Acta Geodaetica et Cartographica Sinica ›› 2016, Vol. 45 ›› Issue (S2): 31-38.doi: 10.11947/j.AGCS.2016.F023

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A Kalman Filter Method for BDS/GPS Short-term ISB Modelling and Prediction

ZHANG Hui, HAO Jinming, LIU Weiping, YU Heli, TIAN Yingguo   

  1. Institute of Navigation and Aerospace Engineering, Information Engineering University, Zhengzhou 450001, China
  • Received:2016-11-25 Revised:2016-12-20 Online:2017-05-20 Published:2017-05-20
  • Supported by:

    multi-constellation PPP;BDS;ISB modelling;Kalman filter;fitting accuracy;prediction accuracy

Abstract:

Study of inter-system biases(ISB) is essential for the data processing of multi-constellation precise point positioning(PPP). When establishing BDS/GPS short-term ISB models, as equal-weight least square(LS) fails in taking the different weights of fitting ISB data into consideration, a method based on Kalman filter is proposed for the parameter estimation of ISB modelling, and the variance of fitting ISB data in Kalman filter is adjusted according to the distance between the data time and the forecast time. ISB models are established with ISB data over 7 days and the accuracy and applicability of ISB prediction for the 8th day is verified by static PPP experiments. The analysis result shows that the accuracy of ISB prediction generated by Kalman filter model is 29.7%, 11.5%, 43.5% and 32.0% higher than those generated by LS model at 4 stations, respectively. With the priori constraints of ISB prediction generated by Kalman filter model, the averaged RMS values of static PPP solutions are promoted by 2.7% and 0.9% higher in E and U components than those with priori constraints generated by LS model, and are promoted by 10.6%, 26.3% and 3.4% higher in E, N, U components than those without priori constraints, respectively.

Key words: multi-constellation PPP, BDS, ISB modelling, Kalman filter, fitting accuracy, prediction accuracy

CLC Number: