Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer
Bibliographic record
Abstract
The problem of estimating nonlinear systems is an important issue in many engineering applications. One of these applications is in the nanosatellite for Vehicle-to-everything (V2X) communications, which is used to replace sensors in the event of failure or error. Therefore, the accuracy of pre-filter estimation is of great importance. To control the orientation of a satellite, it is important to estimate the attitude accurately. Time-series estimation is especially important in micro and nanosatellites, whose sensors are usually low-cost and have higher noise levels than high-end sensors. Also, the algorithms should be able to run on systems with very restricted computer power. In this paper, an overview of the algorithms used to determine the attitude of nanosatellites, and especially using only the magnetometer sensor, will be discussed. This paper aims to simulate a nanosatellite system to estimate the magnetic field derivative vector using only the magnetometer data with the desired accuracy. The estimated vectors in the pre-filter, along with the magnetometer sensor data, are used to estimate the attitude of the satellite. The estimation of the state vector consisting of the vector part of quaternion and the angular velocity vector is used for the control calculation. To evaluate the accuracy of this pre-filter, a comparison of the magnetometer vector estimator with the body sensor data has been used.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".