Underwater TDOA/FDOA joint localisation method based on cross‐ambiguity function
Bibliographic record
Abstract
In the underwater sound source localisation systems, the sound wave signal is accompanied by time delay and Doppler shift, where the cross‐ambiguity function (CAF) is commonly applied as a method of estimating them. This study proposes a novel method for calculating the exact value of the CAF,based on the theory of curved surface interpolation. Firstly, two optimisation problems with equality and inequality constraints are developed, where the penalty function method is employed to turn the equality constraint problem into an unconstrained problem. Secondly, the time delay and Doppler shift of the CAF solution are processed to obtain the measurement values required for accurate localisation. Furthermore, the authors propose an improved time difference of arrival and frequency difference of arrival (TDOA/FDOA)‐based joint localisation algorithm, which changes the structure of traditional localisation equations and eliminates the localisation imprecision caused by neglecting the square term of the noise. The performance of the proposed algorithm is verified by extensive simulations and comparisons with several established methods. Remarkably, the proposed localisation method is confirmed to be noticeably superior and effective for the considered application.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".