Low-complexity 2D root-MUSIC pairing for an L-shaped array
Bibliographic record
Abstract
We propose a two dimensional (2D) direction of arrival (DOA) estimation algorithm with multiple signals incident on an L-shaped array. The classical root-MUltiple SIgnal Classification (MUSIC) algorithm for an L-shaped array requires the solution of a 2D polynomial equation, a step that imposes high computational complexity. We propose a method to estimate the DOA of the incident signals using the traditional one-dimensional (1D) MUSIC in each arm of the L-shaped array; the key contribution here is an efficient and effective algorithm to pair the estimates in each arm. The pairing is based on finding the local maxima of the 2D MUSIC statistic over the possible estimated frequencies obtained by the two 1D MUSIC processes. Finally, we derive the Cramer-Rao Bound (CRB) for the estimation of the paired frequencies in the general case of colored noise. The simulation results compare the performance of the proposed method in comparison with available methods and the CRB.
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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 | 0.000 | 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 | 0.001 |
| 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".