Probabilistic Compliance Monitoring for Post-Mission Disposal Guidelines in Low Earth Orbit

As space becomes more crowded and debris risks grow, the probability of Kessler syndrome not staying as just a theoretical threat is also rising. Facing the worst of it is the Low Earth Orbit (LEO), which has been the hub of commercial, military and satellite launches in the past few decades due to the several operational advantages it offers. This warrants the urgent need to engage in effective Post Mission Disposal (PMD) practices which reduce collision risks and further debris growth by limiting the long-term presence of defunct space objects. Countries vary widely in how they enforce PMD rules, thus leaving compliance as a choice. To address this, this study introduces a transparent, scalable, and probabilistic framework for compliance monitoring with PMD guidelines based on publicly available data. A satellite fleet of 4764 satellites is compiled with certain filters for further analysis. First, decay estimates are calculated through a semi-analytical decay model to estimate the timely passive reentry feasibility of active satellites under purely passive decay and end-of-life assumptions. This predicts a baseline global compliance rate of 16.2%. Next, this baseline is integrated with satellite characteristic and policy factors using a Bayesian logistic regression model, trained on 907 labeled satellites (96.1% compliant) with a likelihood downweighting factor of α=0.05 to mitigate selection bias. The model identifies altitude and altitude-age interactions as the dominant drivers of compliance, while policy-related features contribute only weakly, reflecting assumptions made in the current analysis and low levels of binding enforcement worldwide. When applied to the full fleet, the model predicts that 79.3% of satellites are likely compliant (probability of compliance>=0.7), consistent with IADC and ESA estimates. The model is then used to examine compliance trends across countries, different probability-based risk thresholds, launch years, orbital altitudes, and area-to-mass (A/M) ratios. Overall, the approach provides a data-driven method for evaluating PMD compliance that is transparent, interpretable, and scalable to future satellite populations globally.