Underwater Navigation Method Based on Side-scan Sonar Images
Bibliographic record
Abstract
In recent years, navigation techniques for underwater vehicles including autonomous long range vehicles have been actively researched. This paper proposed a novel underwater navigation method using side-scan sonar images. High-order cumulant was introduced into image analysis. Bispectrum of a real-time side-scan image was calculated and was employed as a template in the following scanning process within a prior known map of seafloor. Mean square difference was chosen to evaluate the similarity at each searching point in order to obtain the best fix estimation. Simulations were done with actual sonar data and the results suggested that the method had good robustness of rotation and noise. Accuracy of the estimate was pixel level that relied on the resolution of side-scan sonar images and could be higher than 1 meter. In flat bottom specially, the method was able to give out robust position estimate and could be used as a good supplement to traditional inertial navigation systems.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".