Korean Space Collision Environment Assessment Framework Based on 3D-Cell Model
Abstract
Space situational awareness (SSA) requires purpose-matched models across spatial, temporal, and fidelity scales. Building on our previously reported three-dimensional (3D) cell formulation and implementation, this study establishes a reproducible, resolution-aware, catalog-conditioned framework for macroscopic assessment of the low Earth orbit (LEO) collision environment. The framework maps supplied catalog or scenario populations to time-averaged spatial density and target-specific impact metrics while retaining individual-object information. Using a 2025 Space-Track snapshot, we evaluate radial, declination, and right-ascension resolution sensitivity and computational performance for six targets, including two Korean space assets. Normalized expected impact counts range from 0.615 to 1.599 and vary nonmonotonically; for a synthetic 500-km circular target, the result at a 0.25-km radial width is 38.5\% below the 10-km reference. Runtime and memory show direction-dependent trade-offs. Ten annual snapshots show catalog growth from 15,723 objects in 2016 to 28,540 in 2025 and a 7.86-fold increase in the 500-km target metric, driven primarily by Starlink, other payloads, and unknown/TBA records. In a conditional stress test of the proposed 998,240-satellite SpaceX Orbital Data Center population, exact annual probabilities of at least one impact reach 3.75×10−3 and 1.48×10−3 for the 700-km and 1,000-km targets. The framework provides a reproducible, resolution-aware basis for catalog-conditioned environment monitoring, comparative scenario assessment, and prioritization of cases for higher-fidelity follow-up analysis.
Versions
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BibTeX
@misc{kim2026korean,
title = {{Korean Space Collision Environment Assessment Framework Based on 3D-Cell Model}},
author = {Jaewoo Kim and Minchan Song and Jinsung Lee and Jiwoong Yu and Hosik Kam and Jung Hyun Jo and Eun-Jung Choi and Jin Choi and Jaemyung Ahn},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.22877},
eprint = {2608.22877},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.22877},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Korean Space Collision Environment Assessment Framework Based on 3D-Cell Model
AU - Jaewoo Kim
AU - Minchan Song
AU - Jinsung Lee
AU - Jiwoong Yu
AU - Hosik Kam
AU - Jung Hyun Jo
AU - Eun-Jung Choi
AU - Jin Choi
AU - Jaemyung Ahn
PY - 2026
DA - 2026/08/24
PB - arXiv
DO - 10.48550/arxiv.2608.22877
UR - https://arxiv.org/abs/2608.22877
AB - Space situational awareness (SSA) requires purpose-matched models across spatial, temporal, and fidelity scales. Building on our previously reported three-dimensional (3D) cell formulation and implementation, this study establishes a reproducible, resolution-aware, catalog-conditioned framework for macroscopic assessment of the low Earth orbit (LEO) collision environment. The framework maps supplied catalog or scenario populations to time-averaged spatial density and target-specific impact metrics while retaining individual-object information. Using a 2025 Space-Track snapshot, we evaluate radial, declination, and right-ascension resolution sensitivity and computational performance for six targets, including two Korean space assets. Normalized expected impact counts range from 0.615 to 1.599 and vary nonmonotonically; for a synthetic 500-km circular target, the result at a 0.25-km radial width is 38.5\% below the 10-km reference. Runtime and memory show direction-dependent trade-offs. Ten annual snapshots show catalog growth from 15,723 objects in 2016 to 28,540 in 2025 and a 7.86-fold increase in the 500-km target metric, driven primarily by Starlink, other payloads, and unknown/TBA records. In a conditional stress test of the proposed 998,240-satellite SpaceX Orbital Data Center population, exact annual probabilities of at least one impact reach 3.75×10^−3 and 1.48×10^−3 for the 700-km and 1,000-km targets. The framework provides a reproducible, resolution-aware basis for catalog-conditioned environment monitoring, comparative scenario assessment, and prioritization of cases for higher-fidelity follow-up analysis.
ER -