The SPHERES Navigation System: from Early Development to On-Orbit Testing
Bibliographic record
Abstract
Engage and Reorient Experimental Satellites (SPHERES) facility for the testing of for-mation flight and autonomous docking algorithms inside the International Space Station (ISS), in NASA’s reduced gravity aircraft and in a 1-g laboratory environment. To pro-vide SPHERES with reliable and accurate position, velocity, attitude and angular rate estimation, an innovative state estimation system based on ultrasound transmission has been developed. An extended Kalman filter (EKF) processes time-of-flight data collected by ultrasonic receivers, as well as angular rate measurements provided by gyroscopes, to compute the state estimates required by the satellites when maneuvering. To increase the robustness of the system, the EKF has been augmented with a fault detection capability that uses the filter innovation (residual) to diagnose measurement errors. Two versions of the algorithm were successfully implemented and used on the SPHERES facility onboard the ISS during a series of five test sessions, from May 2006 to November 2006. This paper describes both versions in detail, along with difficulties encountered during the implemen-tation on the hardware and their solution. Results from experiments performed in the ISS to validate the algorithms are also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".