Starling Formation-Flying Optical Experiment (StarFOX): System Design and Preflight Verification
Bibliographic record
Abstract
The Starling Formation-Flying Optical Experiment (StarFOX) is intended as the first on-orbit demonstration of autonomous distributed angles-only navigation for spacecraft swarms. StarFOX applies the angles-only Absolute and Relative Trajectory System (ARTMS), a navigation architecture consisting of three innovative algorithms: image processing, which identifies and tracks multiple targets in images from a single camera without a priori relative orbit knowledge; batch orbit determination, which autonomously initializes orbit estimates for visible swarm members; and sequential orbit determination, which continuously refines the swarm state by fusing measurements from multiple observers exchanged over an intersatellite link. Nonlinear dynamics and measurement models provide sufficient observability to estimate absolute orbits, relative orbits, and auxiliary states using only bearing angles without maneuvers. StarFOX will be conducted using a four-CubeSat swarm as part of the NASA Starling mission, and simulations of experiment scenarios demonstrate that ARTMS meets mission performance requirements. Results indicate that mean bearing angle errors are below 35′′ ([Formula: see text]), initial target range errors are below 20% of true separation, and steady-state range errors are below 2% ([Formula: see text]). Absolute orbit estimation accuracy is on the order of 100 m. Hardware-in-the-loop tests display robust navigation under a variety of conditions, enabling autonomous, ubiquitous navigation with minimal ground interaction for future distributed missions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".