Accurate Acoustic Signal Parameter Estimation for Marine Geodesy Surveys
Bibliographic record
Abstract
Acoustic signal parameter estimation is important for diverse marine geodesy surveys and several other applications. However, the received signal from a far-field target characterized by planar wavefront propagation is frequently affected by strong nearby interfering signals. Their presence deteriorates the performance of direction-of-arrival (DOA) estimation for far-field target. In order to enhance the reception of signal from far-field target, the near-field/far-field (NFFF) beamformer is proposed. Such a beamformer optimizes beam pattern for far-field detection by maximizing beamformer output in the direction of the far-field target with the imposed condition to eliminate interfering signals generated in near-field locations. As the interference suppression only occurs at the position of near-field interference, a possible blind zone for far-field detection in conventional methods is not created. The NFFF beamformer is applicable for coherent signals and the scenario with multi interferences. For stationary situation where interferers locations are fixed, the NFFF beamformer computations do not require time updates with associated computational load. Furthermore the proposed method can be extended to several new situations such as acoustic monitoring performed from a stationary platform subjected to water currents, waves, winds and other variables, all of them generating nearby interferences and also to different array configurations including 2D and 3D arrays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".