An unscented Kalman filter for in-motion alignment of low-cost IMUs
Bibliographic record
Abstract
This paper describes the alignment of low-cost inertial measurement units (IMUs) using an unscented Kalman filter (UKF), which allows large initial attitude error uncertainties. The state vector includes position, velocity, attitude, and sensor biases and scale factors. Position information from the differential global positioning system (DGPS) solutions is used as measurements. Test results with a micro-electrical-mechanical-systems (MEMS) IMU showed that the alignment converged within 50 s with RMS values of 0.093/spl deg/, 0.094/spl deg/ and 0.388/spl deg/ for roll, pitch and heading, respectively. The UKF works well even in cases of large initial attitude errors (about 30/spl deg/) not only for heading but also for roll and pitch. Therefore, the UKF is a unified approach to handle large and small attitude errors of an inertial navigation system (INS) seamlessly.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".