Increasing the Accuracy of Orbital Position Information from NORAD SGP4 Using Intermittent GPS Readings
Bibliographic record
Abstract
Paramount to any satellite mission is the acquisition of accurate vehicle position and velocity information at any particular point in time. With several satellite tracking and propagation methods available, the use of the Two-Line Elements (TLEs) supplied by the North American Aerospace Defense Command (NORAD) in conjunction with the Simplified General Perturbations Satellite Orbit Model 4 (SGP4) is considered the most popular choice for many low-Earth missions. This is primarily due to the fact that the SGP4 algorithm is open-source and that the TLEs are readily available to the public. Furthermore, they are updated on a fairly consistent – albeit infrequent – basis. If a particular mission requires more stringent accuracy than the SGP4 model can provide, an on-board GPS receiver is often a natural choice. GPS receivers can provide much greater orbital position knowledge at the cost of consuming relatively large amounts of power. This paper describes a technique for increasing orbital determination accuracy through the SGP4 model using a GPS receiver for intermittent orbital information, complemented with a TLE from the most recent epoch. The goal is to increase the precision of the estimates obtained from SGP4 with an effort to minimize the duty cycle required by an onboard GPS receiver. This propagation technique is primarily geared towards nanosatellite-scaled missions with regards to stringent power and antenna pointing requirements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".