Regarding the shock failure of a micro-electro-mechanical system (MEMS) vibrating ring gyroscope (VRG) under high overload, structural dynamic response modeling and failure mechanism analysis are conducted. Based on vibration and elastic wave theories, a dynamic response model of gyroscopic structures to high-g shocks is established. Based on the established dynamic impact response model of the MEMS ring gyroscope, the adhesion and fracture failure mechanisms of the MEMS ring gyroscope are analyzed. The equilibrium displacement for adhesion failure and the sensitive location for fracture failure are derived, and the impact expression at the point of failure is obtained. Through high overload experiments, the impact amplitude and pulse width at the critical failure of the MEMS ring gyroscope a re determined. Raman spectroscopy is used to test the surface stress of the MEMS ring gyroscope after high overload application, and the stress-sensitive locations are found to be consistent with theoretical derivations.

To address the challenge of accurately determining the environmental region of unmanned vehicles during seamless indoor-outdoor positioning, a regional recognition method for seamless indoor-outdoor localization is proposed. Firstly, a joint prediction model integrating particle swarm optimization-support vector machine (PSO-SVM) and hidden Markov model (HMM) is designed. Environmental feature data acquired by sensors serve as model inputs to generate regional recognition results. Secondly, three environmental models are introduced to describe the vehicle's operational environment, with corresponding measurement information selected based on the regional recognition outcomes. Finally, the regional transition probabilities are utilized to update the switching probabilities of the three environmental sub-models in the interactive multiple model (IMM) algorithm, thereby enhancing the accuracy of environmental region recognition and positioning precision for seamless indoor-outdoor navigation. The results of real-vehicle experiment show that the proposed joint recognition method achieves an accuracy of 98.09% in region recognition, representing improvements of 2.13% and 9.53% compared to using PSO-SVM or HMM alone. Further experiments indicate that the proposed seamless positioning method enhances the average positioning accuracy by 43.75% and 22.30% compared to the traditional federated Kalman filter (FKF) algorithm and IMM algorithm, respectively.