Vatasta Koul, Kyushu Institute of Technology; Koju Hiraki, Kyushu Institute of Technology
https://doi.org/10.64861/CZXF9453
Keywords: Space Situational Awareness, Conjunction Assessment, Orbit Congestion, Orbital Uncertainty, Modelling
Abstract:
Low Earth Orbit congestion is increasing conjunction screening workload and complicating operational prioritization across Space Situational Awareness systems. While conjunction assessment is typically performed at the event level using pairwise screening and probability of collision metrics, there is limited capability for forecasting where structural screening burden will concentrate under realistic orbital uncertainty growth. This work presents an uncertainty-calibrated predictive mapping framework that generates spatio-temporal screening intensity forecasts for congested LEO regimes, enabling regime-level monitoring and planning over multi-day horizons._x000D_
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The goal of this research is to introduce a mid-layer SSA capability that quantifies where and when conjunction screening density is expected to intensify, incorporating empirically derived orbital state uncertainty growth rather than assumed covariance inflation. The framework propagates trajectories for object populations over 3 to 7 day horizons using two-body dynamics with J2 secular perturbations. Rather than modeling drag as an explicit dynamical force, its effect is captured empirically through the calibrated uncertainty growth model, reflecting real, observed orbital decay behavior. Uncertainty evolution is modeled in the radial, along-track, and cross-track frame to capture anisotropic dispersion behavior that drives structural screening accumulation._x000D_
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A key contribution of this work is empirical uncertainty calibration using multi-year historical TLE differencing across thousands of tracked LEO objects. Variance growth rates are statistically characterized as functions of time-since-epoch and stratified by altitude shell and inclination band. Preliminary analysis indicates along-track dominated growth with measurable regime-dependent variation in radial and cross-track components. These calibrated parameters replace idealized Gaussian covariance assumptions and are incorporated into Monte Carlo dispersion sampling, producing forecast envelopes that reflect operational orbit prediction degradation._x000D_
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Predicted close-approach geometries within a defined structural proximity window are aggregated into altitude-time bins and inclination corridor groupings. A Corridor Screening Intensity (CSI) metric is introduced, defined as the expected number of close-approach events per tracked object per propagation day, normalized by corridor population. This metric enables quantitative comparison of screening burden across regimes and provides a forecasting capability distinct from event-level probability of collision calculations._x000D_
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Demonstration cases across representative LEO regimes reveal measurable conjunction concentration patterns when empirically calibrated uncertainty is applied. Screening intensity hotspots shift in time relative to nominal propagation, and normalized burden varies significantly between certain inclination corridors of comparable population size. Sensitivity analysis assesses whether moderate variations in along-track growth parameters produce observable hotspot migration along orbital trajectories, highlighting the structural influence of uncertainty evolution on screening demand. Multi-day forecasts are generated for representative historical epochs and compared against observed conjunction clustering derived from public conjunction reporting traffic. Temporal correlation between predicted screening intensity peaks and observed report surges in altitude and inclination concentration bands will be analysed._x000D_
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This framework is not intended to replace operational probability of collision calculations derived from conjunction data messages. Instead, it complements existing workflows by providing a regime-level view positioned between pairwise conjunction outputs and high-level congestion statistics. Potential applications include sensor tasking prioritization, screening workload forecasting, and forward-looking capacity planning under continued catalog growth. By incorporating uncertainty realism into structural conjunction aggregation, this work contributes a scalable forecasting capability to support operational SSA in increasingly congested orbital environments._x000D_
Date of Conference: September 15-18, 2026
Track: Conjunction/RPO