Short-Baseline High-Precision DGPS for Smart Snow Blower
Bibliographic record
Abstract
High-precision positioning is critical for many Internet-of-Things (IoT) applications; however, most existing approaches are too expensive to be used in commercial products. A highly accurate differential global positioning system (DGPS) has not been widely used because of the difficulty in solving integer ambiguities in the single-frequency carrier phase. Multipath interference and receiver noise are the main reasons for limiting the DGPS accuracy and efficiency in solving integer ambiguities. In this article, we propose a combination of anti-multipath antennas and high-performance GPS receivers to effectively mitigate impairments due to multipath propagation and receiver noise. Furthermore, by exploiting more data available from high-performance GPS receivers, we can improve the efficiency of solving carrier-phase integer ambiguities. For applications in a smart snow blower, we installed two GPS receivers with a constant separation between them. The distance between the GPS receivers was used to verify the DGPS results. Furthermore, using the proposed DGPS technology, the smart snow blower can obtain a high-precision orientation estimation, with a standard deviation of 0.299 cm in positioning accuracy and 0.409° in orientation accuracy.
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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 | 0.000 |
| 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 | 0.001 | 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".