A satellite cluster observation method for logistics status of industry chain with quantifiable uncertainty

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%.