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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".