Autonomous navigation and control strategy for deorbiting of satellites with unknown orbital parameters
Bibliographic record
Abstract
The recent shift from a 25-year to a 5-year deorbit requirement underscores the urgent need for more robust, flexible satellite disposal methods. In this work, we present an autonomous, self-contained guidance and control approach that uses only a magnetometer, sun sensor, and gyroscope to determine orbital parameters and execute a propellant-efficient deorbit. By blending Particle Swarm Optimization and Power Spectral Density analysis, the proposed method accurately refines unknown orbital parameters without recourse to high-end sensors or external tracking. Simulation results conducted on multiple orbits—from 400 km (ISS altitude) up to 650 km Sun-synchronous trajectories–demonstrate consistent convergence within a defined cost threshold, ensuring reliable five-year compliance. This integrated architecture minimizes mass and power overhead, making it well-suited for budget-conscious CubeSats and other small missions. Moreover, by automating both orbit determination and continuous thruster control within a single compact framework, the solution bridges the gap between academic innovation and immediate industry demands for safe, cost-effective end-of-life satellite disposal. The presented work substantially enhances existing deorbiting paradigms by offering a feasible, low-complexity route to sustainable space operations under increasingly stringent regulatory 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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".