Constrained Diffusion for Data-Scarce Orbital Monte Carlo in Constellation Tasking
Constellation Monte Carlo results depend on the orbital population used to evaluate a tasking policy.
ProofPaper ↗
Key points
- With scarce reference trajectories, replay limits geometric diversity, while independent orbital-element jitter can violate physical constraints.
- We study constrained diffusion for orbital-population augmentation.
- A force-conditioned diffusion model learns a 13-dimensional orbital prior, recovering semimajor axis from perigee altitude and eccentricity; Basilisk propagates each sample under one of five force-model tiers.
- Using 800 reference trajectories, we compare diffusion with jittered bootstrap, per-tier Gaussian mixtures, and a conditional variational autoencoder, and evaluate distributional fidelity, support shift, classical astrodynamics diagnostics, and 4,000 paired GoDSAT-compatible campaigns.
Sources (1)
- [1]Constrained Diffusion for Data-Scarce Orbital Monte Carlo in Constellation TaskingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:29 AM
Constellation Monte Carlo results depend on the orbital population used to evaluate a tasking policy.
With scarce reference trajectories, replay limits geometric diversity, while independent orbital-element jitter can violate physical constraints.
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