Doppler-Based Positioning with LEO Satellites: A Survey of Techniques, Challenges, and Opportunities
Bibliographic record
Abstract
The advent of Low Earth Orbit (LEO) satellite constellations, such as Starlink and OneWeb, has introduced new possibilities for global positioning, navigation, and timing (PNT) services. LEO satellites, operating at altitudes between 500 km and 2,000 km, offer advantages such as reduced signal latency, stronger signal strength, and more frequent satellite visibility compared to traditional Global Navigation Satellite Systems (GNSS). Among the various positioning techniques, Dopplerbased positioning has emerged as a promising alternative or complement to GNSS, leveraging the Doppler shift caused by the relative motion between the satellite and receiver. This survey provides a comprehensive review of Doppler-based positioning techniques using LEO satellites, tracing their historical development and examining both single-satellite and multi-satellite methods. Additionally, it explores the challenges and opportunities inherent in using LEO signals for Doppler-based positioning. The paper also highlights recent advancements in the field, including innovative algorithms and the potential for real-time applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".