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

Lili Shen1, Jiming Guo2, Lei Wang3

  • 1School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China. llshen@sgg.whu.edu.cn.

Sensors (Basel, Switzerland)
|June 9, 2018
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Network real-time kinematic (RTK) systems face scalability challenges with increasing users. A new self-organizing spatial clustering (SOSC) approach efficiently manages users, ensuring reliable positioning for AI and smart devices.

Keywords:
network RTKprecise positioningself-organizing spatial clustering (SOSC)spatial clustering

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Area of Science:

  • Geomatics Engineering
  • Geospatial Technology
  • Satellite Navigation Systems

Background:

  • Network real-time kinematic (RTK) offers centimeter-level positioning crucial for geo-spatial infrastructure.
  • Growing demand from AI and smart devices (autonomous cars, UAVs) necessitates scalable RTK systems.
  • Current RTK systems struggle to support a large number of concurrent users.

Purpose of the Study:

  • To develop an efficient approach for supporting large-scale network RTK systems.
  • To reduce the computational load on network RTK server infrastructure.
  • To investigate the impact of clustering on user-side positioning precision and reliability.

Main Methods:

  • Proposed a self-organizing spatial clustering (SOSC) algorithm to group online users.
  • Compared SOSC with a grid-based clustering algorithm for computational load reduction.
  • Analyzed the effects of clustering on user positioning using real global navigation satellite system (GNSS) data.

Main Results:

  • Both SOSC and grid algorithms effectively reduce computational load.
  • SOSC provides a more adaptive and elastic clustering solution across diverse datasets.
  • A cluster radius threshold of 10 km for SOSC maintains user positioning precision and reliability.

Conclusions:

  • The SOSC algorithm offers an efficient and scalable solution for network RTK systems.
  • Adaptive clustering is superior to predefined grid approaches for varying datasets.
  • Network RTK systems can be scaled to support numerous users without compromising positioning accuracy.