An Adaptive Kalman Filter for Spacecraft Formation Navigation using Maximum Likelihood Estimation with Intrinsic Smoothing
Why this work is in the frame
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Bibliographic record
Abstract
In the interests of enhancing autonomous navigation capabilities for Low Earth Orbit formation flying, this work presents the development of an Adaptive Extended Kalman Filter (AEKF) that estimates relative position and velocity states between two spacecraft. A standard EKF based on the nonlinear dynamics of relative motion is used to provide preliminary state estimates of the formation, which are then corrected through a fixed-window smoothing routine. Since uncertainties in the process and measurement noise covariances within the filter inherently limit the final accuracy of the EKF, an online tuning mechanism is derived using Maximum Likelihood Estimation (MLE) to optimize the noise covariances given an available set of measurements. Inclusion of these adaptations improves filter robustness by allowing the filter to handle situations where noise characteristics of the system are unknown or subject to change, while simultaneously eliminating the need for the initial manual covariance tuning process that accompanies EKF design. Numerical validation of the proposed algorithm is completed by comparing navigation solutions from the AEKF with those obtained from the non-adaptive EKF, using a realistic in-plane elliptical spacecraft formation.
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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