Analysis of Different Associative Memory Neural Network for GPS/INS Data Fusion

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. The NPUA has an Artificial Neural Network (ANN) block that uses Associative memoy Neural Networks like Bidirectional Associative Memory Neural Network (BAM-NN) and Hetero Associative memory Neural Network (HAM-NN). The performances of GPS/INS data integration are computed by using HAM-NN and BAM-NN. The performances of both networks are analysed using real time data in terms of Mean Square Error (MSE), Performance Index (PI), Number of Epochs and Accuracy. It is found that HAM is better than BAM in terms of accuracy, MSE, and PI whereas BAM is better than HAM in terms of Number of epochs.