Grid‐less coherent DOA estimation based on fourth‐order cumulants with Gaussian coloured noise
Bibliographic record
Abstract
This study investigates the continuous coherent direction‐of‐arrival (DOA) estimation, and concentrated on developing grid‐less sparsity‐based methods to Gaussian coloured noise environment. The noise component is greatly suppressed by applying fourth‐order cumulants (FOC) due to its blind property to additive Gaussian noise. Two grid‐less sparse models are designed separately. The first sparse representation model is built based on the simplified FOC vector, which would effectively reduce the computational complexity. Then the dual atomic norm minimisation algorithms are applied to solve the basis mismatch problem and improve the estimation accuracy. Additionally, a Toeplitz matrix based on FOC vector is constructed. The second sparse model based on this Toeplitz FOC matrix is proposed to implement array aperture extension, which can break through the restriction of maximum signal number and improve resolution. The proposed methods can handle the coherent signals and do not require the signal number as a prior. Numerical simulations demonstrate the outperformance of the proposed methods in estimation precision, computational cost and robustness to coloured noise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".