Credible Positioning of BDS RTK/INS Integration Based on Multi-Information Cross-Validation
Bibliographic record
Abstract
The requirement for credible and reliable positioning of a multi-sensor integration system is an essential foundation for comprehensive PNT (Positioning, Navigation, and Timing) services. However, the credibility of positioning results based on multi-sensor integration is difficult to evaluate and even there is no effective mode at present. To try to solve such a problem, a credible positioning model based on the tight integration of BDS Real Time Kinematic (RTK) and Inertial Navigation System (INS) is presented in this paper. In such a model, a multi-information cross-validation algorithm is introduced to ensure the credibility of RTK/INS tight integration. Meanwhile, a backward quality checking model is generated based on the result of the calculated credible positioning error level, which can optimize the positioning results of RTK/INS integration furtherly. After the mathematical model descriptions, a simulated test and a real experiment are adopted to evaluate this presented method. Results illustrated that: 1) envelope level of the credible factor for the real error can reach the centimeter level and the average probability of unenveloped error is within 2%; 2) after applying the quality control scheme based on credible positioning feedback information, the positioning results of RTK/INS integration are improved by 67.6%, 78.9%, and 77.3% on average.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".