Bibliographic record
Abstract
The practical limitations of many bottom mapping sonars lie in their ability to accurately estimate the angle of arrival. This paper addresses the accuracy of angle estimation when employed to determine the location of an extended target such as the bottom. A Gaussian model is assumed for the bottom backscatter and the corresponding Cramer-Rao lower bound for the variance of the angle estimate is determined for multi-element linear arrays. The paper focuses on determining the performance of high-resolution swath bathymetry sonars and, therefore, concentrates on the ability to determine bottom location with short pulses. Two error mechanisms, footprint shift and uncorrelated noise, are identified as important contributors to measurement errors. The two-element interferometric sonar configuration is investigated in detail. It is shown through the use of probability distributions, the Cramer-Rao bound, and simulation that it is difficult to get a good estimate of performance through simulation alone. Performance enhancement through pre-estimation and post-estimation averaging of multiple snapshots and changes in performance with pulse length and pulse rise time are also considered. Bottom estimation performance employing multi-element arrays is compared and contrasted with that of the two-element interferometric array. It is determined that there is little benefit associated with the multi-element array in terms of angle estimation performance alone. However, when other considerations such as angle ambiguities, multiple angles of arrival, and physical shortcomings associated with practical arrays are taken into account, the multi-element array is favored.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".