# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 1 Introduction
Popular surrogate models include response surfaces [29], neural networks [30], and Kriging models [31]. Among these, the Kriging model is widely used for its strong predictive ability and limited constraints, achieving the required precision with fewer samples and enhancing computational efficiency [32]. Sensitivity analysis is a widely used uncertainty analysis method that explores the contribution of input variable uncertainty to output response uncertainty and ranks the importance of input variables. Sensitivity analysis includes local sensitivity analysis (LSA) and global sensitivity analysis (GSA). Local sensitivity is limited by the selection of nominal points and cannot directly reflect the contribution of the interaction between a single input variable and multiple input variables to the statistical characteristics of output performance, lacking global and computational stability. Global sensitivity analysis, also known as importance measure analysis, aims to study the impact of input basic variables on output performance uncertainty in structural systems and rank the importance of input variables based on their degree of impact. Based on [PERSON] global sensitivity analysis, it is possible to evaluate the impact of all input variables and their interactions on the output results, not just local variations. This makes the analysis results more comprehensive and able to capture the complex nonlinear relationships and interaction effects in the system. In light of this, this study develops a mechanical simulation model for a rotational modulation structure in a novel hybrid laser INS, isolating angular movements and simulating the major mechanical environments faced by high-dynamic aircraft. Key structural components are analyzed for mechanical performance under high overloads across different composite materials. Using MATLAB 2018b, an adaptive Kriging surrogate model is constructed, incorporating the unavoidable uncertainties in parameters such as geometry, measurement error, and material strength of components such as the base and frames. The model also considers structural deformation and failure in a half-sine shock environment (120 g, 11 ms), using deviations in the INS body coordinate system from the virtual coordinate system as the failure criterion. A reliability analysis model is established based on INS functional failure, applying Bayesian global sensitivity analysis to quantify the contribution of each input variable to failure probability. A dual-random uncertainty analysis model for frame component strength failure is also developed, accounting for dual-random uncertainties in parameter distributions for the outer frame structure. Using Sobol sequence sampling, the model simulates random phenomena to study the effect of changes in input mean values on structural strength failure probability. This analytical approach not only provides a reliability evaluation and design method for novel hybrid INS structures but also offers insights into how design parameter uncertainties affect INS navigation accuracy, thereby supporting advancements in inertial technology.