Comparison of two angle of arrival averaging strategies
Bibliographic record
Abstract
The accuracy of direction finding (DF) systems can often be improved by using techniques for the time averaging of information from multiple data records, particularly when the signal-to-noise ratio is low. In certain DF systems, the emitter angle of arrival (AOA) is computed from the phase angle of a complex-valued signal. For this type of DF systems, there are two closely related but distinctly different approaches to AOA averaging. The first approach directly averages the complex signal samples and then computes the value of AOA from the phase angle of the resultant average. The second approach reverses the order of operations of the first approach by first computing the phase angle of each complex signal sample and then averaging the computed individual phase angles. Under the assumption that the complex signal samples processed by the AOA estimator are i.i.d. Gaussian distributed, this paper presents a theoretical proof to demonstrate the superiority of the first approach that was observed through experiments with off-the-air data.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".