Estimating MEMS gyroscope g-sensitivity errors in foot mounted navigation
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Bibliographic record
Abstract
Errors in gyroscope measurements due to linear accelerations are often overlooked in foot mounted navigation systems. Accelerations of foot mounted IMUs can reach 5 g while walking and 10 g while running, but vary depending on the sensors location and mounting. These accelerations are often very short and can induce large biases in the gyro which can produce attitude errors when the measurements are integrated. This paper proposes a real time method for the mitigation of g-sensitivity errors whereby the coefficients are estimated in the navigation Kalman filter. Variations of the estimation scheme are given including estimating the diagonal terms of the 3×3 matrix or all nine elements of the matrix. Accuracy (RMS) improved by 45% and 61% in two data sets using two different sensors in different environments. Convergence rates of the estimated variance are also shown.
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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