration. The approximation is valid for short-duration pulses typically used by sonars in the decades following that paper; however, its applicability to present-day high duty cycle sonars is questionable. Although the assumption is based on the physics of the problem, it can have a profound effect on the signal processing, especially for large time-bandwidth signals. In this paper some experimental results will be presented to provide examples of how the approximation fails for a long duration, wideband pulse. Using this as motivation, the authors will explore some areas where incorrectly employing the approximation can introduce errors in the expected signal processing gain. While no attempt will be made to correct shortcomings in the approximation, it is hoped that the discussion may motivate renewed interest in this issue. Funding from ONR and ONR Global is gratefully acknowledged.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".