First smartwatch RTK results: performance analysis of instantaneous, single-frequency multi-GNSS cm-level positioning with comparison to Google Pixel 5 smartphones
Bibliographic record
Abstract
This study investigates the potential of Android-based smartwatch real-time kinematic (RTK) positioning using single-frequency, multi Global Navigation Satellite Systems (GNSSs) observations, including the L1 GPS, E1 Galileo, B1 BDS and L1 QZSS signals. We evaluate the instantaneous (single-epoch), single-frequency, single-baseline RTK performance under three conditions: (1) zero-baseline with external antenna, (2) short-baseline with external antennas, and (3) short-baseline with internal antennas, all under a stationary setup configuration. The benefit of using the instantaneous RTK model is that it is insensitive to cycle slips. We analyze the smartwatch-to-smartwatch single baseline RTK performance in Dunedin, New Zealand using 4 h of data with a one second measurement interval. No geodetic station is involved in this study, as one of the smartwatches serves as the base station. The tested smartwatches include the Google Pixel Watch 1 (GW1) and the Samsung Galaxy Watch 6 (SW6) that can only collect single-frequency data, and we compare the RTK performance to that of Google Pixel 5 (GP5) smartphones. While using external Trimble Zephyr 2 antennas for the GW1 and SW6 smartwatches, we achieved centimeter-level positioning precisions, with instantaneous integer least squares (ILS) success rates (SR) exceeding 99% for both the zero- and short-baseline data. We found that the RTK performance of both smartwatches is competitive with that of the GP5 smartphones. We also demonstrate that using the internal antennas of the smartwatches results in higher sensitivity to potential multipath effects and poorer GNSS signal quality, thereby reducing the RTK positioning performance. Most importantly, we have shown, for the first time, that one of the smartwatch models can achieve cm-level RTK positioning with a remarkable instantaneous ILS SR exceeding 99% over 4 h of data using the internal smartwatch antennas.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".