An Adaptive Kalman Filter for Motion Esitmation/Prediction of a Free-Falling Space Object Using Laser-Vision Data with Uncertain Inertial and Noise Characteristics
Why this work is in the frame
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Bibliographic record
Abstract
A computationally efficient, noise adaptive Kalman filter is presented for the motion estimation and prediction of a free-falling tumbling satellite (target). The filter receives only noisy pose measurements from a laser vision system aboard another satellite (chaser) at a close distance in a neighboring orbit. The filter estimates the full sates, all the inertia parameters of the target satellite, as well as the covariance of the measurement noise. A comprehensive dynamics model that includes aspects of orbital mechanics is incorporated for accurate estimation. The discrete-time model, which involves a state-transition matrix and the covariance of process noise, is derived in closed form, thus rendering the filter suitable for real-time implementation. The statistical characteristics of the measurement noise is formulated by a state-dependent covariance matrix. This model allows additive quaternion noise, while preserving the unit-norm property of the quaternion. The convergence properties of the developed filter is demonstrated by simulation and experimental results. These results also demonstrate that the filter can continuously produce accurate estimates of pose even when the vision system is occluded for tens of seconds.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it