Off-Grid Based DOA Estimation Algorithm Using Auto-Regression (1) Sparse Bayesian Learning with Linear Interpolation Model
Bibliographic record
Abstract
On-grid approaches for DOA estimation majorly exhibits the problem of grid mismatch. Coarse grid leads to reduced estimation accuracy and dense grid leads to increased algorithm complexity and performance degradation due to highly correlated array manifold matrix. In this paper, a fixed off-grid DOA estimation algorithm is proposed to overcome this grid mismatch problem. Firstly, a sparsity based linear interpolation model for array manifold matrix is proposed to avoid the above limitation by introducing a bias parameter into the estimation framework. To solve this model, an Auto-regression (1) (AR (1)) based sparse Bayesian learning algorithm is proposed. To exploit the temporal correlation property of unknown DOA spatial spectrum in a MMV case, we develop this AR (1) model along with SBL to estimate the unknown DOA spectrum and expectation maximization (EM) framework to update the hyper-parameters. The results section shows that the proposed algorithm enjoys good estimation resolution and accuracy in the cases of fewer snapshots available, highly correlated signal sources, very low SNR and very closely spaced sources.
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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 | 0.000 |
| 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".