Abstract
With continuous increasing of the data scale of GNSS observations network, the computing pressure of data processing is growing. The undifferenced precise point positioning (PPP) model is one of the main strategies of GNSS network data processing. With the increasing of stations’ scale, the processing time of PPP pattern also increases linearly, the traditional serial processing pattern need to consume a large amount of computing time. As the PPP model is not related, this model has good characteristics of parallel processing between stations. This paper established a distributed parallel processing strategy based on the PPP model, which can not only improve the efficiency of data processing, but also enhance the efficiency of hardware performance. However, due to the high concurrency of data access and processing, the parallel programming is faced with great challenges which can cause immeasurable results. In this paper, by analyzing the flow characteristics of the PPP method, a parallel GNSS data process model at multi-core and multi node level was set up, and a lightweight parallel programming model was adopted to realize the parallel model. Through a large number of data tests and experiments, high efficiency of parallel processing of GNSS data based on the PPP model was achieved. The experiment shows that, under the environment of four multi-core nodes, the parallel processing is at least six times faster than the traditional serial processing.
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References
Ge M, Gendt G, Dick G, Zhang FP, Rothacher M (2006) A new data processing strategy for huge GNSS global networks. J Geodesy 82:199–203
Chen H, Jiang W, Ge M et al (2014) An enhanced strategy for GNSS data processing of massive networks. J Geodesy 88:857–867
Chen J, Zhang Y, Xie Y et al (2014) Rapid data processing of huge networks and multi-GNSS constellation. Geomat Inf Sci Wuhan Univ 39(3):253–257 (in Chinese)
Cai H (2010) Application research of method of large GNSS network realtime data rapid solution. PhD thesis, University of Wuhan, Wuhan, China. (in Chinese)
Chen Z (2014) Research on the key techniques of distributed processing on large scale GNSS observation data. PhD thesis, PLA Information Engineering University, Zhengzhou, China (in Chinese)
GPS Dancer (2012) IAG dancer: global solutions for all receivers in the world. http://www.gpsdancer.org/documents/igs2012.pdf. Accessed 20 Jan 2014
Boomkamp H (2010) Global GPS reference frame solutions of unlimited size. Adv Space Res 46(2):136–143
Cui Y, Lu Z, Chen Z et al (2013) Research of parallel data processing for GNSS network adjustment under multi-core environment. Acta Geodaetica Cartogr Sin 42(5):661–667 (in Chinese)
Kai Y (2013) Research on high performance GNSS normal equation processing method based on OpenMP. J Geodesy Geodyn 33(1):141–144 (in Chinese)
Hillar GC (2012) Professional parallel programming with C#: master parallel extensions with .NET4. TSinghua Univeristy Press, Beijing (in Chinese)
Acknowledgments
This paper is supported by the National Natural Science Foundation of China, No. 41274015; the National 863 Program of China, No. 2013AA122501; State Key Laboratory of Geo-information Engineering, NO. SKLGIE2014-M-1-5 and NO. SKLGIE2014-M-1-6.
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Cui, Y., Lu, Z., Lu, H., Li, J., Wang, Y., Huang, L. (2015). A Parallel Processing Strategy of Large GNSS Data Based on Precise Point Positioning Model. In: Sun, J., Liu, J., Fan, S., Lu, X. (eds) China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume III. Lecture Notes in Electrical Engineering, vol 342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46632-2_12
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DOI: https://doi.org/10.1007/978-3-662-46632-2_12
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