ZENG Anmin, YANG Yuanxi, JING Yifan, MING Feng. Systematic Bias Compensation Model of Inter-system Bias and Its Performance Analysis for BDS/GPS Fusion Positioning[J]. Geomatics and Information Science of Wuhan University, 2017, 42(10): 1423-1430. DOI: 10.13203/j.whugis20150808
Citation: ZENG Anmin, YANG Yuanxi, JING Yifan, MING Feng. Systematic Bias Compensation Model of Inter-system Bias and Its Performance Analysis for BDS/GPS Fusion Positioning[J]. Geomatics and Information Science of Wuhan University, 2017, 42(10): 1423-1430. DOI: 10.13203/j.whugis20150808

Systematic Bias Compensation Model of Inter-system Bias and Its Performance Analysis for BDS/GPS Fusion Positioning

Funds: 

The National Program on Key Basic Research Project 2016YFB0501700

the National Natural Science Foundation of China 41474015

the National Natural Science Foundation of China 41374019

the National Natural Science Foundation of China 41374003

the National Natural Science Foundation of China 41274040

More Information
  • Author Bio:

    ZENG Anmin, PhD candidate, assistant researcher, specilizes in data processing for dynamic geodetic measurement and terrestrid reference frame. E-mail: Zeng_anmin@163.com

  • Received Date: December 30, 2015
  • Published Date: October 04, 2017
  • Navigation users will significantly benefit from BDS and GPS positioning fusion in terms of availability, accuracy and reliability. However, for single point positioning, systematic biases between multi-GNSS systems cannot be eliminated completely, thus the accuracy of positioning and navigation is not always improved with the un-difference measurements of multi-GNSS systems. In this paper, an integrated BDS/GPS positioning model with unknown systematic parameters that compensates for systematic bias is proposed. Furthermore, a Bayesian estimation of fusion positioning model is specifically investigated in which the priori information of the additional parameters is taken into account. Real data collected from different areas with different types of receivers are used to verify those new algorithms. The results show that (a) receiver-dependent inter-system biases are quite evident, while the size of the system bias varies with the receiver type; (b) the precision of fusion positioning is improved significantly by introducing additional parameters into the functional model; and (c) Bayesian estimation of fusion positioning model can still obtain ideal position solution when the number of visible satellites is not enough.
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