Analysis of Swath Bathymetry Sonar Accuracy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it