An Interpretable Hierarchical Belief Rule Base for the Performance Evaluation of Laser Inertial Measurement Units

Laser strapdown inertial measurement units (LIMUs) are critical components of autonomous navigation and attitude determination systems used in mission-critical platforms such as spacecraft. Their performance directly affects the reliability and safety of space missions. To address the challenges posed by high-dimensional indicator sets, limited data, and stringent interpretability requirements, this paper proposes an interpretability-aware hierarchical Bayesian belief rule base model, termed IH-BRB, for LIMU performance evaluation. First, a five-level hierarchical evaluation structure is developed based on the physical architecture and error-propagation relationships of a LIMU, thereby reducing rule base complexity and improving inference traceability. Second, an interlayer transmission mechanism based on the expected grade scores and entropy-derived reliability is introduced to preserve essential uncertainty information and dynamically adjust the attribute weights of upper-level BRB nodes. Third, semantic consistency constraints are imposed on the conditional probability tables and rule-consequent belief distributions to prevent semantic drift during data-driven optimization. The proposed model is validated using an engineering LIMU dataset with expert-defined target grades to assess its consistency with the established grading criterion. The results show that IH-BRB maintains a competitive evaluation performance while preserving physically meaningful rule semantics and layer-wise traceability.