Sensor data fusion using a probability density grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A novel technique has been developed at DRDC Ottawa for fusing electronic warfare (EW) sensor data by numerically combining the probability density function representing the measured value and error estimate provided by each sensor. Multiple measurements are sampled at common discrete intervals to form a probability density grid and combined to produce the fused estimate of the measured parameter. This technique, called the discrete probability density (DPD) method, is used to combine sensor measurements taken from different locations for the EW function of emitter geolocation. Results are presented using simulated line of bearing measurements and are shown to approach the theoretical location accuracy limit predicted by the Cramer-Rao lower bound. The DPD method is proposed for fusing other geolocation sensor data including time of arrival, time difference of arrival, and a priori information.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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