Temperature compensation model of MEMS inertial sensors based on neural network
Bibliographic record
Abstract
Micro-electromechanical Systems (MEMS) inertial sensors are lightweight, small size and low-cost sensors that consume less power energy compared to their high-precision bulky counterparts. However, this miniaturization is a double-edged sword and MEMS-based inertial sensors suffer from various error sources, noises and instabilities. Indeed, inertial sensor errors vary with time, temperature and from turn on to turn on. In order to exploit the full potential of a MEMS-based inertial navigation system (INS), and to enhance its accuracy, it is indispensable to develop a temperature-dependent model that compensates these errors. Traditional temperature compensation methods rely on polynomial regression method, which fails to take into account the nonlinearities inherent in the sensor errors. This paper proposes a new temperature compensation model for a full inertial measurement unit (IMU), based on a radial basis function neural network (RBFNN) that compensates the significant deterministic errors of both accelerometer and gyroscope triads in a wide temperature range. A high precision rate table and a thermal chamber are used for accurate testing. The effectiveness of the method is investigated with various static and dynamics tests in the laboratory and with a car, and results are compared with the traditional polynomial fitting method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".