Increasing the Uniform Degrees of Freedom for Moving <i>q</i>-Dilated Arrays
Bibliographic record
Abstract
For$q$-dilated arrays, i.e., dilated arrays with dilation factor$q$, the uniform degrees of freedom (uDOFs) of the synthetic array after array motion can be increased by a factor of$q$compared with that of original linear arrays for$q\leq 3$. However, when$q\geq 4$, the number of uDOFs of the synthetic arrays of$q$-dilated arrays is only three using existing moving array models. To achieve a high number of uDOFs for$q$-dilated arrays with$q\geq 4$, in this article, we design a new moving array processing model in$q$-dilated arrays, which synthesizes multiple shifted arrays with displacements of half wavelength multiples. The number of shifted arrays in the proposed model is shown to be a function of the dilation factor$q$. First, we prove that the number of uDOFs of synthetic arrays of$q$-dilated arrays can be$q $times that of their original arrays for arbitrary positive integer$q$. Hence, the maximum number of detectable sources for direction-of-arrival (DOA) estimation is increased by a factor of$q $. Second, we apply the new model to two-parallel$q$-dilated arrays to estimate the 2-D DOAs, increasing the number of identifiable sources by a factor of$q$for 2-D DOA estimation. Numerical examples show the superiority of the proposed model based on$q $-dilated arrays.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".