On the lower performance bounds for DOA estimators from linearly-modulated signals
Bibliographic record
Abstract
In this paper, the problem of direction of arrival (DOA) estimation from linearly-modulated signals over AWGN channels is considered. We derive closed-form expressions for the inphase/quadrature Cramér-Rao lower bounds of the data-aided (DA) DOA estimates from any linearly-modulated signal corrupted by additive white circular complex Gaussian noise (AWCCGN). We consider the case of single-source signals impinging on multiple receiving antenna elements, commonly known as single input multiple output (SIMO) configurations. An analytical approach is conducted to compare the achievable performance in coherent estimation against noncoherent estimation over uniform linear array (ULA) and uniform circular array (UCA) configurations. It will be shown that the CRLBs that can be achieved over a ULA are lower than those that can be achievable over a UCA up to a given angular aperture whose expression is also derived in this paper. It will be shown also that ULAs exhibit lower CRLBs in coherent estimation than in noncoherent estimation and that the CRLBs hold, however, the same for UCAs in both estimation schemes.
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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 | 0.010 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".