Roll Rate Estimation for Cylindrical Spinning Vehicle Based on Pseudorange Observations
Bibliographic record
Abstract
Roll rate estimation is essential to attitude measurement. During the processing of baseband signals, it is complicated to measure the vehicle rolling attitude, based on the energy features of existing satellite signals. To measure the energy of received signals in real time, the data communication interface and storage unit of the receiving device must be customized. However, the customization will push up the cost of attitude sensors, and result in processing delays. To solve the problem, this paper measures the vehicle roll rate by the variation features of pseudorange observations, which are easy to acquire and highly applicable. Specifically, the roll rate of a cylindrical spinning vehicle was estimated, using Global Navigation Satellite System (GNSS) receiver with single side-mounted antenna. After analyzing the variation of pseudorange observations, the authors calculated the roll rate of the cylindrical spinning vehicle based on the variation rules. The proposed approach was verified through experiments with simulated GNSS signals. The accuracy of our approach was measured at different rotation speeds and translational speeds. The results show that our approach can accurately estimate the roll rate when the standard deviations of noise is less than 1m for pseudorange measurement.
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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".