High Duty Cycle (HDC) sonar processing interval and bandwidth effects for the TREX'13 dataset
Bibliographic record
Abstract
Unlike conventional Pulsed Active Sonar (PAS), which listens for target echoes in between short-burst transmissions, High Duty Cycle (HDC) sonar attempts to detect echoes amidst the continual interference from source(s) transmitting with nearly 100% duty cycle. HDC sonar presents an additional processing parameter, not available with PAS, which is the processing interval. The processing interval is a selectable subset of time within a CAS repetition cycle used for coherent processing. Hence, the choice of processing interval may be used to tune the performance of the sonar to local environmental conditions and to the operational scenario. Theoretically, increasing the processing interval increases target detectability, but in practice other factors should also be considered. In real acoustic environments, sound propagation is subject to temporal and spectral spreading effects, and these may limit the processing gains to lower levels than expected. Target Doppler can also become a more significant issue with longer processing intervals. Shorter processing intervals provide an increased number and rate of detection opportunities, which can be a significant advantage, leading to improved target holding, localization, tracking, and classification. This paper describes the various expected effects of the processing interval on performance for continuous-time LFM signals. It presents an analysis conducted on the TREX'13 sea trial dataset, and shows various results achieved as a function of processing interval. The results are explained and compared with theoretical expectations, and show the complicating effects of a real acoustic environment. In particular, we see the limitation on performance gains with increasing the processing interval due to acoustic environmental spreading effects, the target's physical extent and Doppler effects. Comparisons are shown between echoes from three different targets: mobile compact, mobile extended, and fixed. The evaluation describes performance using the quantities of Received Level, SNR, echo time-extent, and delay bias.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".