Online Semi-Supervised Transformer for Resilient Vehicle GNSS/INS Navigation

The Inertial Navigation Systems (INS) and Global Navigation Satellite Systems (GNSS) integrated navigation system is widely employed for vehicular positioning. However, obstacles in the wireless signal path often lead to GNSS outages, diminishing the reliability of positioning. Many deep learning-based methods have been proposed for their high accuracy and outstanding generalization capabilities. Nevertheless, they overlook the constraints posed by limited computational resources and memory capacity within real-time, online navigation systems. To strike a balance between positioning accuracy and model complexity, this paper proposes an Online Semi-supervised Transformer (OSS-Transformer) model for GNSS/INS integrated navigation that functions effectively during GNSS signal outages and has low computational requirements. This model comprises three critical components: 1) A Transformer-based architecture for adaptive feature extraction; 2) A self-supervised Masked Language Model (MLM) task to understand contextual relationships and feature distributions within INS data; 3) An improved memory replay technique for efficient online network training. Verified by the road test, the proposed OSS-Transformer based integrated navigation system reaches a maximum error of 6.02 m during 40 s outages and 9.81 m during 100 s outages, significantly outperforming traditional deep-learning methods. Additionally, the improved memory replay technique reduces loss by 26.23% compared to conventional methods, making it feasible on navigation devices with constrained computational resources and memory.