Asymptotically optimal narrowband signal detection using uniform linear array antenna
Bibliographic record
Abstract
This paper addresses the detection of a narrowband signal in Gaussian noise with unknown parameters. Assuming unknown direction-of-arrival (DoA), amplitude, frequency, phase and noise variance, two Separating Function Estimation Tests (SFETs) and a Generalized Likelihood Ratio Test (GLRT) are proposed to detect the signal. These SFETs are estimates of a proposed Separating Function (SF). This proposed SF provides asymptotically optimal detectors using Maximum Likelihood Estimation (MLE) and is derived by the decomposition of Fisher information function of the induced maximal invariant. We propose two estimators MLE and Outlier Processed MLE (OPMLE) for estimation of the SF. It is shown that, the MLE of frequency and DoA are obtained by an exhaustive search to maximize the absolute of the two-dimensional discrete Fourier transform (DFT) of the received signals. We propose OP-MLE as an MLE based estimator by first eliminating the outliers from the DFT of the received signal using a pre-estimation of DoA and frequency. The simulation results show that the omission of outliers results in considerable improvement. Similarly, the resulting SFET using OP-MLE provides a higher probability of detection comparing with SFET using MLE and GLRT.
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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 | 0.001 | 0.011 |
| 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 | 0.002 |
| 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".