Improving Statistical Robustness of Matched-Field Source Localization via General-Rank Covariance Matrix Matching
Bibliographic record
Abstract
Performance of matched-field source localization is highly dependent on the precision of the model to actual physical processes. Model mismatch, including sound propagation environmental mismatch, statistical mismatch, and system mismatch, causes severe performance degradation. Statistical mismatch occurs when an insufficient snapshot set is used to estimate the data covariance matrix. Diagonal loading can improve the widely used minimum variance distortionless response (MVDR) and minimum power distortionless response (MPDR) processors against statistical mismatch, however, the result relies on selection of the loading level. Previously, a new processor named the matched-covariance estimator (MCE) was shown to have statistically robust estimation capability under white noise conditions by matching the general-rank data covariance matrix with a covariance matrix of the modeled replicas. In this paper, the method is further developed in more realistic application scenarios with discrete point interferences and/or surface-generated noises. Simulations and analyses show that MCE can outperform classical MVDR/MPDR matched-field processors by: exploiting both signal and noise data structure at the same time; being less prone to mistaken nulling of the signal; requiring no rank-1 signal space restriction; and not needing signal-free and long duration training data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".