Improving INS/GPS Navigation Accuracy through Compensation of Kalman Filter Errors
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
The Kalman filter is often used to integrate satellite navigation systems with inertial navigation systems. Such integrated systems are especially useful for navigation of vehicles in urban environments where satellite signals are frequently blocked by tall buildings. The filter weights the measurements of both navigation systems to provide an overall optimal solution. Unfortunately, an optimal solution is only achieved when the filter has been supplied with ideal a priori information such as proper measurement noise characteristics and system dynamics. If such parameters are not perfect they can be detected and compensated for using an intelligent navigation scheme which is adaptable to different sensors. As dynamics are encountered, satellite signal blockages are simulated to test the optimality of the filter. A neural network is then trained to learn any residual deterministic errors which are then removed from future system drifts during signal blockages.
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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 | 0.000 | 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 it