A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems
作者： Lili Shen , Jiming Guo , Lei Wang
DOI: 10.3390/S18061855
关键词: Real-time computing 、 Cluster analysis 、 Reliability (computer networking) 、 Grid 、 Key (cryptography) 、 Determining the number of clusters in a data set 、 Computer science
摘要: 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, RTK systems will face issues the support of large numbers concurrent users. In past, high-precision services were oriented towards professionals only supported few Currently, precise provides spatial foundation for artificial intelligence (AI), countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require services. Therefore, development approaches to large-scale is urgent. this study, we proposed self-organizing clustering (SOSC) approach which automatically clusters online users reduce computational load on system server side. experimental results indicate that both SOSC algorithm grid efficiently, while gives more elastic adaptive solution with different datasets. determines cluster number mean distance center (MDTCC) according data set, are all predefined. side-effects algorithms user side analyzed global navigation satellite (GNSS) sets. 10 km be safely used as radius threshold without significantly reducing precision reliability
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