Open AccessA novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems
Author(s) -
Al Bitar Nader,
Gavrilov Alexander
Publication year - 2021
Publication title -
navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.847
H-Index - 46
eISSN - 2161-4296
pISSN - 0028-1522
DOI - 10.1002/navi.435
Subject(s) - nonlinear autoregressive exogenous model , gnss applications , kalman filter , computer science , autoregressive model , nonlinear system , control theory (sociology) , artificial neural network , real time computing , artificial intelligence , global positioning system , mathematics , control (management) , telecommunications , physics , quantum mechanics , econometrics
Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel system that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX‐based module is utilized to predict the measurement updates of UKF during GNSS outages. A new offline approach for selecting the optimal inputs of NARX networks is suggested and tested. This approach is based on mutual information (MI) theory for identifying the inputs that influence each of the outputs (the measurement updates of UKF) and lag‐space estimation (LSE) for investigating the dependency of these outputs on the past values of the inputs and the outputs. The performance of the proposed system is verified experimentally using a real dataset. The comparison results indicate that the NARX‐aided UKF outperforms other methods that use different input configurations for neural networks.