Scalable LEO Conjunction Screening using Adaptive Synthetic-Covariance Thresholds
In this work, we present an uncertainty-aware conjunction screening method that replaces a single fixed apsis threshold with object-specific thresholds derived from propagated radial uncertainty.
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Key points
- As orbital populations grow, conjunction screening must search more object pairs for potential collision risks.
- We pair this adaptive screening rule with a binary-search altitude sweep that makes resulting heterogeneous intervals efficient to evaluate at catalog scale with log-linear search complexity.
- We evaluate the method on 33,791 objects from the Space-Track General Perturbations (GP) catalog using the preceding eight public ephemeris snapshots and a seven-day prediction horizon.
- These results demonstrate that uncertainty-aware screening can substantially reduce the candidate burden passed to downstream conjunction analysis, providing a scalable path toward managing increasingly large and heterogeneous orbital populations.
Sources (1)
- [1]Scalable LEO Conjunction Screening using Adaptive Synthetic-Covariance ThresholdsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 10:20 AM
In this work, we present an uncertainty-aware conjunction screening method that replaces a single fixed apsis threshold with object-specific thresholds derived from propagated radial uncertainty.
As orbital populations grow, conjunction screening must search more object pairs for potential collision risks.
Extractive summary: sentences quoted from the sources.