The Framework of an Software-defined Gyroscope and Stochasitic Error Modeling Analysis
Bibliographic record
Abstract
This paper reports an application of inertial sensors based on a brand-new concept of software-defined inertial (SDI). The idea is aiming at unfolding inertial sensors' signal processing domain to the customers for integrating external information into the inertial sensor's signal processing part to improve the performance of the inertial sensors and the integration system. The implementation of the software-defined gyroscope (SDG) reported in this paper is the first attempt to use the software architecture to process the signals inside the inertial device. Such a structure would bring lots of benefits, including performance improvement of inertial sensors and flexible signal processing parameter adjustment. By employing the Allan Variance method, this paper reveals the relationship between the signal processing process and the random characteristics of inertial sensors, which is critical for applications such as GPS/INS integrated systems. We show that different signal processing strategies would result in different stochastic error characteristics for the inertial sensors. Thus, we believe that the contribution of this paper can be good guidance for more advanced integrated system designs using software-defined inertial sensors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".