Fast Underwater Target Localization With Wideband Signals for the Internet-of-Underwater-Things
Bibliographic record
Abstract
Fast underwater localization serves as a critical application of Internet-of-Underwater-Things (IoUT), such as underwater search and rescue. Narrowband sinusoidal pulses are very commonly used in locator beacons installed on flight recorders. A mobile anchor (e.g., autonomous underwater vehicle (AUV)) has to keep receiving the signal and measuring Doppler shift for a long time, so that enough information can be collected for reliable localization. The need of a long observation window is deeply rooted in the fact that the Doppler shift measurements are highly correlated when they are taken at closely located spots. In this paper, we will show that by replacing the narrowband beacon signal with a wideband one, high-accuracy positioning can be achieved within a short period of time. The basic idea is to simultaneously measure Doppler shift and time of arrival (ToA) from wideband signals, and the errors spaces corresponding to these two measurements are complementary even when they are taken at the same position. The Cramér-Rao bound (CRB) will be derived for such a system. In low-signal-to-noise ratio (SNR) regime, the observation window has to be prolonged for effective information extraction, and we will see that the wideband signals still have an edge over the narrowband signals in positioning accuracy. Efficient algorithms are designed for positioning and the closed-form positioning error is derived. We also show that the localization accuracy will experience a significant drop when ToA is replaced by time difference of arrival (TDoA), because the perfect complementation no longer holds. The performance of the proposed algorithm, along with corresponding comparisons, is verified through simulations over various parameters such as SNR, number of measurements, and length of observation window, etc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".