Probabilistic Adaptive Extended Kalman Filter for Satellite Localization in the Presence of Measurement Faults
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Bibliographic record
Abstract
In this study, a probabilistic adaptive filtering technique is described for the extended Kalman filter (EKF) algorithm, which is used to estimate low Earth orbit (LEO) satellite position, velocity, and clock bias using global navigation satellite system (GNSS) distance measurements. The proposed probabilistic adaptive extended Kalman filter (pAEKF) algorithm is based on tracking normalized innovation sequences in the filter and calculating the probability of normal operation of the estimation system. The filter gain is adjusted based on this probability to maintain the filter’s tracking performance despite inaccurate measurements. The developed pAEKF algorithm is used in the LEO satellite navigation system, which includes four global positioning system (GPS) receivers, to estimate orbital motion parameters from distance measurements. The orbital motion of the LEO satellite is simulated using the Kepler and Newton equations, taking into account the effect of the J2 perturbation caused by the oblateness of the Earth. In order to evaluate the performance of the proposed method, several simulations are performed where measurement bias type faults (additive measurement faults) are introduced to the GPS distance measurements. The estimation accuracies of the proposed pAEKF, multiple measurement noise scale factors (MMNSFs)–based adaptive extended Kalman filter (AEKF) and conventional EKF were investigated and compared.
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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 | 0.000 |
| 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