A Fast Parallel Processing Strategy of Double Difference Model for GNSS Huge Networks

doi:10.11947/j.AGCS.2017.20160585

Abstract

Abstract: In view of the existing problem of poor timeliness for larger GNSS network data processed by the double-difference (DD) model, a new approach for the independent DD observations selection and integrated processing of huge GNSS networks is presented by using the parallel algorithm design and parallel programming technology in this paper. The new approach is implemented by using the parallel computing strategy of multi-core parallel level and network parallel level. Validation experiment with GPS observation data collected from about 375 IGS stations was carried out, which demonstrated that the new approach has an average about 530,000 more independent DD ambiguities than the traditional method. The average ENU(East North Vertical) positioning precision of the new approach was improved by 14.0%、12.9% and 29.2%. In the whole network processing of 375 stations, the efficiency of parallel solution with four quad-core nodes is increased about 14 times than the traditional serial computing scheme, significantly improves the efficiency of the entire network processing with DD model.

Key words: large GNSS network, double difference model, independent baseline, multi-core multi-node, parallel computing

CLC Number:

P228

CUI Yang, LÜ Zhiping, LI Linyang, CHEN Zhengsheng, SUN Dashuang, KUANG Yingcai. A Fast Parallel Processing Strategy of Double Difference Model for GNSS Huge Networks[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(7): 848-856.

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