Global Navigation Satellite System Positioning Under Interference Conditions

Abstract

This paper contains a model of the flying object (FO) flight trajectory in the geocentric coordinate system and a model of FO position measurement errors using a Global Navigation Satellite System (GNSS) receiver under interference conditions. The size of the FO coordinate errors in the horizontal and vertical planes ranged from +20.0 m to −20.0 m. This paper’s contribution is the creation of algorithms for processing GNSS signals under interference conditions based on the Savitzky-Golay filter and neural network. The simulation results confirmed that the Savitzky-Golay filter when applied in the GNSS receiver, can suppress FO position measurement errors. For example, if absolute FO position measurement errors due to GNSS interference were from 0.0 m to 5.0 m, and the mean square error of these errors was 0.441 m2, the Savitzky-Golay filter reduced them to a value from 0.0 m to 0.8 m, and the mean square error of these errors was equal was 0.0195 m2. In this case, the mean error value in the FO position after filtering with the Savitzky-Golay filter was 0.32 m, and the mean square error was 0.019 m2. Advanced machine learning techniques in the form of neural networks were also used to suppress errors from the output of the Savitzky-Golay filter. The simulation confirmed that if the FO position errors at the neural network’s input ranged from 0 m to +0.8 m, the neural network reduced them to 0.0 m to 0.5 m. The mean FO position error value was 0.12 m, and the mean square error was 0.0089 m2. The results of this study may contribute to more precise and reliable FO navigation and improve the use of GNSS systems in interference conditions.