Autonomous optical navigation using nanosatellite-class instruments: a Mars approach case study
Bibliographic record
Abstract
<p>Star trackers must be calibrated prior to flight so that they can make accurate measurements of star positions within the instrument field of view. This calibration is usually performed in atmosphere and after the sensor is launched; it is not uncommon to observe a small shift in some of the calibration parameters. In this paper, we explore several autonomous strategies for on-orbit recalibration of star trackers. We present an improved version of a popular camera model, develop optimizations to identify optimal parameter values, and validate performance using the data collected from on-orbit sensors. When compared with human-mediated batch processing, autonomous methods have comparable reliability, performance, and commissioning time. The sensor datasets used in this paper come from six Sinclair Interplanetary ST-16 star trackers launched between November 2013 and July 2014. Both batch and autonomous approaches to on-orbit calibration yield improvements in measurement availability as well as a 20%-80% reduction in residual geometric error compared to ground calibrations.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".