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Sensors 2018, 18(6), 1855; https://doi.org/10.3390/s18061855

A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems

1
,
1,* and 2,*
1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Received: 11 May 2018 / Revised: 30 May 2018 / Accepted: 5 June 2018 / Published: 6 June 2018
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [3398 KB, uploaded 6 June 2018]   |  

Abstract

The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side. View Full-Text
Keywords: network RTK; spatial clustering; self-organizing spatial clustering (SOSC); precise positioning network RTK; spatial clustering; self-organizing spatial clustering (SOSC); precise positioning
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Shen, L.; Guo, J.; Wang, L. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems. Sensors 2018, 18, 1855.

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