Modern warfare is increasingly dependent on logistical support. The improvement in satellite imaging technology and the increase in the number of satellites in orbit have provided a technical foundation for using satellite observations in military logistics. Due to uncertainties in the processes of production, transport, and observation, the satellite-based observation and state estimation of military logistics exhibit characteristics of uncertainty. This paper proposes an attribute-based staged method to quantify uncertainty, addressing mixed uncertainties during satellite observations of logistics. First, Bayesian estimation is used to quantify the aleatory uncertainty in the process of single-stage logistics observation. Second, evidence theory is adopted to quantify the epistemic uncertainty caused by conflicts in multi-stage logistics observation results and the lack of understanding of production principles. Through the design of the identification framework and the dynamic optimization of basic reliability, key logistics elements are identified, enabling an accurate estimation of the state of military logistics. Finally, the application case is used to validate the effectiveness and accuracy of the proposed method. Compared to conventional evidence theory, the proposed method can make fuller use of multi-source information and reduce the relative error between the estimated value and the true value to below 0.015%.

The uncertainty of the model should be statistically assessed as the foundation for model selection, as there are errors between the digital twin model and the actual system that must be reduced. However, the satellite digital twin model exhibits multi-dynamic, multi-spatial scale, and multi-physical field coupling properties. Additionally, the numerical solution will reveal the stiffness problem of ordinary differential equations and the multi-scale problem of partial differential equations. If many telemetry parameters are updated at the system level, the results will not converge. A multi-granularity and negotiation model updating framework for satellite digital twin method was proposed. The parameters were grouped by correlation analysis and frequency domain analysis. Multi-granularity digital twin models were built based on the requirements, and various granularity models of satellite subsystems and components were produced. The coupling relationship between the satellite structure of different levels was studied, and a negotiation updating method was proposed. Real on-orbit telemetry data were used to verify the framework. According to the research findings, the proposed method updating approach outperforms the unused one by over 50% in terms of accuracy, and the updating results are more thorough and methodical.