Vector tracking loop assisted by the neural network for GPS signal blockage

Abstract

This paper investigates incorporation of the neural network (NN) into the vector tracking loop (VTL) for improving the Global Positioning System (GPS) positioning quality during satellite signal blockages. The VTL architectures of the GPS receivers provide several important advantages as compared to the scalar tracking loop (STL) architectures. The tracking and navigation modules in the VTL are not independent anymore as compared to those in the traditional STL since the user’s position can be determined by using the information from the satellites with stronger signals and the weak signals in turn can then be predicted on the basis of the states of the user. Signal blockage and reflections from buildings and other large, solid objects can lead to accuracy degradation. One of the merits of the VTL is that the tracking loop can be assisted from the navigation solution. The NN is used to bridge the GPS signal and prevent the error growth due to signal outage from contaminating the entire tracking loop. In the training stage, the output information from the discriminator and navigation filter is adopted as the inputs of the neural network. The NN is employed for predicting adequate numerical control oscillator (NCO) inputs, i.e., providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system. Two types of networks involved are the radial basis function neural network (RBFNN) and the adaptive network-based fuzzy inference system (ANFIS). Performance evaluation for the NN aided architecture as compared to the unaided VTL is carried out. The results show that the VTL with the support of neural networks can effectively provide improved performance during GPS signal blockages.