Matched-filter loss due to reflection from the sea surface: measurements and simulation
Bibliographic record
Abstract
Acoustic reflection from the sea surface can cause signal distortion owing to the time-varying nature of the sea surface, leading to matched-filter loss and degraded sonar per-formance. Surface reflections were measured and simulated to quantify matched-filter loss as a function of signal duration (up to 8 s) for a linear frequency modulated (LFM) waveform spanning the 2000–4000 Hz band. The ‘exact’ boundary integral equation (IE) method was used for the simulation, following the 2D treatment in the literature. The IE method was extended to 3D and a comparison was made against the 2D method for a scaled down example; further testing will require more computational resources. A new IE-p method was also implemented in 2D and 3D where the surface is partitioned, each partition is solved using the IE method, and the solutions for each of the partitions is combined to form the total solution. The widely used Kirchoff approximation was also implemented in 2D and 3D. A 2D simulation using the IE, KA, and IE-p methods was performed to recreate an experiment done during the Littoral Continuous Active Sonar sea trial in 2018 where a source and receiver were separated by 110 m. The three methods gave similar results at this close range, whereas the KA method is not expected to perform as well at longer ranges with lower grazing angles and increased shadowing. The simulated matched-filter loss generally agreed with the measured loss as a function of duration.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".