Open AccessA wavelet-NARX Model for SDINS/GPS Integration System
Author(s) -
Teena Abbas Ali,
Ahmed Mudheher Hasan
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1094/1/012066
Subject(s) - nonlinear autoregressive exogenous model , inertial measurement unit , global positioning system , kalman filter , inertial navigation system , gyroscope , computer science , navigation system , accelerometer , autoregressive model , extended kalman filter , gps/ins , wavelet , noise (video) , gps signals , artificial intelligence , engineering , artificial neural network , assisted gps , inertial frame of reference , telecommunications , mathematics , econometrics , physics , quantum mechanics , image (mathematics) , aerospace engineering , operating system
Recently, the Strapdown Inertial Navigation System (SDINS) has been successfully integrated with Global Positioning System (GPS) to obtain reliable navigation solutions. SDINS is based on Micro-Electro Mechanical System (MEMS) sensors such as accelerometer and gyroscope sensors. To improve the overall performance of the integrated navigation system, the Inertial Measurement Unit (IMU) readings is treated through effectively band-limiting the high frequency noise using the Discrete Wavelet Multi-resolution Algorithm (DWMRA) as a first step, while the second step is to enhance the navigation position and velocity through utilizing the Nonlinear Autoregressive model with eXogenous inputs (NARX) to fuse GPS and INS systems. The performance of the proposed integrated navigation system is validated through comparison with other systems. Finally, the obtained results suggest a promising and superior prospect for NARX in the field of navigation for low-cost IMU’s during GPS denied signals, since it outperforms the Conventional Neural Network (CNN) and Extended Kalman Filter (EKF) by 84% and 92%, respectively.