An improved underwater TDOA/AOA joint localisation algorithm
Bibliographic record
Abstract
Abstract To solve the problem of sound source localisation in underwater sensor networks, this paper constructs a pseudo‐linear equation system of time difference of arrival and angle of arrival (TDOA/AOA) and then uses the weighted least squares algorithm to estimate the target position. This paper proposes a two‐stage weighted least squares algorithm that uses target position error. First, the adapted TDOA equation and the existing AOA equation are combined into the first‐stage weighted least squares algorithm, which improves the estimation accuracy of the first stage compared with the traditional algorithm. Second, this paper uses the target position error in the second stage to derive a new TDOA/AOA equation. Finally, the target position calculated in the previous stage is adjusted by the solved target position error. The performance of the proposed algorithm is verified by comparison with the Cramer–Rao lower bound. Simulation results show that the proposed algorithm still has good localisation performance even under high‐angle noise.
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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 | 0.002 |
| 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) | 0.003 | 0.002 |
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".