An Efficient Method for Evaluating the Performance of MEMS IMUs
Bibliographic record
Abstract
Advances in MEMS technology combined with the miniaturization of electronics, have made it possible to produce chip-based inertial sensor for use in measuring angular velocity and acceleration. These chips are small, lightweight, consume very little power and are extremely reliable. They have therefore found a wide spectrum of applications in the automotive and other industrial applications. Currently, new MEMS inertial sensors or IMUs developed by various manufacturers continue to emerge on the market. However, such sensors should be evaluated in terms of navigation performance. Common testing in the lab can provide parameters such as sensor noise density and bias instability but cannot predict the corresponding performance of a full navigation system. IMU/GPS field testing is the only way to evaluate the performance of MEMS IMUs especially when GPS signals are temporarily blocked. However, testing every MEMS sensor (or IMU) in the field is not practical since it is a time- consuming and costly task. Therefore, the main objective of this paper is the development of an efficient method for evaluating the navigation performance of any MEMS IMU using lab testing only. The developed method is based on using MEMS sensors static data signals to estimate the MEMS sensor errors. Hence, by grafting these errors into the signals of a high quality IMU (gyro drift of 0.005 deg/h), collected in a previously conducted typical field test, a quasi field dataset of the MEMS is obtained since the high quality IMU signals can be considered as the true inertial sensor. Such emulated MEMS IMU field data can then be processed with the corresponding GPS data collected in the same test to evaluate the MEMS IMU navigation performance. To test the efficiency of the proposed method, several land-vehicle kinematic datasets with GPS, a high-quality IMU and different MEMS IMUs were used. Static data of the same MEMS IMUs was collected and then the proposed method was applied. The performance of the MEMS IMU actual and emulated datasets is compared during several GPS signal blockage periods. The results show that both solutions have a similar behavior with an average difference of only 20% in terms of accumulated position drifts. This illustrates the usefulness of the proposed technique in addition to the cost and time savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".