Optimizing GNSS/INS Integrated Navigation: A Deep Learning Approach for Error Compensation

This letter addresses challenges stemming from sensor manufacturing processes and technological constraints, such as nonlinear stochastic noise, leading to rapid INS positioning error divergence. To enhance the performance of GNSS/INS (Global Navigation Satellite System/Integrated Navigation Systems) integrated navigation methods, we propose an AI-based adaptive error compensation method. We introduce deep learning to correct INS computational errors, leveraging its precision modeling without strict noise assumptions. Our approach integrates filtering methods and deep learning approaches, avoiding the uncontrollable introduction of positioning errors inherent in end-to-end models' black-box mode. We design a novel deep model structure to improve generalization while reducing parameters and computational complexity. Validation is conducted using a vehicular navigation data acquisition platform, simulating scenarios of GNSS signal loss. Experimental results demonstrate a 77.70% improvement in recognition rates across different road segments compared to traditional methods based on Extended Kalman Filter, indicating significant practical value.