Distance Measurement by a Galileo GNSS Receiver Using the Signal E6-C

Abstract

The new concept of air traffic control favours the global navigation satellite system (GNSS) Galileo over the classic navigation system. Practice shows that GNSSs are unreliable when operating in the presence of interference. That is why we examined the feasibility of measuring the distance between a Galileo receiver on board a flying object and a Galileo satellite using the E6-C pilot signal. We derived algorithms to measure the distance between the GNSS Galileo receiver and the satellite in the presence of narrowband interference. To derive algorithms for processing the E6-C signal in a global navigation satellite system receiver, we used a quasi-optimal nonlinear filtering method with a quadratic loss function. When creating the simulation models, we set the following conditions: the user receiver can demodulate the primary code of the E6-C signal, the measurement results are not affected by the E6-C transition through the atmosphere and troposphere, and the receiver operates under interference conditions. We have not yet verified the proposed distance measurement method with receiver hardware designed according to our proposed architecture. The simulation results showed that the distance measurement error between the Galileo satellite and the user receiver was +0.16 m and remained approximately the same throughout the simulation period. We verified by simulation, according to the algorithms and the selected positions of the satellites, that if we can measure all four distances of the Galileo user’s receiver from the cooperating satellites with errors that do not exceed 0.16 m, then the maximum errors in determining his X, Y, and Z coordinates would be 0.28 m. Their maximum root mean square (RMS) values were 0.11 m, with the magnitudes of these errors depending on the receiver’s position relative to the satellites. The advantage of this method is the very short convergence time. The disadvantage of the presented algorithms is the need for matrix multiplication, which places high demands on the signal processor’s data processing speed. We assume that this shortcoming can be eliminated by simplifying the algorithms, a direction that warrants further research.