Random Optimization Algorithm on GNSS Monitoring Stations Selection for Ultra-Rapid Orbit Determination and Real-Time Satellite Clock Offset Estimation

Joint Authors

Yang, Xu
Wang, Qianxin
Xue, Shuqiang

Source

Mathematical Problems in Engineering

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-17, 17 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-02-14

Country of Publication

Egypt

No. of Pages

17

Main Subjects

Civil Engineering

Abstract EN

Geographical distribution of global navigation satellite system (GNSS) ground monitoring stations affects the accuracy of satellite orbit, earth rotation parameters (ERP), and real-time satellite clock offset determination.

The geometric dilution of precision (GDOP) is an important metric used to measure the uniformity of the stations distribution.

However, it is difficult to find the optimal configuration with the lowest GDOP when taking the 71% ocean limitation into account, because the ground stations are hardly uniformly distributed on the whole of the Earth surface.

The station distribution geometry needs to be optimized and besides the stability and observational quality of the stations should also be taken into account.

Based on these considerations, a method of configuring global station tracking networks based on grid control probabilities is proposed to generate optimal configurations that approximately have the minimum GDOP.

A random optimization algorithm method is proposed to perform the station selection.

It is shown that an optimal subset of the total stations can be obtained in limited iterations by assigning selecting probabilities for the global stations and performing a Monte Carlo sampling.

By applying the proposed algorithm for observation data of 201 International GNSS Service (IGS) stations for 3 consecutive days, an experiment of ultra-rapid orbit determination and real-time clock offset estimation is conducted.

The distribution effects of stations on the products accuracy are analyzed.

It shows that (1) the accuracies of GNSS ultra-rapid observed and predicted orbits and real-time clock offset achieved using the proposed algorithm are higher than those achieved with the traditional method having the drawbacks of lacking evaluation indicators and being time-consuming, corresponding to the improvements 17.15%, 19.30%, and 31.55%, respectively.

Only using 30 stations selected by the proposed method, the accuracies achieved reach 2.01 cm (RMS), 4.93 cm (RMS), and 0.20 ns (STD), respectively.

Using 60 stations, the accuracies are 1.47 cm, 3.50 cm, and 0.17 ns, respectively.

(2) With the increasing number of stations, the accuracies of the Global Positioning System (GPS) orbit and clock offset improve continuously, but more than 60 stations, the improvement on the orbit determination becomes more gradual, while for more than 30 stations, there is no appreciable increase in the accuracy of the real-time clock offset.

American Psychological Association (APA)

Yang, Xu& Wang, Qianxin& Xue, Shuqiang. 2019. Random Optimization Algorithm on GNSS Monitoring Stations Selection for Ultra-Rapid Orbit Determination and Real-Time Satellite Clock Offset Estimation. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1197101

Modern Language Association (MLA)

Yang, Xu…[et al.]. Random Optimization Algorithm on GNSS Monitoring Stations Selection for Ultra-Rapid Orbit Determination and Real-Time Satellite Clock Offset Estimation. Mathematical Problems in Engineering No. 2019 (2019), pp.1-17.
https://search.emarefa.net/detail/BIM-1197101

American Medical Association (AMA)

Yang, Xu& Wang, Qianxin& Xue, Shuqiang. Random Optimization Algorithm on GNSS Monitoring Stations Selection for Ultra-Rapid Orbit Determination and Real-Time Satellite Clock Offset Estimation. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1197101

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1197101