The Satellite Selection Algorithm of GNSS Based on Neural Network

  • Jinben Wei
  • Anmin Ding
  • Kezhao Li
  • Leijie Zhao
  • Yunkai Wang
  • Zhiwei Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 388)

Abstract

With the development of Global Navigation Satellite System (GNSS), there is an increase in the number of visible satellite in navigation and positioning. Calculating all visible satellite not only makes the hardware design of the receiver harder, but also can’t guarantee to improve the accuracy of the positioning results, so it is significant to take a certain satellite selection algorithm to the visible satellites optimization. The traditional optimal satellite selection algorithm is complicated, and it also influences the real-time performance of the navigation and positioning. The subsequent improved satellite selection algorithm has effectively enhanced in robustness, accuracy and real-time. The satellite selection algorithm by using Generalized Regression Neural Network (GRNN) to predict GDOP is proposed. The simulation results showed that the algorithm has improved in robustness, accuracy and real-time, and it has certain exploring value for the GNSS satellite selection algorithm with machine learning.

Keywords

GNSS Satellite selection GDOP GRNN 

Notes

Acknowledgements

This work was supported by National Science Foundation of China (Grant Nos. 41202245, 41272373). The authors would like to thank Liu Yao of Xiangtan University, Shi Yifang of Twente University for their valuable suggestions.

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Jinben Wei
    • 1
  • Anmin Ding
    • 1
  • Kezhao Li
    • 1
  • Leijie Zhao
    • 1
  • Yunkai Wang
    • 1
  • Zhiwei Li
    • 1
  1. 1.Schools of Surveying and Land Information EngineeringHenan Polytechnic UniversityJiaozuoChina

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