Lidar-Based Rendezvous Navigation for MSR
Bibliographic record
Abstract
This paper presents the results of a simulation of an autonomous rendezvous in Mars orbit. The analysis is conducted in the context of a terminal rendezvous sequence for the Mars Sample Return mission currently planned by the European Space Agency. Relative orbit determination is carried out by an Extended Kalman Filter (EKF) in which the Clohessy-Wiltshire (CW) equations are used for state propagation. The EKF navigation is performed by processing chaser-to-target range and bearing data as observed by a scanning lidar instrument. A measurement model is defined to form angular observations of the target in the chaser satellite reference frame using direction cosines to accomodate the filter. The simulated lidar observations are generated by integration of the two-body equation in the Mars inertial frame. This setup was chosen in order to ensure an independent solution to the filter CW model. We use a stochastic noise model based on the specifications of the Rendezvous Laser Vision (RELAVIS) scanning lidar instrument currently being developed by industry for rendezvous-in-space applications. The filter state corresponds to the position and velocity of the target satellite in the CW frame. The navigation solution is assessed through analysis of residuals, covariance and geometric properties of the problem. Trajectory analysis and computation of rendezvous maneuvers are topics also discussed in the paper. This not only allows a complete simulation of the rendezvous, but also assesses the impact of a given maneuver’s delivery errors on the solution. Results show that both the filter and the guidance algorithms prove reliable in achieving rendezvous within the design requirements.
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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) | 0.000 | 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) | 0.002 | 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 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".