Deep Sea TDOA Localization Method Based on Improved OMP Algorithm
Bibliographic record
Abstract
Compressed sensing is recently applied to time delay estimation, resulting in higher accuracy and stability compared to traditional methods. In this paper, a time delay estimation model is designed based on adaptive iterative local searching orthogonal matching pursuit (AILSOMP) algorithm, and an improved three-stage weighted least squares localization algorithm is proposed using the time delay values. Firstly, the sensor receives acoustic waves from the target in the deep-sea multipath environment. It then obtains the rectilinear propagation time delay of the sound wave through compressed sensing. Secondly, the time synchronization between the two sensors is maintained, and the difference between the estimated delays of both sensors is multiplied by the speed of sound to obtain the measured distance value. Finally, an improved three-stage weighted least squares algorithm is applied to locate the target using the time difference of arrival (TDOA). Simulation results confirm that the proposed algorithm has better localization performance compared to other methods in a multipath interference environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".