Exploring the Potential of Deep Learning Aided Kalman Filter for GNSS/INS Integration: A Study on 2D Simulation Datasets

In the field of positioning and navigation, global navigation satellite system (GNSS) and inertial navigation system (INS) integration is a widely used approach to provide continuous and accurate navigation solutions. The most commonly used algorithm is the error-state extended Kalman filter (ES-EKF). As a modelbased algorithm, the ES-EKF estimates the accumulated error in the strapdown computation based on a priori system models and noise statistics. The performance of the conventional ES-EKF is influenced by the noise model assumptions, system model deficiencies, and a variety of errors in the inertial measurement units (IMU). To improve the accuracy of GNSS/INS integration, this paper proposes a hybridization of model-based and learning-based algorithms to learn the optimal Kalman gain together with the errors in the IMU measurements. The hybridization algorithm reduces the training effort while maintaining interpretability compared to learningbased algorithms. We evaluate the proposed algorithm on the simulated 2D dataset and demonstrate its superior performance over the ES-EKF in terms of both estimated navigation solutions and IMU errors.