Channel State Information localization based on improved DBSCAN clustering algorithm
CSTR:
Author:
  • Liu Yu

    Liu Yu

    1. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China; 2. Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China; 3. Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • Yu Xuexiang

    Yu Xuexiang

    1. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China; 2. Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China; 3. Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • Xie Shicheng

    Xie Shicheng

    1. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China; 2. Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China; 3. Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • Liu Shuang

    Liu Shuang

    1. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China; 2. Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China; 3. Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
  • Zhu Ping

    Zhu Ping

    1. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China; 2. Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China; 3. Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China
    Find this author on All Journals
    Find this author on BaiDu
    Search for this author on this site
Affiliation:

1. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China; 2. Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China; 3. Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China

Clc Number:

TN92

  • Article
  • | |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • | |
  • Comments
    Abstract:

    In recent years, wireless signals using WiFi Channel State Information have played important roles in scenarios such as indoor positioning, fall detection, and identification. However, the impact of multipath effects in complex environments makes the accuracy of fingerprint positioning to be improved. To solve this problem, this paper proposes an improved Density-Based Spatial Clustering of Applications with Noise during the process of noise reduction combined with Enhanced weighted K-nearest neighbor algorithm in the online stage. First, the Hampel algorithm is used to remove outliers of the amplitude information; then, the improved DBSCAN algorithm automatically adjusts the parameter to cluster data; finally, the Enhanced weighted K-nearest neighbor algorithm is used to match the real-time positioning points from the fingerprint database. The experimental results show that the average positioning accuracy of the DBSCAN algorithm reaches 1.579m in a positioning area of about 5×10m2, and the percentage of error within 2m is increased by 42.9% compared to the traditional fingerprint method.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Online: May 14,2024
Article QR Code