On the Autonomous in Orbit Calibration of Satellite Attitude Sensors
Bibliographic record
Abstract
Typically, satellite attitude control software includes a few algorithms to determine satellite attitude. The most accurate Attitude Determination Mode (ADM) is designated as the primary ADM and is used as the reference base (physical platform) to calibrate redundant auxiliary sensors. They are not involved in closed loop control of satellite attitude in the primary ADM and can therefore be considered as passengers; however, they are used as part of the control loop under special circumstances: e.g. eclipse, primary sensor failure, recovery from Safe Hold Mode, and special attitude manoeuvres. Such a strategy has been adopted by two Canadian satellite missions: RADARSAT–1 and SciSat, both of which are operated by the CSA Mission Control Centre (MCC). In developing its strategies for small and micro satellite design and operation, the Canadian Space Agency (CSA) recognises the benefits of providing this new generation of satellites with the capability of extended operation autonomy. The goal of such autonomy is to reduce the cost and complexity of satellite mission support, which is in line with the microsatellite philosophy. A large part of the early stages of satellite operation is devoted to the evaluation of attitude accuracy and the calibration of attitude sensors in orbit. This article proposes a general approach to solving the sensor calibration problem autonomously using an onboard processor with a sub-optimal Kalman Filter (KF). The approach is illustrated with RADARSAT-1 magnetometer calibration. This paper proposes the transfer of calibration authority from a ground-based MCC to on-board algorithms, while preserving the underlying calibration strategy. A recursive Kalman Filter [5] algorithm is used for real time on-board estimation of the calibration parameters of a satellite’s attitude sensors. To have the method applicable for a microsatellite with an inexpensive, resource-limited processor, some effort was spent to suboptimise the developed Kalman Filter in order to make it more economical from a computational loading point of view. The approach presented in this paper avoids the computation of covariance matrices and weight coefficients – which are the most computationally demanding aspects of Kalman Filtering – by approximating these coefficients as analytical functions of time [2]. The decision concerning the insertion of the derived estimates into the control algorithms is based on a criterion of trust, including evaluation of the values of the estimates and their stability in time after some pre-determined observation period.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".