An Improved Localization Method Using Error Probability Distribution for Underwater Sensor Networks
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
An accurate localization scheme is essential to many underwater sensor applications. However, due to the persistent existence of uncertainties and measurement errors, an accurate localization is very difficult to achieve. To mitigate this problem, multi-iteration measurement and least squares scheme are often adopted in terrestrial applications to find a good estimate. But, in underwater applications the multi-iteration scheme is not practical due to high communication cost. Meanwhile, it has been observed that the errors in distance measurement often follow a certain pattern, which can be utilized to further improve on localization accuracy. In the paper, we analyze and utilize the measurement error distributions to better improve localization accuracy. An analytical model is developed for performance evaluation, along with extensive simulations. Both uniform error distribution and normal error distribution are considered in our research. Our results indicate that our proposed probabilistic localization method can significantly improve the localization accuracy over the commonly adopted least squares estimate (LSE) scheme.
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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 it