Mechanism- and Data-Informed Hierarchical Bayesian Electro-Optical Reconnaissance Performance Evaluation

Abstract

Abstract

Performance evaluation of electro-optical reconnaissance systems is affected by multi-source uncertainty, data heterogeneity, and model mismatch. This paper presents a hierarchical Bayesian evaluation framework that combines physical constraints with data from different sources. The capability model represents detection, recognition, tracking, and localization through four bounded indicators. Physical relations inform the prior structure, while observation, parameter, and hyperparameter layers represent variation within and between data sources. Joint modeling describes the dependence among the indicators and provides posterior predictions with uncertainty intervals. The reported comparisons examine prediction error, robustness across scenarios, uncertainty calibration, and performance at different sample sizes. These results illustrate the use of a common probabilistic framework for comparing capability estimates and their uncertainty.