High-Resolution Wideband DOA Estimation Based on Multi-Frequency Cyclic Rank-Minimization
Bibliographic record
Abstract
Wideband DOA estimation has been applied in various signal source location scenarios, e.g., in wireless communication systems to improve the capacity of communication. Existing wideband DOA methods often require prior knowledge such as the number of sources as well as pre-estimations. Moreover, they may suffer from model-mismatch problem. In this paper, we employ the manifold separation technique and Jacobi-Anger expansion to allow multi-frequency joint processing of wideband DOA, which alleviates the challenge of model-mismatch and leads to a much higher DOA resolution. The proposed method is further formulated to be a multi-convex rank-minimization problem to facilitate the analysis of the problem and to improve the convergence performance. The superior performance of the proposed multi-frequency joint processing method has been demonstrated by several numerical studies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".