DOA Estimation Based on an Adversarial Learning Network via Small Antenna Arrays
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Bibliographic record
Abstract
As a key technology for radio monitoring and positioning, direction-of-arrival (DOA) estimation has garnered significant attention and has undergone in-depth research. This article proposes a new subspace-based DOA estimation algorithm based on an adversarial learning network. Considering the impact of the number of antennas in the signal-receiving array on the resulting DOA estimation accuracy, the proposed algorithm takes a covariance matrix corresponding to a small antenna array as the input of the adversarial learning network and reconstructs an extended covariance matrix corresponding to a virtual large antenna array. By introducing subspace technology, the multiple signal classification (MUSIC) algorithm can achieve high-resolution DOA estimation. Therefore, the extended covariance matrix corresponding to the virtual large antenna array is combined with the MUSIC to achieve DOA estimation. Simulated and real-world experimental results demonstrate that compared with conventional subspace-based DOA estimation algorithms, the proposed algorithm achieves significantly improved DOA estimation performance.
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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 it