A Triaxial Accelerometer Calibration Method Using a Mathematical Model
Bibliographic record
Abstract
This paper presents a new triaxial accelerometer calibration method using a mathematical model of six calibration parameters: three gain factors and three biases. The fundamental principle of the proposed calibration method is that the sum of the triaxial accelerometer outputs is equal to the gravity vector when the accelerometer is stationary. The proposed method requires the triaxial accelerometer to be placed in six different tilt angles to estimate the six calibration parameters. Since the mathematical model of the calibration parameters is nonlinear, an iterative method is used. The results are verified via simulations by comparing the estimated gain factors and biases with the true gain factors and biases. The simulation results confirm that the proposed method is applicable in extreme cases where the gain factor is 1000 V/(m/s2) and the bias is ±100 V, as well as the cases where the gain factor is 0.001 V/(m/s2) and the bias is 0 V. The proposed calibration method is also experimentally tested with two different triaxial accelerometers, and the results are validated using a mechanical inclinometer. The experimental results show that the proposed method can accurately estimate gain factors and biases even when the initial guesses are not close to the true values. In addition, the proposed method has a low computational cost because the calculation is simple, and the iterative method usually converges within three iteration steps. The error sources of the experiments are discussed in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".