A Method for Optical Tracking of On-Orbit Servicing Operations in Geostationary Orbit Using Speckle Interferometry
Bibliographic record
Abstract
On-Orbit Servicing (OOS) missions in geostationary orbit with inter-satellite separations less than one kilometer pose a problem for ground-based electro-optical space surveillance sensors. The tight separations between space objects subtend angles comparable to the size of turbulence (seeing) cells of Earth’s atmosphere making classical ground-based space surveillance detection approaches problematic. Speckle interferometry using a cross-spectrum approach was explored in this research to overcome atmospheric turbulence and enable ground-based measurement of the relative positions of OOS satellites without use of adaptive optics. Field testing of this technique using a medium aperture telescope on co-located geostationary satellites, acting as OOS proxies, found that cross-spectrum speckle interferometry measurements can obtain in-track and cross-track relative position precisions better than 100 meters when the satellites’ apparent angular separations were less than 5 arcseconds. The cross-spectrum method was found to be susceptible to high fringe rotation rates inducing incorrect separation measurements during times when the co-located satellites were at their closest point of approach. While this effect produced spurious fringes during co-located satellite observation tests, this effect is not expected to be encountered during observation of a true OOS mission. Brightness differences did not pose a significant observational limitation as brightnesses of Mprimary = 10.2, Δm = 0.3 and Mprimary = 9.1, Δm = 1.5 were speckled successfully and relative position estimates obtained. Additional testing with small aperture telescope achieved similar brightness and positional measurement performance. Due to the plane-of-sky differential angle observations of the objects a partial observability condition is encountered where the radial component of the servicer’s motion requires more than three hours of observations to converge on a relative orbit solution. The near collinear alignment of the detector with the in-track and cross-track directions causes this partial observability condition. Fortunately, due to the dynamic coupling between the in-track and radial directions of relative orbital motion, the radial component can be estimated and the time to converge is largely dependent on the measurement precision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".