Sparse broadband acoustic response estimation via a gaussian mixture model with application to m-ary orthogonal signaling
Bibliographic record
Abstract
Shallow water acoustic response functions at high frequencies and large bandwidths exhibit spatio- temporal variability that depends greatly on the propagation media's volume and boundary conditions as well as system source-receiver motion. For this reason practical acoustic systems invariably must operate without perfect knowledge of the space-time state of the ocean media. Considered here is a Gaussian mixture assignment over Doppler and channel bandwidth employed to describe the amplitude and phase of such acoustic response functions over signal duration and bandwidth that can serve in many scenarios to replace recursive least squares and Kalman-like algorithms. The mixtue Gaussian model of channel dynamics allows for the accurate and adaptive description of the response function. The model is flexible and naturally accommodates varying degrees of observed channel spar- sity. Posterior expectations are derived and shown to be soft shrinkage operators over Doppler-channel frequency. The model allows for novel and accurate estimates regarding the aggregate acoustic path dilation process that serve to replace conventional phase locked loops. This adaptive filtering scheme with aggregate path dilation estimation and compensation is tested on M-ary orthogonal signals at both 1 and 2 bits per symbol during the Unet08 acoustic communication experiments. These tests took place in the downward refracting, lossy bottom environment of St. Margaret's Bay Nova Scotia off of the R/V Quest. Receiver algorithms based on this approach were applied to a single element acoustic time series and empirical bit error rates demonstrate a 4 dB improvement over rank based maximal path combining methods. For a single hydrophone at 2 bits per symbol a bit error rate of less than 10-4 is observed at received SNR < -10 dB corresponding to an SNR/bit < 14 dB.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".