Bibliographic record
Abstract
This paper examines the concept of replacing a few expensive high-accuracy radio direction-finding (DF) sensors operating in a stand-off baseline with many low-accuracy DF sensors deployed on existing military vehicles located throughout an area of interest. A formula is presented to estimate the geolocation accuracy that could be achieved for a given number of sensors with a specified DF accuracy. Monte Carlo and virtual simulation of sensors with varying DF accuracy is used to assess the accuracy and reliability of the geolocation estimates that could be achieved. Geolocation results are computed using Stansfield's method and a technique developed at DRDC Ottawa, called the Discrete Probability Density (DPD) method, and compared to the theoretical location accuracy limit predicted by the Cramer-Rao Lower Bound. The DPD method is shown to provide more accurate and more reliable geolocation estimates than Stansfield's method when incorporating large quantities of low-accuracy DF data. This suggests that the DPD method could be used with several less-expensive low-accuracy DF sensors to provide improved emitter geolocation capability compared to the conventional deployment of a few high-accuracy DF sensors.
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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".