A Hybrid Data Fusion Approach to AI-Assisted Indirect Centralized Integrated SINS/GNSS Navigation System During GNSS Outage

The main challenges for integration between low-cost strap-down inertial navigation system (SINS) and global navigation satellite system (GNSS) are to manage the non-Gaussian measurement noises and to access the reliable measurements during GNSS outages. Therefore, in this paper, some approaches are taken in the proposed integrated navigation system: (1) using an iterative empirical mode decomposition (EMD) interval thresholding de-noising method for low-cost inertial measurements to provide smoother and more accurate SINS measurements with less complexity, (2) designing an enhanced indirect centralized correntropy Kalman filter using a novel fuzzy parameter adjustment approach, which adaptively changes the estimation algorithm parameters to further improve the robustness of the proposed algorithm in the presence of dynamical maneuvers and non-Gaussian measurement noise, and (3) utilizing a pre-filtered nonlinear autoregressive network with exogenous inputs (PFNARX) designed to provide reliable interrupted positioning information during GNSS outages. The proposed SINS/GNSS system is experimentally evaluated through real-world flight tests. The obtained results reveal that the designed integration algorithm remarkably improves the estimation accuracy by adaptively tuning the parameters in an improved correntropy Kalman filter and an efficient and well-trained artificial neural network-based model in the absence of GNSS signal.