DOA estimation of underwater acoustic co-frequency sources for the coprime vector sensor array
Bibliographic record
Abstract
A coprime array with fewer sensors can achieve the same resolution as a uniform linear array. However, when detecting co-frequency targets, there can be prominent false alarms due to overlaps between the main and grating lobes of subarrays. This study proposes a direction-of-arrival (DOA) estimation method to obtain the co-frequency target directions from high grating lobes. The method utilizes joint processing of sound pressure and vibration velocity data from vector hydrophones of a coprime vector hydrophone array and designs joint-cross terms (JCTs) using channel combinations. Based on JCTs, we establish a characteristic data point identification algorithm. The method in this paper can stably and accurately acquire co-frequency target directions from high grating lobes without decoherence operation. Simulation results demonstrate that the proposed algorithm achieves accurate DOA estimation even with reduced signal-to-noise ratio (SNR) and fewer data points. Additionally, a sea experiment confirms the rationality and efficiency of the proposed algorithm, providing new ideas for co-frequency source detection using coprime vector sensor 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".