Space-Time Noise Characterization for Underwater Acoustic Communications
Bibliographic record
Abstract
The performance of an underwater acoustic receiver may not be accurate when the noise at the receiver is assumed to always be white Gaussian, and uncorrelated between elements. In this work, ocean ambient noise is characterized to accurately predict the gain on an array of acoustic sensors. First, an analytical, discrete-time model to generate synthetic space-time noise over a receiver array is presented using an autoregressive AR) model. Spatial and temporal variations are observed for noise sources due to surface activity. In this work, ambient noise data measurements from a 3-day sea experiment, Dalcomm1, run on the East Shore of Nova Scotia is used to analyze the impact of noise on a 5-element communication receiver. The multi-path behavior of acoustic noise is analyzed for data sets obtained during the measurement campaign. Finally, the performance of a space-time adaptive equalizer is analyzed using a 2D correlated noise model. The optimal weights which minimize the distortion due to ambient noise are obtained by using an adaptive minimum mean-square error (MMSE) space-time equalizer. The space-time filter performance is compared for both correlated and uncorrelated noise sources.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".