A New Positioning Filter: Phase Smoothing in the Position Domain
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT: Motivated by a requirement to provide real-time meter-level positioning of a NASCAR racing car, a modification of the standard Kalman filter was devised. This paper describes an approach that incorporates previous as well as current position states in a Kalman filter to take advantage of phase measurements differenced over time. In this formulation, the phase measurement difference is a measure of the difference in position in the line-of-sight direction to the satellite, so it can act as a relative position constraint of the current position with respect to the previous one. The formulation of the delta-phase observation equation is described, as well as the modifications made to the Kalman filter to incorporate it. An example used to illustrate the effectiveness of the delta-phase measurements in controlling position error growth is included. Test results in various urban environments are presented.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it