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 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".