A spatial exploration based blind DOA estimation algorithm for closely spaced sources
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Bibliographic record
Abstract
In this paper, a spatial extrapolation-based blind DOA estimation method is presented for closely spaced sources. The new method is based on the linear extrapolation of the correlation matrix using AR coefficients, which are first estimated through an initial DOA estimation. Both the initial and final DOA estimations are performed by using the ESPRIT algorithm. Unlike conventional AR coefficient estimation method which estimates AR coefficients on the snap-shot basis, our AR coefficient estimation is carried out in correlation domain once a block of snap-shots, thus significantly reducing the computational complexity of the antenna array. Computer simulations show that the proposed blind DOA estimation method outperforms the conventional method in terms of the mean square-error (MSE) when the angle of separation of DO As is very small.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 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