Improved structured least squares for the application of unitary ESPRIT to cross arrays
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Bibliographic record
Abstract
A key problem in high-resolution multidimensional parameter estimation via unitary ESPRIT is to jointly solve a set of invariance equations by means of least-squares minimization. It has been shown previously that existing least-squares techniques fail when applied to the category of cross arrays, which consist of perpendicular uniform linear arrays crossing at the center of the array. Cross array geometries are of special interest because they provide a larger aperture and, hence, better resolution for a given number of array elements than other multidimensional uniform array geometries. This letter proposes an improved structured least-squares method that enables successful application of unitary ESPRIT to cross arrays. Results of simulated direction-of-arrival estimation experiments using a three-dimensional cross array indicate that considerable performance improvements can be achieved if the new method is used.
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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 | 0.000 | 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 | 0.000 |
| 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 it