Abstract
In recent years, the Wi-Fi-based indoor positioning technology has become a research hotspot. This technology mainly locates the indoor Wi-Fi based on the received signal strength indicator (RSSI) signals. The most popular Wi-Fi positioning algorithm is the k-nearest neighbors (KNN) algorithm. Due to the excessive amount of RSSI data, clustering algorithms are generally adopted to classify the data before KNN positioning. However, the traditional clustering algorithms cannot maintain data integrity after the classification. To solve the problem, this paper puts forward an improved public c-means (IPC) clustering algorithm with high accuracy in indoor environment, and uses the algorithm to optimize the fingerprint database. After being trained in the database, all fingerprint points were divided into several classes. Then, the range of each class was determined by comparing the cluster centers. To optimize the clustering effect, the points in the border area between two classes were allocated to these classes simultaneously, pushing up the positioning accuracy in this area. The experimental results show that the IPC clustering algorithm achieved better accuracy with lighter computing load than FCM clustering and k-means clustering, and could be coupled with KNN or FS-KNN to achieve good positioning effect.
Highlights
Recent years has seen a growing demand for location services, especially indoor positioning
It can be seen from the results that the required number of fingerprint points was reduced after using the improved public c-means (IPC) clustering algorithm before the feature scaling (FS)-k-nearest neighbors (KNN)
The results show that the IPC clustering algorithm coupling FS-KNN was still superior to the other algorithms in the new environment
Summary
This work was supported in part by 2017 Annual Outstanding Young Teacher Training Program Project of North China University of Technology under Grant XN019009, in part by Scientific Research Project of Beijing Educational Committee under Grant KM201710009004, in part by 2018 Science and Technology Activities Project for College Students of North China University of Technology under Grant 110051360007, in part by Research Project on Teaching Reform and Curriculum Construction of North China University of Technology under Grant 18XN009-011, in part by 2019 Beijing University Student Scientific Research and Entrepreneurship Action Plan Project under Grant 218051360019XN004, in part by 2019 Education and Teaching Reform General Project of North China University of Technology under Grant 108051360019XN141/021, and in part by 2019 Fundamental Research Funds for Beijing Universities under Grant 110052971921/004.
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