A spatio-temporal stacking approach for estimating two-dimensional direction-of-arrival
Bibliographic record
Abstract
A number of 2-D DOA estimation techniques based on L-shaped array have received much attention. In general, these methods require division of the L-shaped array into two independent uniform linear array (ULA) to obtain the azimuth and elevation angles independently, and then use additional pair matching technique to achieve 2-D DOA estimation. Therefore, these methods have some drawbacks such as 1) requirement of pair matching which may increase the computational burden significantly and 2) estimating the azimuth and elevation angles by independently using each ULA half of the L-shaped array. The purpose of this paper is to develop a spatio-temporal stacking approach (STSA) to deal with these shortcomings. The STSA method first partitions the many lag cross-correlation matrices into a lot of submatrices based on the assumption on the second-order temporal structure (SOTS) of the source signals, and then stacks these submatrices according to some special structure to form a spatio-temporal stacking matrix. Finally, the joint singular value decomposition (JSVD) technique is exploited to extract out the 2-D DOAs one by one.
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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".