Carrier Phase-Based Precise Heading and Pitch Estimation Using a Low-Cost GNSS Receiver
Bibliographic record
Abstract
Attitude and heading estimation methods using the global navigation satellite system (GNSS) are generally based on multi-antenna deployment, where the installation space and system cost increase with the increase in the number of antennas. Since the single-antenna receiver is still the major choice of the mass market, we focus on precise and reliable heading and pitch estimation using a low-cost GNSS receiver. Carrier phase observations are precise but have an ambiguity problem. A single difference between consecutive epochs can eliminate ambiguity and reduce the measurement errors. In this work, a measurement model based on the time-differenced carrier phases (TDCPs) is utilized to estimate the precise delta position of the antenna between two consecutive epochs. Then, considering the motion constraint, the heading and pitch angles of a moving land vehicle can be determined by the components of the estimated receiver delta position. A threshold on the length of the delta position is selected to avoid large errors in static periods. To improve the reliability of the algorithm, the Doppler-aided cycle slip detection method is applied to exclude carrier phases with possible cycle slips. A real vehicular dynamic experiment using a low-cost, single-frequency GNSS receiver is conducted to evaluate the proposed algorithm. The experimental results show that the proposed algorithm is capable of providing precise vehicular heading and pitch estimates, with both the root mean square errors being better than 1.5°. This also indicates that the cycle slip exclusion is indispensable to avoid unexpected large errors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".