CNN based GPS/INS data integration using new dynamic learning algorithm

Abstract

Aircraft navigation relies mainly on Global Positioning System (GPS) to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS outages within urban areas, where satellite signals are blocked. To overcome this drawback, generally GPS is integrated with inertial sensors mounted inside the vehicle to provide a reliable navigation solution. Inertial Navigation System (INS) and GPS are commonly integrated using a Kalman filter (KF) to provide a robust navigation solution, overcoming situations of GPS satellite signals blockage. This work presents New Position Update Architecture (NPUA) for GPS and INS data integration. NPUA uses Constructive Neural Network (CNN) for training and prediction. New dynamic learning algorithm (NDLA) has been developed for CNN to predict the INS position error and determine the accurate position of the moving aircraft during signal blockages in GPS. GPS and INS data are integrated using CNN in NPUA and the results are simulated. The output obtained using CNN is compared with the performance of Multilayer Feed Forward Network (MFNN). The CNN is found to have optimal topology when compared to MFNN. CNN has better learning ability, network constructing ability and accuracy when compared to MFNN.