A new multi-position calibration method for MEMS inertial navigation systems
Bibliographic record
Abstract
The Global Positioning System (GPS) is a worldwide navigation system that requires a clear line of sight to the orbiting satellites. For land vehicle navigation, a clear line of sight cannot be maintained all the time as the vehicle can travel through tunnels, under bridges, forest canopies or within urban canyons. In such situations, the augmentation of GPS with other systems is necessary for continuous navigation. Inertial sensors can determine the motion of a body with respect to an inertial frame of reference. Traditionally, inertial systems are bulky, expensive and controlled by government regulations. Micro-electro mechanical systems (MEMS) inertial sensors are compact, small, inexpensive and most importantly, not controlled by governmental agencies due to their large error characteristics. Consequently, these sensors are the perfect candidate for integrated civilian navigation applications with GPS. However, these sensors need to be calibrated to remove the major part of the deterministic sensor errors before they can be used to accurately and reliably bridge GPS signal gaps. A new multi-position calibration method was designed for MEMS of high to medium quality. The method does not require special aligned mounting and has been adapted to compensate for the primary sensor errors, including the important scale factor and non-orthogonality errors of the gyroscopes. A turntable was used to provide a strong rotation rate signal as reference for the estimation of these errors. Two different quality MEMS IMUs were tested in the study. The calibration results were first compared directly to those from traditional calibration methods, e.g. six-position and rate test. Then the calibrated parameters were applied in three datasets of GPS/INS field tests to evaluate their accuracy indirectly by comparing the position drifts during short-term GPS signal outages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".