The filtering method of MEMS gyro signal based on sparse decomposition
Bibliographic record
Abstract
Traditional denoising methods for Micro-electromechanical Systems (MEMS) gyro signal are required to obtain a priori noise statistical properties, which result in poor denoising performance in MEMS gyro utilized in Micro-Inertial Measurement While Drilling (MWD), due to the unknown and complex noise characteristics in MWD. According to this problem, a kind of gyro signal denoising method based on sparse decomposition without a requirement of the priori noise characteristics, utilizing a newly designed atom dictionary, is proposed. Firstly, the MEMS gyro output differential equation is established on the basis of the physical mechanism of the MEMS gyro, then the real MEMS gyro output signal characteristics are analyzed according to the solution of the differential equation. Secondly, the characteristic wave atom most similar to the gyro output signal is designed. Finally, the gyro signal sparse decomposition denoising experiments based on the designed atom dictionary are conducted, compared with the wavelet threshold method and Kalman filter. The experiment results show that the proposed denoising method based on sparse decomposition utilizing the newly designed atom dictionary outperforms wavelet threshold method and Kalman filter in MEMS gyro signal processing of MWD, especially when the noise statistical properties of gyro signal are completely unknown.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".