Effect of channel impulse response on matched filter performance in the 2013 Target and Reverberation Experiment
Bibliographic record
Abstract
Active sonar performance is determined by the characteristics of the target, the sonar system and the effect of the environment on the received waveform. The two main influences of the environment are propagation effects and the contamination of the target echo with a background. The ambient noise and reverberation are mitigated by means of signal processing, mostly through beamforming and matched-filtering. The improvement can be quantified by the signal to noise ratios before and after processing. Propagation effects can have a large influence on the gains obtained by the processing. To study the effect of the channel on the matched filter performance, broadband channel impulse responses were modeled and compared to measurements acquired during the Office of Naval Research-funded 2013 Target and Reverberation Experiment (TREX). In shallow water, a large time spread is often observed, reducing the effectiveness of the matched filter. TREX data show, however, a limited time spread. Model predictions indicate that this could be caused by a rough sea-surface, which while increasing propagation loss, at the same time increases matched filter gain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".