Scalable LEO Conjunction Screening using Adaptive Synthetic-Covariance Thresholds

Vedant Srinivas; Grace Ra Kim; Duncan Eddy; Mykel J. Kochenderfer

cs.ROOct 8, 2026

Scalable LEO Conjunction Screening using Adaptive Synthetic-Covariance Thresholds

Authors: Vedant Srinivas, Grace Ra Kim, Duncan Eddy, Mykel J. Kochenderfer

Organizations: Stanford University 496 Lomita Mall Stanford, CA 94305

Abstract

As orbital populations grow, conjunction screening must search more object pairs for potential collision risks. Existing pipelines use early-stage filters, such as an apsis filter, to eliminate pairs whose orbital altitude ranges cannot produce a close approach. However, these filters apply the same fixed threshold to every object, making the screening unnecessarily conservative for well-constrained objects while potentially insufficient for objects with greater orbit-prediction uncertainty. 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. The resulting thresholds vary across both objects and prediction time, becoming narrower when propagated uncertainty is small and wider when uncertainty grows. 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. The adaptive sieve retains 162.8 million candidate pairs, reducing the candidate set by 28.78% relative to a fixed 50 km apsis threshold and by 71.48% relative to brute-force all-pairs screening. It retains all close approaches identified in two independently propagated reference sets. At the largest tested catalog size, the binary-search sweep is 27 times faster than exhaustive interval evaluation while returning the same candidate set. 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.

Figures & tables

Fig. 1: Conjunction-screening filters: (a) apsis, (b) path, and (c) phase.

Method	Pairs retained	Retained fraction	Reduction vs. brute force
Brute force	570,898,945	100.00%	0.00%
Fixed 50 km	228,619,849	40.05%	59.95%
Adaptive	162,819,449	28.52%	71.48%

TABLE I: Seven-day all-vs-all screening results. Reduction is reported relative to the unscreened pair universe.

Panel of Figure 3 — Fig. 2: Adaptive-threshold evolution for three representative objects. Solid curves show the orbital-window median and shading shows the local min–max envelope. The dashed line marks the fixed $50\text{\,}\mathrm{km}$ baseline. Logarithmic vertical axes are used for the payload and rocket-body cases, while a linear vertical axis is used for the debris case.

Panel of Figure 4 — Fig. 3: Scaling of candidate-pair count and CPU screening time with catalog size. The $O(N^{2})$ and $O(N\log N)$ curves are theoretical scaling guides rather than fitted models. Orbit propagation and covariance generation are excluded.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 5 — Fig. 4: Comparison of propagated synthetic and reported radial variance at TCA for 97 LEO object-cases from the NASA CARA PcTestCaseCDMs dataset. The case-level comparison shows exact agreement and factor-of-two and factor-of-ten regions, while the agreement distribution summarizes the symmetric factor between the synthetic and reported radial variances.

Axis	Cases	Median ratio	Underpredicted	Within $2\times$	Within $10\times$	Within $100\times$
Radial	97	5.95	22.7%	12.4%	48.5%	91.8%
Along-track	97	0.18	70.1%	16.5%	49.5%	66.0%
Cross-track	97	21.77	11.3%	17.5%	38.1%	76.3%

Appendix

TABLE II: Agreement between propagated synthetic and reported CARA covariance diagonal entries.

Panel of Figure 7 — Fig. 5: Sensitivity of the seven-day adaptive threshold to the number of preceding ephemeris records used to construct the synthetic covariance. The dashed line marks the selected configuration, $n_{\mathrm{recent}}=8$ .

Panel of Figure 8 — Fig. 6: STM-propagated RTN position uncertainty for three representative objects. Curves show the radial, along-track, and cross-track standard deviations over the seven-day prediction horizon, with shading indicating the corresponding orbital-period minimum–maximum envelope.

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