Parallel computation of regional CORS network corrections based on ionospheric-free PPP

Li, Linyang; Zhang, Lu; Chen, Zhengsheng; Cui, Yang; Kuang, Yingcai; Wang, Fangchao

doi:10.1007/s10291-019-0864-9

Cited by 10 publications

(6 citation statements)

References 30 publications

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“…(1) Basic layer: Based on server, storage, network and virtualization system. By integrating infrastructure resources and improving the utilization rate of resources, it provides an extensible, safe and stable basic support for the educational big data analysis system [30]. In this paper, the Hadoop platform and its surrounding opensource components are used to build the basic layer service and store the educational data set.…”

Section: Platform Software Architecture Designmentioning

confidence: 99%

Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

Sharma

2021

IJCAI

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Big data technology plays an important role in optimizing the education intelligence by enhancing the learning experience through novel assessment strategies and predictive teaching. This work aims at improving the learning efficiency of users and increase the effective learning quality of the smart classroom concept using the big data technology. A regional network education information collection platform based on big data technology has been developed, which can collect student learning data for subsequent analysis and processing. The software architecture of this platform is mainly divided into basic layer, platform layer and access layer. The physical structure mainly includes web servers and Hadoop clusters. The big data acquisition platform of this education has excellent acquisition performance, and the data acquisition of different data item field length is larger than the expected index. The outcomes obtained reveals that the data item field is less than 20 and the acquisition amount is 145% of the expected result. When the data item field is between 20 and 40, the acquisition amount is 137% of the expected result. The collection is 116% of the expected result, when the data item field is between 40 and 50. When the data item field is between 50 and 60, the collection is 103% of the expected result. The comparison of the regular teaching method with the smart classroom based concept revealed that the big data platform is 87% better than the normal regular teaching methods in terms of study material as well as meaningful teacher-student interaction. The research on education big data can provide better education services for educational activities, drive the reform of teaching mode and optimize the teaching methods of education enabling the smart classroom concept.Povzetek: Predstavljena je regionalna platforma za upravljanje pametne učilnice na osnovi tehnologije velikih podatkov.

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Section: Platform Software Architecture Designmentioning

confidence: 99%

Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

Sharma

2021

IJCAI

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“…The LSC method requires prior knowledge of regional ionospheric characteristics, while Kriging interpolation may involve a relatively large computational burden. These methods are used in PPP-RTK ionospheric delay interpolations (Zha et al 2021;Zhang et al 2022b) and recent studies improved these models by using other information such as rate of total electron content index (ROTI) and the elevation of satellites (Li et al 2023). In the case of small-scale networks, the corresponding performances are similar.…”

Section: Introductionmentioning

confidence: 99%

Regional ionospheric correction generation for GNSS PPP-RTK: theoretical analyses and a new interpolation method

Zhang,

Wang,

Khodabandeh

2024

GPS Solut

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The regional ionospheric corrections have become one of the critical parts of Precise Point Positioning (PPP)-based Real-Time-Kinematic (RTK) services to achieve fast positioning convergence. Several methods for regional ionospheric corrections supporting PPP-RTK have been developed and implemented over recent years, but little attention is given to the theoretical foundation of existing ionospheric correction methods and their performance comparison to find an optimal method in some sense. The optimality criterion of such methods should not only be based on the precision of the ionospheric correction itself, but also on its broadcasting strategies, and implementation aspects. This contribution studies ionospheric correction generation methods within the best linear unbiased predictor (BLUP) framework. Comparing the accuracy performances of the methods, we demonstrate that the Kriging method with trend, as a special case of BLUP, is the most appropriate method for large-scale networks (above 500 km). A strategy for the evaluation of the uncertainty of the grid-interpolated ionospheric corrections is also developed. In contrast to other empirical methods, this new method is rigorous in the sense that it avoids the underestimation of the uncertainty of predicted ionospheric corrections, especially when reference stations are close to a grid point.

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“…For instance, one procedure, e.g., preprocessing, can be further separated into several independent subtasks, which are operated in parallel by all processors over a distributed communication network. Based on this distributed environment, some parallel processing strategies have been developed to speed up epoch-wise PPP or baseline solutions (Li et al 2019;Cui et al 2021).…”

Section: Introductionmentioning

confidence: 99%

A new parallel algorithm for improving the computational efficiency of multi-GNSS precise orbit determination

Chen

Hugentobler

et al. 2022

GPS Solut

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The computational efficiency is critical with the increasing number of GNSS satellites and ground stations since many unknown parameters must be estimated. Although only active parameters are kept in the normal equation in sequential least square estimation, the computational cost for parameter elimination is still a heavy burden. Therefore, it is necessary to optimize the procedure of parameter elimination to enhance the computational efficiency of GNSS network solutions. An efficient parallel algorithm is developed for accelerating parameter estimation based on modern multi-core processors. In the parallel algorithm, a multi-thread guided scheduling scheme, and cache memory traffic optimizations are implemented in parallelized sub-blocks for normal-equation-level operations. Compared with the traditional serial scheme, the computational time of parameter estimations can be reduced by a factor of three due to the new parallel algorithm using a six-core processor. Our results also confirm that the architecture of computers entirely limits the performance of the parallel algorithm. All the parallel optimizations are also investigated in detail according to the characteristics of CPU architecture. This gives a good reference to architecture-oriented parallel programming in the future development of GNSS software. The performance of the multi-thread parallel algorithm is expected to improve further with the upgrade of new multi-core coprocessors.s

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Parallel computation of regional CORS network corrections based on ionospheric-free PPP

Cited by 10 publications

References 30 publications

Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

Regional ionospheric correction generation for GNSS PPP-RTK: theoretical analyses and a new interpolation method

A new parallel algorithm for improving the computational efficiency of multi-GNSS precise orbit determination

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