Indoor Positioning by a Novel Location Fingerprinting Algorithm of SVM-based Cluster Assignment

Clustering approaches have been used in location fingerprinting systems to improve positioning accuracy and reduce computational overhead. However, traditional methods can not use the data collected directly, and this problem is the main issue in combining the supervised and unsupervised learning. This study proposes a novel clustering algorithm based on SVM called SVM-C and it solves the problem about the clustering algorithm applying in the classification. The SVM-C approach focuses on the distance between the classes. It utilizes the margin between two canonical hyperplanes to cluster them instead of using the Euclidean distance between two average points.
This thesis applies the proposed algorithms to realistic wireless local area networks.
Experimental results demonstrate that the SVM-C outperforms the K-means and SVC reducing the mean localization error by 19.61\% and 15.31\% respectively under the traditional AP-selection schemes. The experiments based on different fingerprinting approaches and different AP-selection schemes also confirm the advantages of the proposed algorithms.