A robust phase gradient bearing estimation algorithm for a tri-axis cross-dipole acoustic array, with application to a long range autonomous underwater vehicle homing and tracking system
Bibliographic record
Abstract
A bearing estimation algorithm was developed as part of a long range acoustic bearing (LRAB) homing system implemented on an autonomous underwater vehicle (AUV). A tri-axis cross-dipole acoustic array with seven digital hydrophones was developed and mounted in the AUV for the homing system. The Phase Gradient algorithm was implemented on the vehicle's Acoustic Homing and Localization System processor and run in real-time. The algorithm was designed to estimate the bearing and elevation angles to a continuous wave (CW) signal from a beacon source. The algorithm directly estimates the three Cartesian components of the incoming signal wave-vector from estimated cross-spectra between the hydrophone elements. The algorithm is robust against hydrophone failure, and every hydrophone in the array is used to estimate each component of the wave-vector. In this paper, the theoretical development of the Phase Gradient algorithm is presented, as well as the results from real applications of the algorithm as part of an AUV homing and tracking system.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".