PERFORMANCE ANALYSIS OF VARIOUS ARTIFICIAL INTELLIGENT NEURAL NETWORKS FOR GPS/INS INTEGRATION

Abstract

An aircraft system mainly relies on a Global Positioning System (GPS) to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS absence because of ephemeric error, satellite clock error, multipath error, and signal jamming. To overcome these drawbacks, generally a GPS is integrated with an Inertial Navigation System (INS) mounted inside the vehicle to provide a reliable navigation solution. INS and GPS are commonly integrated using a Kalman filter (KF) to provide a robust navigation solution. In the KF approach, the error models of both INS and GPS are required; this leads to the complexity of the system. This research work presents new position update architecture (NPUA) which consists of various artificial intelligence neural networks (AINN) that integrate both GPS and INS to overcome the drawbacks of the Kalman filter. The various AINNs that include both static and dynamic networks described for the system are radial basis function neural network (RBFNN), backpropagation neural network (BPN), forward-only counter propagation neural network (FCPN), full counter propagation neural network (Full CPN), adaptive resonance theory-counter propagation neural network (ART-CPN), constructive neural network (CNN), higher-order neural networks (HONN), and input-delayed neural networks (IDNN) to predict the INS position error during GPS absence, resulting in different performances. The performances of the different AINNs are analyzed in terms of root mean square error (RMSE), performance index (PI), number of epochs, and execution time (ET).