Low-cost IMU Data Denoising using Savitzky-Golay Filters
Bibliographic record
Abstract
MEMS sensors have been used in many applications including navigation systems. However, these sensors suffer from highly noisy measurements. If left untreated, these errors will significantly degrade the ultimate navigational solution. Hence, applying a pre-filtering technique becomes a necessity to de-noise these sensor signals to improve the overall system performance. While wavelet denoising is the most common technique for sensor data pre-filtering, it may not be suitable for real-time implementations. This paper explores another method; namely, Savitzky-Golay filters, which can provide competitive denoising performance with a less computationally demanding algorithm. The purpose of the paper is to examine the performance of the new method against wavelet de-noising with respect to both positioning and attitude accuracy and computations time. We applied the filter to denoise MEMS-based inertial sensors data in a tightly coupled integrated INS/GPS system. Our results showed that the new method outperformed the wavelet denoising approach. Moreover, the new method demands much less computations time, which makes it more suitable for embedded systems and real-time applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".