Dynamic Process Noise Covariance Adjustment in GNSS/INS Integrated Navigation Using GRU-SAC for Enhanced Positioning Accuracy

The Kalman filter is widely used in GNSS/INS integrated navigation systems to fuse information, resulting in high precision and robust positioning performance. In the Kalman filter, the accuracy of the process noise covariance matrix directly affects the precision of the positioning results. We propose a Soft Actor-Critic (SAC) algorithm based on Gated Recurrent Unit neural networks (GRU-SAC) to dynamically adjust the process noise covariance matrix online using sequential observation data to improve positioning accuracy. We model the decision-making process as a Partially Observable Markov Decision Process (POMDP) and incorporate multiple information sources as the system state. The GRU network is used to extract temporal features from the navigation data and to address memory consumption issues commonly associated with POMDPs. And the SAC algorithm continuously adjusts the process noise covariance based on observations from the Kalman filter, allowing the algorithm to perform better in complex, dynamic, and changing navigation environments. Additionally, we provide detailed design and deployment strategies for both loosely-coupled and tightly-coupled systems. Extensive experiments have been conducted to validate the effectiveness of our method. The results show that our approach generalizes well across a wide range of preset process noise covariance matrices and performs excellently in suppressing error drift during GNSS outages.