Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks

Hanlin Chen,Tao Geng,Xing Su,Qiang Li,Fei Niu,Zhimin Liu

doi:10.3390/rs13234801

Abstract

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

Highlights

Because the variation of the orbit error has no obvious regularity and the traditional method has no advantage in modeling details, we propose the use of BP and Particle swarm optimization (PSO)–BP neural networks to model the orbit errors of broadcast ephemeris
There are different rules of variation in these different orbits, so we show the satellite model output of the orbit errors of each orbit to analyze the details of the PSO–BP and BP neural network models
According to the curve changes, the outcome of the PSO–BP neural network in the along-track direction at 2–8 h, the crosstrack direction at 12–18 h, and the radial-track direction 2–6 h, which were closer to 0 m, were significantly smaller than those of the BP neural network

Summary

Introduction

With the construction of GNSS in recent years, single-point positioning as one of the main functions of GNSS has become a research hotspot [1,2,3]. In single-point positioning and navigation, the accuracy of broadcast ephemeris is very important [4,5]. The accuracy of GPS and BDS is evaluated from multiple aspects based on MGEX broadcast ephemeris and precision ephemeris. The overall orbit accuracy of GPS is better than 0.3 m, the clock error of the RMS is better than 0.4 m, and the SISRE (Signal-In-Space Range Error) of the RMS is better than 0.5 m; for BDS, the overall orbit accuracy of BDS-2 is better than

Methods

Results

Discussion

Conclusion