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A novel method based on MapReduce to extract auxiliary information for GNSS receivers
Author(s) -
Li Dengao,
Wu Gang,
Zhao Jumin,
Niu Wenhui,
Li Shuai
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3922
Subject(s) - gnss applications , computer science , weighting , pseudorange , a priori and a posteriori , real time computing , variable (mathematics) , data mining , global positioning system , mathematics , telecommunications , medicine , mathematical analysis , philosophy , epistemology , radiology
Summary In order to improve the positioning and navigation performance of Global Navigation Satellite System (GNSS) receivers, a novel method to extract auxiliary information for GNSS receiver is proposed in this paper, which obtains the area GNSS auxiliary information (AGAI) with enough credibility and area attribute. Firstly, a mass of historical GNSS intermediate frequency data is divided into blocks to be acquired and tracked parallel getting massive pseudorange and navigation message (MPD). Then, the massive MPD are weighted and fused parallel by an appropriate weight matrix, which is determined by a priori weighting based on altitude angle and posterior weighting based on M‐residuals variable components estimation. Lastly, the fused information is corresponded to the corresponding location coordinate, completing the parallel extraction of AGAI. The method implemented by parallel programming models MapReduce of Hadoop to guarantee a high efficiency. Experimental results show that the positioning and velocity error of GNSS receivers are reduced by 18.24% and 20.48% using AGAI instead of traditional auxiliary information, and the execution time of the method using MapReduce is reduced by 46.72%, so the proposed method is reliable and effective. Copyright © 2016 John Wiley & Sons, Ltd.