Performance Evaluation for Fiber Optic Gyroscopes Using Adaptive Belief Rule Base Under Imperfect Data

Performance evaluation for fiber optic gyroscopes (FOG) has become a hot field in recent years. However, such evaluations are often challenged by imperfect data characterized by low quality and nonuniform distribution, which severely affects the accuracy of fiber optic gyroscope performance evaluations. To address this problem, an adaptive belief rule base (BRB) for data quality and distribution (ABRB-QD) method is proposed for modeling FOG performance evaluation under imperfect data. ABRB-QD effectively integrates data quality assessment and distribution adaptation into a unified belief rule structure. In this method, a data quality factor is introduced based on data stability to solve poor data quality issues. An adaptive membership function is established based on data distribution to transform input information, addressing non-uniform distribution problems in imperfect data. Furthermore, a parameter optimization model is developed to enhance the evaluation accuracy of ABRB-QD. Finally, to illustrate the effectiveness of the developed method, a case study of FOG performance evaluation is conducted.