A Fuzzy Adaptive Kalman Filter for Spacecraft Formation Navigation
Bibliographic record
Abstract
Over the past two decades, advances in spacecraft technologies have prompted the development of autonomous onboard navigation systems. This paper presents the design of a novel Fuzzy Adaptive Extended Kalman Filter (FAEKF) suitable for estimating the relative position and velocity between two spacecraft flying in formation. A fuzzy adaptation architecture is embedded within a standard Extended Kalman Filter (EKF), thereby allowing the filter to adapt internal noise characteristics that would otherwise remain constant after the initial filter design. Inaccurate tuning of the process and measurement noise covariance matrices within an EKF are commonly a limiting factor in the estimation performance, especially in situations where the behaviour of the noise processes are poorly defined or subject to change. In this context, the proposed approach provides a method to update the process and measurement noise covariances online based on a covariance-matching analysis of the filter residuals. A demonstration of the technique is given through numerical simulations of a spacecraft formation in low-Earth orbit, which are used to compare state estimates from the FAEKF with those from measurement-only and non-adaptive EKF solutions.
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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) | 0.001 | 0.001 |
| 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".