Bayesian Information Criterion for Source Enumeration in Large-Scale Adaptive Antenna Array
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Bibliographic record
Abstract
Subspace-based high-resolution algorithms for direction-of-arrival (DOA) estimation have been developed for large-scale adaptive antenna arrays. However, its prerequisite step, namely, source enumeration, has not yet been addressed. In this paper, a new approach is devised in the framework of the Bayesian information criterion (BIC) to provide reliable detection of the signal source number for the general asymptotic regime, where m, n → ∞ and m/n → c ∈ (0, ∞), with m and n being the numbers of antennas and snapshots, respectively. In particular, the a posteriori probability is determined by correctly calculating the LLFs and PFs for the general asymptotic case. By means of the maximum a posteriori probability, we are capable of effectively finding the signal number. An accurate closed-form expression for the probability of missed detection is also derived for the proposed BIC variant. In addition, the probability of false alarm for the BIC detector is proved to converge to zero as m, n → ∞ and m/n → c. Simulation results are included to demonstrate the superiority of the proposed detection approach over state-of-the-art schemes and corroborate our theoretical calculations.
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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 | 0.001 | 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 it